Gongcheng Kexue Yu Jishu/Advanced Engineering Science

Modern transport systems face many challenges, and Intelligent Transport Systems (ITS) have emerged as a promising solution that aims to increase sustainability, efficiency, and safety. Among the various ITS technologies, Vehicular Ad Hoc Networks (VANETs) are essential for enabling communication and coordination between vehicles, infrastructure, and other elements of the ransportation ecosystem. With a focus on the key ideas, issues, and developments in this area, this urvey paper offers a thorough overview of the integration of VANETs into ITS. It looks at the nderlying ideas behind VANETs, such as network architectures, communication protocols, and ecurity mechanisms. The study looks at the various ways that VANETs are used in ITS, including raffic management, collision avoidance, intelligent routing, and emergency services, by applying arious emerging technologies.This survey provides an analysis of the current security landscape in ntelligent Transportation Systems (ITS) and Vehicular Ad hoc Networks (VANET) with a focus on ntegrating different routing protocols and algorithms. It examines the potential security threats and hallenges faced by ITS and VANET, including attacks on vehicle-to-vehicle and vehicle-to-nfrastructure communications, privacy breaches, and data integrity issues. The survey investigates the role of routing protocols and algorithms in addressing these security concerns and improving verall security in ITS and VANET. It evaluates the strengths and limitations of various protocols, onsidering factors like scalability, adaptability, and resilience to security threats. The survey explores the influence of external factors such as environmental conditions, traffic patterns, and user behavior on the performance of VANET-based ITS. Furthermore, emerging technologies such as edge computing, artificial intelligence, and blockchain are examined in the context of addressing these challenges in enhancing security in ITS and VANET. The findings and recommendations of this survey provide guidance for researchers, practitioners, and policymakers in developing secure ITS and VANET environments.

In this paper, we discuss special operators and level sets of intuitionistic fuzzy d-ideals of d-subalgebra. Here we also investigate about some of its properties in detail by using the concepts of intuitionistic fuzzy d-ideals.

The notion of Pythagorean Neutrosophic Hypersoft Set relations (〖PNHSS〗_r s) are introduced in this work, along with several related ideas including equivalent (〖PNHSS〗_r s), Partition(p-n)and Composition.

The objective of this research to explore the creation and assessment of solid-state connections between dissimilar materials, specifically Copper and Brass, by incorporating naval brass as an interlayer and employing the drive friction welding process. This pioneering method harnesses heat generated through friction between two surfaces, leading to plastic deformation. Throughout the experimental phase, a thorough examination of diverse welding process parameters was conducted. The results were subsequently subjected to a comprehensive analysis, encompassing tensile testing, Vickers micro-hardness testing, and SEM-EDX (energy dispersive X-ray) analysis. These analyses played a crucial role in identifying the phases that emerged during the welding process

This validation is essential to the overall success and impact of this work since it establishes a suitable degree of confidence in the capability of the developed numerical models to represent the physical behavior of the analyzed structural components. Attaining a realistic representation would enable the use of the proposed numerical descriptions for parametric studies to support the development of a design methodology for RHCSP.A successful validation against experimental results was achieved. The top, bottom and core plates of the sandwich panels were discredited using a fine mesh of geometrically and materially nonlinear 9-noded shell elements, capable of accurately predicting the effects of large displacements and structural instabilities at a global and local level. A suitable representation of the experimental data was achieved due to the modeling of slotted cores by disconnecting

Present study exploring the influence of Dufour effect on a non-linear heat and mass transfer flow past a stretching/shrinking sheet prescribed with variable heat flux in the presence of heat source and constant suction. The fluid viscosity and thermal conductivity are assumed to be inverse linear functions of temperature. A numerical solution for the system of non-linear momentum, energy and concentration equations are worked out by adopting Runge-Kutta shooting method. The consequences of both viscosity and thermal conductivity parameters along with Dufour number and Magnetic parameter on the flow are presented and discussed graphically.

Recent years have seen a surge in interest in biopolymers, a kind of natural biodegradable polymer. Researchers were compelled to engage in the biopolymer business due to rising environmental consciousness and the depletion of fossil fuel supplies. Cellulose, chitosan, pectin, agar-agar, and carrageenan are a few of the biopolymers utilised in the manufacture of rechargeable batteries. Given the hazardous nature of lithium-based materials, researchers are eager to find an alternative energy storage system that is similar to lithium-ion battery technology. In this study, a team has explored magnesium ion conducting polymers made from iota carrageenan and magnesium chloride salt for potential battery applications. To construct the solid polymer electrolyte, methyl cellulose (MC) and magnesium chloride (MgCl2.6H₂O) were combined using the solution solvent and casting methodologies. Structural analyses including X-ray diffraction (XRD) technology appear to its amorphous nature while Fourier transform infrared (FTIR) technique confirms its compatibility with both polymer components. To understand the glass transition temperature, differential scanning calorimetry (DSC) was employed. The electric and dielectric properties of the polymer electrolyte were studied using AC impedance spectroscopy. Evidently, when one-gram is I-carrageenan joins 0.4 weight percent of MgCl2, it produced a maximum ionic conductivity at 6.1 10-4 S/cm with a lowest activation energy value of 0.175 eV per gramme. An evaluation of a biopolymer membrane consisting of 40% CA to 60% MgCl2 revealed that it demonstrated maximum conductivity at room temperature of 4.05*10-4 S/cm. Through linear sweep voltammetry, this material was found to possess an electrochemical stability up to 3.58 Volts, with a total ionic transference number around 0.98. Subsequently, this high conducting polymer electrolyte was employed in constructing a primary magnesium ion battery.

With the colossal development of PC organizations and the immense expansion in the number of utilizations that depend on it, network security is acquiring expanding significance. Additionally, practically all PC frameworks experience the ill effects of safety weaknesses which are both troublesome and monetarily exorbitant to be addressed by the makers. In this way, the job of Intrusion Detection Systems (IDSs), as unique reason gadgets to recognize irregularities and assaults in an organization, is turning out to be more significant. Cyber-attacks detection using intrusion detection system IDS along with WEKA simulation tool. In this paper, we represent the activities and detection of the malicious nodes with information to the system administrator. Various types of cyber-attacks occurring recently now. To safeguard the system from various assaults, an intrusion detection system allows for simple identification of potential threats.

As per IEC Standards, partial discharge (PD) are “localized electrical discharges that only partially bridge the insulation between conductors.” PD is local electrical stress on the insulation it can be internal or on the surface of the insulation. Petroleum-based mineral oil is traditionally used as insulation for power and distribution transformers over the century because of its scientifically proven advantages. But the use of mineral oil has disadvantages to the environment also PD effect significantly deteriorates insulation strength and can cause the transformer to fail. The ester-based Synthetic oil is been used as an alternative to mineral oil in recent decades due to its biodegradable and fire-resistant properties. In this paper, the partial discharge effect of mineral oil and synthetic ester oil is investigated experimentally as per IEC Standards and Phase Resolved Partial Discharge analyses are carried. The phase vs Charge, Charge vs Number of counts, and Phase vs Number of counts patterns are obtained individually for both oils, and results are compared and presented for PD deterioration effects.

Healthcare monitoring system in hospitals and numerous health centers has experienced large growth, and portable healthcare monitoring systems with promising technologies are becoming of great concern. The huge volume of data generated by IoT devices creates a significant challenge in the healthcare system pertaining for handling and managing the data. The conventional techniques managing huge volume of data but accurate healthcare monitoring were not obtained since it learns the numerous features from raw inputs. In order to improve the healthcare monitoring system with big data, a novel technique called a TArgeted feature projection-based Hedge Iterated Gaussian Naive Bayes MIL Boost Classification (TAHIB) technique is introduced for patient e-health monitoring with lesser time. IoT patient data is gathered from the database. Initially in TAHIB technique, feature selection process is carried out using Motyka Indexive targeted projection method. The target projection method selects the relevant features through determining the similarity between features and objective (i.e., patient health monitoring). When similarity value is higher value, the feature is selected as relevant feature. After that, the relevant features are taken for patient e-health monitoring. Then, Hedge Iterated Gaussian Naive Bayes MIL Boost Classification process is carried out to classify the patient as normal or abnormal condition. The boosting classifier is an ensemble of several weak learners and combined to make a strong classifier hence it provides the final accurate classification results as normal or abnormal condition. Experimental evaluation of is carried out using on factors such as classification accuracy, classification time and error rate, space complexity with respect to number of patient data. The quantitatively discussed results indicate that performance of proposed TAHIB method increases data accuracy of disease diagnosis with a minimum time, error and memory consumption than conventional methods.

The fast technological development has affected both the day-to-day operations of companies and the day-to-day activities of people living in this digital environment characterized by Industry 4.0. The lack of scalable and distributed architectures in the process of analyzing malware was the impetus for the present study. The novel Scalable Machine Learning Network (ScaleMalNet) proposed for malware detection. The framework designed to deal with malware in real time and on demand. utilizes a two-stage method of analysis: in the first stage, executable files are classified as malicious or legitimate based on static & dynamic analysis results; in the second stage, malicious executable files are classified into corresponding malware families based on static and dynamic analysis results. It offers the capacity to utilize Big Data approaches to handle a big number of malware samples. This study made use of two different kinds of conditioning information, both of which were put in the discriminator and the generator. One of the encoding mechanisms is the BASE + XOR combination. It is carried out in the generator of the work that has been proposed, and it results in lower overall energy consumption during data transmission. An objective is for comparison of the performance of traditional Machine Learning Approaches (MLA)s and Deep Learning (DL) architectures based on the range of different malware analysis models as a benchmark.

The fragile landscapes of the Himalayan region are highly susceptible to natural hazards, and there is on-going concern about current and potential climate change impacts which may include abnormal floods, droughts and landslides, loss of biodiversity and threats to food security. The Indian subcontinent has been prone to disasters of great scope for generations, and recent events such as the 1999 Orissa cyclone and the 2001 Gujarat earthquake, coupled with the ongoing landslide, floods, droughts, and other hazards that are in fact commonplace. The methodological procedure adopted, integrating different GIS modules for Slope hazard and emerging landslide risk assessment. The data used in the present study are Satellite image LANDSAT 8 OLI (operational land imager), Aster GDEM (30 × 30 m resolution), Google Earth Image, Geological Map (Geological Survey of India) and Topographical Map (Survey of India). Landslide Distribution Map/inventory map was carved out to determine landslide affected area (%) and frequency of landslide for each class of the landslide inducing factors/factors maps evaluating SOI Toposheet, Google Earth Image and intensive field investigation with GPS. The main objective of this study to analysis the trend and susceptibility of landslide and evaluate its impact on local community in the study region.

Stock cost forecast is the foremost altogether utilized within the budgetary segment. Stock advertise is unstable in nature, so it is troublesome to anticipate stock costs. Usually a time arrangement issue. Stock cost expectation could be a troublesome assignment where there are no rules to foresee the cost of the stock within the stock advertise. There are so numerous existing strategies for anticipating stock costs. The prediction strategies are Calculated Relapse Demonstrate, SVM, ARCH model, RNN, CNN, Backpropagation, Naïve Bayes, ARIMA demonstrate, etc. In these models, Long Short-Term Memory (LSTM) is the foremost reasonable calculation for time arrangement issues. The most objective is to estimate the current advertise patterns and seem foresee the stock costs precisely. We utilize LSTM repetitive neural systems to anticipate the stock costs precisely. The comes about appear that expectation exactness is over 93%.

As cloud service become popular and cost effective throughout the world, Virtual Machine (VM) technology has out staged as the key of many cloud services providers. To maximize data center utilization, cloud data center usually utilizes supervisor to manage virtualized resources. The existing system has issues with higher overhead and computational complexity for load balancing mechanism. Hence, the overall cloud performance is reduced considerably. To overcome the above mentioned issues, in this work, Enhanced Simulated Annealing and Weighted Support Vector Machine algorithm based Cost effective-VM migration (ESA+WSVMCVM) approach is proposed. This research contains main modules are such as system model, virtualization, load balancing and VM migration. System model includes no of VMs, cloud user, CPU, memory usage, no of tasks and no of resources. After construction of system model, virtualization is performed which is used for easy migration of VMs. It provides computing infrastructure resources, such as computing power, data storage, networking, all in the form of web services. Then, load balancing is done by using ESA algorithm which is used to equalize the total workloads over cloud. Load balancing is achieved by transferring tasks from over-loaded nodes to under-loaded nodes. By generating best fitness values of ESA, the balanced loads are provided and it used to optimize the resource use, maximize throughput, minimize processing time. Finally, cost effective VM migration is performed using WSVM algorithm. It is focused to understand the pattern of overload and under load using weight values of SVM and also identifies the VM migration that requires minimal energy expenditure without compromising with quality of services. From the experimental result, it concluded that the proposed ESA+WSVMCVM algorithm provides better cloud performance in terms of higher throughput and lower computational complexity, cost complexity, Mean Square Error (MSE) rate and energy consumption rather than the existing methods

A significant amount of data has been produced as Internet of Things (IoT) applications are being used in day-by-day activities. IoT applications frequently need for the involvement of several technologies, such as, cloud computing (CC), edge computing (EC), fog computing (FG), green computing (GC), etc. which have created significant security issues. Because of the inefficiency of present security measures, cyberattacks are also increasing as a result of the usage of these technologies. Many artificial intelligence (AI)-based security solutions, including intrusion detection systems, have been created in recent years (IDS). For the creation of smart analytical tools,it is necessary to have preprocessing, feature selection, data augmentation, machine learning (ML) techniques.This study aims to improve intrusion detection accuracy by employing a supervised classification framework and a mode rank-based mayfly optimization algorithm (MRMFOA). It produces the mayfly optimization by redefining the related components to operate on discrete spaces. To be more precise, it introduces a random exploration function that adds additional variety and redefines the concept of distance (between individuals in mayfly optimization). In addition to the random move defined in the MFO algorithm, the latter includes two extra random approaches based on the crossover and mutation operators. On the UNSW-15 datasets, experiments were conducted to assess how well the recommended approach worked. The MRMFOA produced better results by showing 2% of improvement in terms of accuracy.

Findings demonstrated that such approaches may prevent an adversary from acquiring hidden IDs or asymmetric encryption via infiltrative, learning, side channel, and computer assaults. Radio-Frequency Systems (RFS) identification architectures provide safe data transmission within the web. In contrast extreme, Unaccessible Physical Function (UPF) might exploit manufacturing process irregularities to automatically identify microchips, making a UPF-based system incredibly durable and secure at a reasonable cost. They offer RFS-UPF, a Deep Neural Network (DNN) based methodology that level processes wireless node authentication utilizing the effects of intrinsic variability on RFS features of the remote controls (Tx), identified through in-situ supervised learning at the wireless sensors (Tx). A proposed method makes use of the current asymmetric RFS communication infrastructure by accumulating any special transistors to semantic segmentation or UPF invention. Similar to a human listener's brain works, Rx assumes a full burden of device identification at the gateway. According to experimental results, which include process performance at a predefined 65 nm threshold voltage and characteristics like LO misalignment and I-Q differences found using a predictive model with 52 hidden nodes, a framework could distinguish up to 4800 transmitters with the durability of 99.9% under different channel quality, without the necessity of conventional preambles.

Investors are able to combine their funds into a single investment vehicle by making use of mutual funds. This vehicle may then be used to acquire securities such as government bonds, corporate bonds, short-term money market instruments, other securities, or a combination of the assets listed above. Since the beginning of their existence, mutual funds have maintained their position as the investment choice that first-time investors pick the most often out of all the available options. The purpose of this research is to assess the efficiency of mutual funds and to investigate the roles that benefit management companies play in both the public and private spheres. The primary objective of this inquiry is to analyse the economic performance of a variety of different mutual fund schemes by using factual criteria such as beta, standard deviation, Treynor's measure, and the Sharpe ratio. The findings of the study will prove to be beneficial in assisting investors in making judgements on their subsequent investments. The mutual funds that are sponsored by public sector banks beat those that are sponsored by UTI banks and the private sector. This is due to the public sector banks' higher returns, all-positive and diversified schemes, and efficient portfolio management. This is the case due to the fact that both mutual funds sponsored by the private sector and UTI banks derive their investments from the banking industry. Since the beginning of their existence, mutual funds have been widely regarded as the best investment opportunity for individual investors. It's possible that the high investor return and little risk are the primary motivating factors for doing this. Small investors are given the option to participate in the market for securities, which enables them to expand their enterprises while also reducing the risk involved. This article makes it quite clear that one must do extensive study before investing in any mutual funds. Because investing in mutual funds is becoming an increasingly common activity, it is essential to have a solid understanding of the potential downsides as well as the potential for significant upsides. This article intends to raise awareness about the mutual fund industry in India and makes an attempt to do so throughout.

The phenomenon of internet shopping is growing nowadays. More and more people are choosing E- shopping websites for purchasing their items. The behavior of customers are changing rapidly and E- commerce companies are giving importance to the factors that influence customers for doing online shopping. Thisstudy helps to understand some of the factors that influence customers for doing online shopping.

Internet of Things (IoT) has played a major role in the rapid expansion of various linked devices. A wide range of alternative uses may be found for these devices, not only in smart homes. The linked IoT makes use of sensors and resource-constrained devices. It is challenging to secure the network since the devices are so tiny and have so many energy limits. Through a device, any virus or incursion may enter the network, compromising the whole system. When compared to other types of computers, this increases the likelihood of an attack. In order to bring intelligence and analytical thinking closer to its origins, new and creative learning models must be used.

Financial institutions must use digital innovations to be competitive in today's market and meet evolving client expectations. These innovations are no longer optional in the modern banking landscape. Artificial intelligence (AI) has been the primary force behind the revolutionary disruption of banking channels (such as automated teller machines, internet banking, and mobile banking), services (such as check imaging, speech recognition, and chatbots), and solutions in the modern banking age (e.g., AI investment advisors and AI credit selectors). The usage of AI in banking is widespread, with applications in the front office (voice assistants and biometrics), middle office (complicated legal and regulatory workflows) and back office (smart credit underwriting with machine learning

Magneto-hydrodynamic (MHD) flow dilemmas along nonlinear stretching sheets in corporation with transverse magnetic fields are analyzed. The governing equations are transformed into nonlinear ordinary differential equations and solved by adopting the homotopy perturbation method. In the present analysis, the homotopy perturbation method shows an excellent agreement with the method implemented by the present authors.

Medical imaging and telemedicine are becoming increasingly popular these days. Because of the increased interest in keeping and distributing medical pictures, memory space and transmission bandwidth are in short supply. Compression was developed in response to these problems. The major purpose of lossless image compression is to enhance compression efficiency, reduce bit rate, and boost accuracy for medical image storage and transmission while maintaining acceptable image quality for diagnosis. Background suppression for lossless picture compression utilising fuzzy clustering is implemented in this article. . In this initially input images are given as inputs. Pre-processing image is applied to the input images. Fuzzy clustering process is applied to the pre-processed image. For this preprocessed image thresholds are computed. Now framework is constructed based on the computed thresholds. If there is noise generated in the framework then noise is reduced by averaging the frame. After generating the frame casting technique is applied to the boundary’s. In this image pixels are obtained from boundary’s. Mask is obtained for the image pixels by applying background image suppression. For this suppressed image loss less image compression technique is applied. At last compressed image is encoded using encoded image. From results it can observe that image quality, compression efficiency, accuracy will be improved and error bit rate & storage level is reduced.

The need for lower power Digital Signal Processing (DSP) devices has grown significantly as a result of the meteoric rise in mobile computing as well as portable multimedia applications. The most crucial factors in the construction of DSP systems and higher performance systems nowadays are low power consumption and small footprint. Finite Impulse Response (FIR) filters are among the most frequently utilized DSP techniques. In both signal processing and transmission, FIR filters are crucial. The main requirements for FIR filters are area minimization and speed. Operations including addition, multiplication, and shifting are used in FIR filters. The main building components of the filter are multipliers and adders, with multipliers taking up the most silicon space and using the most power. Digital circuits can use a variety of adders and multipliers, but constructing effective filters needs an effective adder and multiplier architecture. Thus, in this analysis, a PPA adder and multiplier are used to develop an effective VLSI architecture of a FIR filter. Reduced power units can be applied upon this multiplier and the design of full adders can be achieved; the outputs are then assessed for improved performance. Improved performance can be obtained with this FIR filter architecture in regards to latency, area, and overall power.

In general, basic operations like addition, subtraction, and division may be performed using various types of binary adders in digitally based processors and control systems. The device's adder performance is solely used to measure a processor's or system's high speed and precision. Addition is one of the most vital and initial operations among all arithmetic operations and is utilized in many of the mathematical equations. In digital world, the addition operation can be performed by several adders. These adders produce carries with preferred power and delay. An adder is the basic functional unit to perform the modular arithmetic in various cryptography and pseudorandom bit generator (PRBG) algorithms. Here an efficient adder circuit is implemented that revolves around reducing the cost to propagate the carry among consecutive bit positions. Hence in this work, design a high speed and area efficient KS (Kogge-Stone) and BK (Brent Kung) tree adder architecture. In this approach, the two Parallel Prefix Adders like Kogge-Stone and Brent Kung adders are used. This hybrid adder can significantly perform the addition operation with high speed and it requires less area.

As the complexity and size of VLSI (Very Large-Scale Integration) designs tend to increase, pre-silicon fault detection technologies have become crucial for maintaining reliability in IC design. For the modern power system to be protected from transmission line faults, real-time monitoring and quick control are necessary. For power systems to operate with reliability, it is essential to identify and classify fault conditions. The engineers manual feature extraction having prior knowledge, which has been suggested by various scientists for fault detection and classification, is a key component of traditional fault diagnosis approaches. Any analog circuit's reliability depends heavily on the capacity to detect problems. By preventing potential severe damage from a defect, early fault detection can considerably aid in system maintenance. In the realm of fault detection and diagnosis, automatically and reliably recognizing the incipient micro-fault in power system, particularly for fault orientations as well as severity degree, remains a serious difficulty. Consequently, in this work, fault detection on the VLSI circuits employing R-FF design and implementation (Razor Flipflop). The Razor Flip-flop is a technology for circuit-level timing speculation that relies on dynamic failure and fault detection and correction in digital systems. The razor flip flop will increase the energy efficiency of presented system. This approach can effectively detect the faults in VLSI circuits.

The rapid growth in the population is the main reason for today’s water pollution in the sources of water, which would be creating the stress in the water throughout the world. Treatment of waste waters of the industries which would be having the contaminated metals in them will be involving the techniques like reducing the toxic in water for meeting the standards set by the authorities or government. Various treatments for the removal of heavy metals from industrial wastewater have been evolved. The adsorption commonly used method for reducing the pollutant in the water totally, the fundamental process of physicochemical treatments in the wastewaters that is economically meets higher effluent standard. The activated charcoal is one of the oldest also a widely used for the adsorbent of water for treatment by removing both the organic and the inorganic pollutants. This method is activation, with the nature of precursors which greatly influences the surfaces of the functional group with pore structured activated charcoal. Despite having many techniques for the purification of water the adsorption is the fastest, less expensive and is also known as the universal technique. In this paper we have mainly concentrated on the various recently developed methods for wastewater treatment through activated charcoal.

The interest in information security strategies is expanding due the quick expansion in the use of media content and transmission over the web employments. Accordingly, there is a need of special embedding procedure for steganography. The creator presents an original reversible data hiding (RDH) algorithm for colour pictures that further develops the inserting execution by applying a channel transformation function and a versatile expectation blunder extension Prediction Error Expansion structure. The proposed calculation will bring the first colour picture with no information misfortune from the inserted picture (which is unique in addition to target picture). This calculation uses the zero or the base place of the histogram and marginally changes the pixel esteems for inserting the information. It can insert more information when contrasted with the majority of the current data hiding calculations. A hypothetical confirmation and various examinations show that the Embedding Capacity of the proposed model is consistently more noteworthy than the other reversible data hiding techniques. The calculation has been applied to a wide scope of various colour pictures effectively. Some exploratory outcomes are introduced to show the legitimacy of the calculation.

The purpose of this study is to analyze the effectiveness of open-ended, growth-oriented equity schemes in the transition economy throughout the five-year period beginning in April 2018 and ending in March 2022. In order to compute the returns from the various fund schemes, the daily closing NAV was used from each of those fund schemes. For the market portfolio, the BSE-Sensex has been used. The past performance of the chosen schemes was analyzed using the measure developed by Sharpe, Treynor, and Jensen. The findings of this analysis will be helpful to investors in making more informed choices about their financial investments. According to the findings of the research, only 14 of the 30 different mutual fund schemes had achieved a return that was higher than the benchmark. The findings also revealed that some of the schemes had not met the expectations set for them; these schemes were struggling with the diversification issue. According to the findings of the research, the Sharpe ratio was positive for all schemes, indicating that funds were giving returns that were higher than the rate at which there was no risk. The results of the Jensen measure showed that 19 out of 30 different schemes had positive alpha, which suggested that the schemes had greater performance. The purpose of carrying out this study is to determine which kind of investing strategy is most often used in Indian mutual funds: conventional. In addition to that, the purpose of the research is to investigate the function of investing mutual funds in Indian. This study also finds out the factors that have an effect on investments in mutual funds and measures the performance of mutual funds using models that are used all over the world to evaluate the investment tendency in the area of mutual funds through portfolio (Risk/Return) Sharpe Measure and Treynor Measure, to make the best point for investment through graphical representation for both conventional and Indian private banks investments, which makes it much easier to make a better decision for investment either by selling or buying.

Virtual entertainment and different stages on Internet are ordinarily used to convey and produce data. Generally speaking, this data isn't approved, which makes it hard to utilize and examine. Despite the fact that there exist concentrates on zeroed in on data approval, a large portion of them are restricted to explicit situations. In this way, a more broad and adaptable engineering is required, that can be adjusted to client/designer necessities and be free of the virtual entertainment stage. We propose a structure to naturally and continuously perform validity examination of posts via virtual entertainment, in view of three degrees of believability: Text, User, and Social. The overall design of our system is made out of a front-end, a light client proposed as a web module for any program; a back-end that executes the rationale of the believability model; and an outsider administrations module. We foster a first rendition of the proposed framework, called T-CREo (Twitter CREdibilityanalysis structure) and assess its presentation and versatility.

Providing the security and robustness for the model is the crucial as the attackers are more for the machine learning and deep learning model due to increase in the usage of the Artificial intelligence in many applications. Hence this vulnerability to the model has drawn an attention of the researchers. Inducing the changes in the model causes the changes in the weights which in-turn causes the misbehaviours in the predication. The commonly used steps for the attacking are changes in maps, surface decision changes. This paper proposes the methodology called as Efficient Gradient Integrated Attack based on FGSM (EGIA). It also describes about the fundamental concepts in adversarial attack and its applications. The model is implemented in the python scripting languages and achieved the considerable attack on the existence model.

In human body certain organ and tissue cells divides each other and increases. Some of the cells get increased due to duplication and some are prone to death of cells to maintain the equilibrium state concerning the organs integrity. DNA or genetic defects balances the creation and death of the cells. The propagation and replication of the cells take place which results in new emerging changes. This can result in uncontrolled growth of cells which turns in to tumor. These cancer cells grab all the nutrients from healthy cells and start encroach the nearby tissue cells. Some of the cancer cells remain obscure doesn’t get replicated but some cancer cells also enters into other parts of the body via glands or the body parts. The proposed work is to present an approach for classification of the breast cancer using mammograms. The preprocessing of mammograms is a very important step to segment the region of interest and enhancing the image quality in order to detect image calcifications. The wavelets based artifacts removal is performed. Pectoral muscles are detected using k-means clustering and are removed. Segmentation is performed using region growing algorithm and extract shape vectors. Classification of cancer is performed using artificial neural networks or support vector machine. After applying SVM classifying technique to the images of database, will come to know the tumor is either Benign or Malignant.

The electronics world has a lot of dependency on processing devices in the present and future developments. Even non-electronic industries have much data to process and are indirectly dependent on processors. The larger the number of processors incorporated into the architecture will lower the data handling and processing time; thus, efficiency improves. Hence multi-core processors have become a regular part of the design of processing elements in the electronic industry. The large number of processors incorporated into the system architecture results in difficulty in communicating among them without a deadlock or live-lock. NoC is a promising solution for communicating among the on-chip processors, provided it is fast enough and consumes less energy. Further, to stand with the increasing data acquisition and processing in the new/developing operating systems and software, the latency among the mulit-core processors should be optimal. This paper aims to address energy efficiency and latency reduction methods/techniques for Multi-core architectures.

Removal of noise is an important and critical research work to get noise less image in Image processing field. More and More filters analysis and design is must to achieve noisy less image information and improved version of image information in image processing. There are different types of filters are available to remove the noise presence in the various types of noisy and corrupted images. Various types of denoising algorithms applied on the noisy images to filter the images. By applying different types filters on the images to remove the noise presence the images results non efficiency in pixel of the images. There are different types of noise removal filters are identified to remove the noise and non efficiency of the pixels in images where filters applying on the images.

The elevated uterine cancer mortality age is a result of women's ignorance of the value of early detection. Cervical cancer is a serious malignancy that poses a threat to the health of women everywhere, and it has rather subtle early-warning symptoms. It causes harm to the cervix's deep tissues and can eventually spread to other parts of the body like the lungs, liver, and vagina, which can make the situation more challenging. As a result, this study offers a clever method for cervical cancer prediction using DL (Deep Learning) algorithms. We evaluated the performance of our DL models using some different metrics, including recall, precision, and accuracy. The DBN algorithm yields the highest classification score of 92-% in terms of predicting cervical cancer. With MLP, however, accuracy has been determined to be 89%. To evaluate the effectiveness of the models, the computational complexity of traditional DL techniques is calculated.

This generation's leading cause of cancer-related death is lung cancer, and it is anticipated that this will continue for the foreseeable future. Lung cancer can be successfully treated if the disease's early signs are recognized. Deep learning (DL) models based on LSTM, RNN and CNN were utilized to optimize the process of detection from the lung cancer dataset. Lung cancer patients are categorized according to their symptoms using DL classifiers, and the Python programming language is also used to advance the model's implementation. Accuracy, precision, and recall were just a few of the different metrics used to assess how good our DL models were. The suggested LSTM model was contrasted with the RNN and CNN techniques already in use. Comparing the recommended LSTM method to the current ones, it achieves a rate of accuracy of 93%.

The agriculture industry plays a crucial role in a country's economic growth cycle across the world. In the field of agriculture, weeds are the major concern for farmers as they reduce crop yield. They compete with the crops vigorously for water, sunlight, and soil. Thus, it is important to remove these weeds at early growth stages. As a result, weed classification is required, but it is a time-consuming and difficult effort to do it manually. An automated weed system based on images is required for weed classification. Deep learning approaches show better performance in image classification, among which Convolutional Neural Networks are the most preferable. Therefore, in this study, the classification of nine different species of weeds is performed using six pre-trained convolutional neural networks: Xception, Inceptionv3, VGG16, VGG19, AlexNet, and InceptionResNetv2. These pre-trained models' performance parameters, such as accuracy, f1-score, precision, and recall, are computed. The results suggest that Xception outperforms the other networks evaluated, with an accuracy of 94%.

Reading long documents, newspapers and textbooks can be generally a time-consuming process and is complex in nature. Summarization techniques play a vital role for understanding the text in simplest forms of optimal fashion. In research areas creating summaries for Research / Scientific Articles in order to get a clear picture of what the article is about, what information it contains, and so on. To accomplish this, we present SIMCLS, a conceptual framework for summarization in abstractive manner that can combine the gap between the learning objective and evaluation metrics caused by the presently dominant sequence-to- sequence learning technique by articulating text generation as a reference-free evaluation problem approach-driven by contrastive learning. Experiment analysis interpret that SimCLS can significantly enhance the evaluation of existing top-performing models with minor modifications over existing top-scoring systems. Abstractive Summarization is a type of summarization that uses rephrasing or creating new words to generate novel sentences and thus summaries for research articles, as opposed to Extractive Summarization, which uses extraction to extract important words and then generate a summary. Contrastive learning, a self- supervised, task independent deep learning technique, allows a model to learn about data even in the absence of labels. SimCLS is a simple framework that employs abstractive summarization and contrastive learning. This is used to generate summaries of research articles.

Real-time event detection is one of the many applications for wireless sensor networks (WSN). Since the sensor nodes are open to everyone, they are vulnerable to attacks. One of the frequent attacks carried out by malicious attackers that shorten the lifespan of the network and generate congestion is the denial-of-service attack. By modelling an intrusion detection or prevention system they categorise the normal and attack traffic with the present state of the network as good or poor, which is possible to identify DoS attacks by seeing the issue as a classification problem on network state. We propose that Recurrent Neural Networks (RNN) can be used to classify the novel, previously undetected variants of attacks to increase the rate of intrusion detection. Compared to the conventional machine learning classifiers, RNN architectures had a low false positive rate. The main factor is that RNN designs may store data for long-term dependencies through time lags and can correct this with information from subsequent connection sequences. In this paper the simulation was run to evaluate the model's effectiveness, and the findings indicate that the RNN as classifier model outperforms other current machine learning models in terms of improving the rate of detection and prevention by classifying the attacks with less positive rate.

In web-based item audit frameworks, clients are permitted to submit surveys about their bought things or administrations. Nonetheless, counterfeit surveys posted by fake clients frequently delude buyers and carry misfortunes to endeavors. Customary misrepresentation recognition calculation chiefly uses rule-based techniques, which is deficient for the rich client collaborations and chart organized information. As of late, chart based strategies have been proposed to deal with this present circumstance, however not many earlier works have seen the disguise fraudster's way of behaving and irregularity heterogeneous nature. Existing strategies have either not resolved these two issues or just somewhat, which brings about horrible showing. On the other hand, we propose another model named Fraud Aware Heterogeneous Graph Transformer (FAHGT), to address covers and irregularity issues in a brought together way. FAHGT embraces a sort mindful component planning system to deal with heterogeneous diagram information, then, at that point, executing different connection scoring techniques to lighten irregularity and find disguise.

Opinion mining has become difficult due of the overabundance of user-generated information on social media. Twitter is used to gather opinions about products, trends, and politics as a microblogging site. Sentiment analysis is a method for examining someone's attitude, feelings, and views. Different individuals toward anything, and it is possible to do so by analysing tweets to determine how people feel about the news, regulations, social movements, and people. Opinion mining may be carried out without manually reading tweets by using Machine Learning models. Their findings could be useful to corporations and governments as they implement policies, programmes, and events. The use of seven machine learning models for emotion acknowledgment by dividing tweets into pleased and angry categories. An extensive performance comparison investigation revealed that the suggested voting classifier (LR-SGD with TF-IDF) gives the best results.

This task predicts the yield of practically a wide range of harvests that are planted in India. This content makes novel by the utilization of basic boundaries like State, locale, season, region and the client can anticipate the yield of the harvest where year the person needs to. The paper utilizes different Machine learning models procedures like Random backwoods, Decision tree, KNN, SVM, and Navie Bayes supporting calculations to anticipate the yield and uses the idea for improving the calculations to give a superior forecast.

The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generatedalso. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically,it is more and more difficult for existing rule-based expert systems and traditional machine learning modelsystems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequentlychanging their fraud methods. In this article, an intelligent Internet financial fraud detection is proposed to implement graph embedding algorithm Node2Vec to learn andrepresent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the machine learning models.

From the past decade understanding of depressive state of humans is interesting topic of researchers. They are consider a different brain datasets which contain different set of features and apply machine learning models to identify the depressive state. But the machine learning models like SVM and KNN provided with the prediction accuracies of 98% and 78-85%, respectively. There is still need improvement in detection accuracy of identifying depressive state. In this paper, we utilize the EEG dataset with 19 channels. It can be utilized to produce the exact report on the level of depression. Our plan is to adapt and fine-tune the weights of networks to the target task with the small-sized dataset. Finally, to improve the recognition performance, an ensemble method based on majority voting of outputs of five mentioned deep TL architectures has been developed. Results indicate that the best performance among basic models achieved by DenseNet121 with accuracy, sensitivity and specificity of 95.74%, 95.56% and 95.64%, respectively. An Ensemble of these basic models created to surpass the accuracy obtained by each individual basic model.

According to some recent statistics, India accounts for roughly six percent of global road accidents while owning only one percent of the global vehicle population. With the high number of traffic incidents and deaths these days, the ability to forecast the number of traffic accidents over a given time is important for the transportation department to make scientific decisions. In this scenario, it will be good to analyze the occurrence of accidents so that this can be further used to help us in coming up with techniques to reduce them. In this paper, we have studied the inter relationships between road accidents, condition of a road and the role of environmental factors in the occurrence of an accident. We have made use of data mining techniques in developing an accident prediction model using Apriori algorithm and Support Vector Machines. The results from this study can be advantageously used by several stakeholders including and not limited to the government public work departments, contractors and other automobile industries in better designing roads and vehicles based on the estimates obtained.

Face has recently attracted more attention in a number of fields because to its security features and its simplicity of use. Face-based biometric technologies are widely used in various person identification applications. This is due to the fact that human faces are the most easily identifiable features from day-to-day living and also store the most information. However, despite ongoing attempts to spoof faces, facial recognition systems continue to be vulnerable to attack. The act of damaging or attacking a face recognition system by gaining unauthorized access to the system and exploiting security holes is known as spoofing. This may be accomplished by getting into the system without the agreement of an authorized user. The act of damaging or attacking a face recognition system by gaining unauthorized access to the system and exploiting security holes is known as spoofing. This may be accomplished by getting into the system without the agreement of an authorized user. Attacks that involve faking one's visage provide a persistent risk to face-recognition systems. Our goal is to create a system that will put an end to face spoofing, despite the fact that academics have created a variety of face spoofing detection approaches that have demonstrated to be highly effective. Anyone is capable of fooling a facial recognition system by uploading fake photos or videos of themselves, or by employing some other decoy to stand in for an authorized user's face. The proposed work, which is based on an algorithm for a deep neutral network, suggests the real spoofing prevention method by assessing the liveness of the face. Additionally, it offers defense against spoofing attacks such as image masks, replay assaults, print photo assaults, and mobile photo assaults. The technique has a success rate of 99%, particularly when applying the convolutional neural network (CNN) algorithm. In facial recognition systems, the primary goal is to accurately differentiate between real and fake faces using the CNN approach. This is done to improve accuracy.

Agriculture plays a paramount role in the economic growth of countries such as India, where demand for food supplies is high. Extreme weather and climate changes can increase the risk of plants being attacked by infectious diseases. Fungi, viruses, or bacteria can create a dangerous environment for plants. Plants are an essential part of our world, providing food and other products that many industries rely upon. With the diseases they face, it's important to identify and treat them as early as possible. Conventional plant identification methods relied on experts in the field and were time-consuming or impractical. We experimented with deep learning-based approaches for the purpose of the disease detection and classification, especially AlexNet, which is a leading architecture for object-detection tasks. VGG-16 also presents excellent performance when detecting objects. ResNet-50 has even better performance in this task and it's 50 layers deep. This paper aims for the disease detection on samples of Sunflowers.

Water is the elixir of life in this world. Rainwater, Groundwater, and River water are the major sources of water for agriculture, and the groundwater is the most valuable resources for food production in agriculture. Using important components of agriculture needs in TOPSIS for the normalized fuzzy decision matrix, 〖fuzzy+〗^ve and fuzzy-^ve ideal solutions in different quality parameters of groundwater from different places were analyzed and subjected to UPDATED FUZZY TOPSIS Method. The best correlating place in terms of water quality performance among the twenty five places have been selected under our study.

The Internet has rapidly led to the development of Internet of Things (IoT) based health monitoring systems, such as advances in sensor technologies and the emerging thought applications of behavioral and physical monitoring systems. Nowadays, many patient lives are scattered over wide geographical areas alone, and it is necessary to monitor the status of their health function. This work proposes an IoT-based health monitoring model that monitors key symptoms and detects biological, behavioral changes through smart health technologies. In this model, sensitive data are collected via IoT devices. Data analysis is carried out using the Dynamic Rule Soft Signaling (DRSS) method, which detects the possible risks of patient physiology and behavior changes. The experimental results suggest that the proposed model meets efficiency and reasonable accuracy for detecting the patient condition. This research work also mainly focuses on the Electronic Health Record (EHRs) security requirements for storage discovery, accessing, sharing and audit monitoring in Cloud Healthcare System (CHMS) using Dynamic Attribute-Based Encryption (DABE) system. After evaluating the proposed model, the dynamic rule soft signaling method and dynamic attribute-based encryption (DABE) system have achieved the best accuracy of 97%, which is a promising result for the proposed method’s objective. Proposed methods' results outperformed fuzzy, adaptive neuro-fuzzy and Artificial Neural networks.

Water is indispensable for the development and maintenance of all forms of life. There are major issues with the availability and quality of drinking water on a global scale. Drinking water contaminated with pathogenic germs, hazardous compounds, etc., can be dangerous to one's health. In this study, we describe a procedure for evaluating water excellence and providing early warning of impending contamination. The water might be contaminated by a wide variety of factors. These factors are considered and used to calculate the optimal frequency of water purification. Internet of Things (IoT) and Machine Learning are utilised by the system. It includes a physical and chemical sensor to check the parameters (pH, conductivity, turbidity, temperature, and humidity of the water in the tank). The information collected by the sensors are uploaded to a database and then analysed. Predictions are made using the Sequential Learning Neural Network (SLNN) method. There are a lot of factors to be considered when setting up a neural network, and finding the optimal setup may be a tedious and time-consuming process. The Sequential Learning Neural Network method is used for this task because it is efficient and may significantly reduce the amount of time and information storage required. It creates a non-linear relationship between input and output. When any of a user's parameters fall below predefined thresholds, the system issues a warning message. The client can get an early warning concerning water contamination in their storage tanks. This method may be used in large-scale water treatment plants as well as in private homes with storage tanks.

A graph H is said to be a product cordial graph if there is a mapping g from V(H) to {0,1} such that if each line rt is given the value g(r).g(t), then the cardinality of points with value 0 and the cardinality of points with value 1 differ at most by 1 and the cardinality of lines with value 0 and the cardinality of lines with value 1 differ by at most 1. In this case, g is said to be a product cordial labeling of G. In this paper, we investigate the product cordial labeling of some snake related graphs such as D(T_p )⊙(K_1 ) ̅,D(T_p )⊙(K_2 ) ̅,D(T_p )⊙(K_3 ) ̅,D(Q_p )⊙(K_1 ) ̅,D(Q_p)⊙(K_2 ) ̅ and D(Q_p)⊙(K_3 ) ̅.

The decision making is used to calculate the suitable alternatives with different criteria for the desired problem. The process of identifying a patient’s sickness or condition from the disease symptoms or indicators is known as medical diagnosis. In this paper, two sets of criteria and alternatives are mentioned with number of kid and disease and disease with symptom using linguistic variables, Fuzzy ELECTRE I (Elimination and Choice Translating Reality) method is used to obtain the priority of the kid’s selection for the treatment by combining two sets of criteria and alternatives with obtained CRITIC (Criteria Importance Through Inter Criteria Correlation) weight. The minimum degree of possibilities for each criteria over the other for combining the sets are calculated using as modified Chang’s method. Moreover, the comparison analysis demonstrates that the fuzzy concordance select the maximum weight qualifier for which diagnostic process is more reliable and accurate. This paper presents and finds the particular child is affected with particular disease with different symptom using above two sets of criteria and diseases.

Foam concrete or cellular light weight concrete or reduced density concrete is formed by mixing Portland cement, fine aggregates, water and stable foam. It is called as the cellular concrete because of the pores introduced in concrete due to foam. Foam concrete is being used in non-structural applications extensively like void filling, filling of sunken slab, precast blocks, sub base in highways, prefabricated insulation boards etc., Limited works are carried out in foam concrete as a structural member. An attempt was made to study the flexural strength of foam concrete slab specimen using Sodium Lauryl Ether Sulphate (SLES) liquid as foaming agent, M sand with constant mix proportion (cement : fine aggregate) of 1:1 and welded mesh / woven mesh as reinforcement. The density of foam concrete was kept constant at 1500kg/m3and was subjected to flexural test. The test results were compared to the test results of the same slab specimen of normal mix without foam. It was noticed that the slab with foam failed at a lower load and deflected more when compared with the slab without foam. Test results revealed that the slab specimens with foam concrete can be used as a structural member subjected to low loads. The load carried by the foam concrete slab was sufficient to be used as a light roofing member.

The paper is about a single channel queuing problem. It shows how to solve queuing problems with a single-channel queuing model that has no losses. It consists of a queue and a service centre, and it uses the FIFO system. The service is provided by the station for the first element in line. This theory is the ordered analysis of this unique in order to find best result so that everyone gets helping hand without having to expect in line for an extended the time. The implementation is explained in C++ coding concept.

With the increasing use of network and computer resources on the Internet, defending against attacks and intruders has become a key concern. One way to assess the network security problem is to detect intrusions. An intrusion detection system (IDS) gives your infrastructure an additional layer of security. As cybercrime has evolved over time, intrusion detection technology has also advanced significantly. Researchers have worked to enhance intrusion detection while maintaining network performance ever since the technology's development in the middle of the 1980s. An Intrusion Detection System (IDS) was implemented using the extensive dataset available from the Canadian Institute of Cybersecurity (CIC). This paper proposes a network intrusion detection algorithm based on the random forest classifier. The dataset was appropriately formatted and some records containing non-numeric values were weeded out. The record set was processed using the SCIKIT library in Python which offers Random Forest Classifier (RFC) object for machine learning. After the RFC was trained with 70% of the records, the testing carried out using the remaining 30% of records indicated result accuracy of 99.853%.

Globally increase in the temperature causing various effect on earth and human lives and therefore has become more concern of the researchers. The supply chain activities, are also involves energy consumption releasing carbon di oxide gas which is more responsible affecting earth temperature. Introduction of technologies in supply chain system faster the release of carbon emission, mainly during production and stocking goods in warehouse. This paper focus on sustainable green supply chain model (SGSCM) preferring in reduction of carbon emission by learning effect on production as well as on holding cost together with consideration of price sensitive and stock level dependent demand rate of market which is realistic phenomenon of customers. The two-stage integrated sustainable green supply chain model (SGSCM) with vendor-buyer policy for single item has been developed with joint agreement of investing in learning process to reduce time of production and number of shipments together with carbon emission under centralised policy. Authors consider investing in learning in production and training labour affecting carbon emission and increasing overall profit of supply chain. Validity of model is carried by numerical illustration with comparison of centralised and decentralised policies.

This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

In the present paper, classes and are introduced from the family of analytic functions. We have worked on Leaf Like Domain and studied the Fekete-Szego inequality for the Starlike and bounded turning functions with (p,q) derivative operator. .

In this paper we are going to study a new subclass M_f of S. The main motive of our this paper is to find an estimate of coefficients of Analytic-univalent functions in M_f and also find the upper bound of Hankel determinants upto 4th order of M_f.

The basic concept of a set theory is fundamental for the whole mathematics. As an extension of classical set theory, rough set theory is a relatively mathematical tool used in various sectors that they are characterized by vagueness and uncertainty. In recent years, many similarity measures have been proposed between fuzzy sets and fuzzy rough sets. In this paper, we discuss two similarity measure (having properties) for mapping the degree of similarity between fuzzy rough sets. These measures can represent a better way for measuring the degree of similarity between fuzzy rough sets. Finally given the illustration for the problem of choosing the mutual funds by similarity measures in fuzzy rough sets.

For third-party businesses, millions of reviews are shared on platforms such as Amazon (e-commerce), Airbnb (travel and hospitality), OYO (hotels), and Google. Because reviews have a great influence on consumers, spammers use Fake reviews to promote their products, services, or organizations while demoting competitors. Several researchers have presented various methods for detecting bogus reviews. This research focuses on detecting false reviews using deep learning approaches. The research is broken into 2 sections: the first half examines ML methods and the reasons for preferring deep learning over machine learning. The second section of the study includes a systematic review of deep learning algorithms, summarizing these techniques in a tabular format, as well as the current research gaps in the relevant research field.

Avoiding failure in construction projects is not a simple process, which makes project failure to meet its objectives a serious issue faced by all countries, including Indonesia. Where over two-thirds of all construction projects worldwide have encountered serious issues, such as an increase in the project's cost, a delay in the project's scheduled completion date, or the project's cancellation. As a result, it is necessary to research and implement innovative ways for managing construction projects to assure their success and achievement of their objectives. The purpose of this study is to data is processed with the partial least squares (PLS) technique. The findings of the research indicate that there is a correlation between Agile Construction Management and Construction Projects. Additionally, research indicates that risk management, quality management, and strategy management all influence the Agile Construction Management connections and construction projects and investigate Agile Construction Management and its influence on building projects. Additionally, to identify the Agile project management concepts and principles that may be utilized in the Indonesian construction sector for it to be embraced as a new approach for managing construction projects in Indonesia. The researcher conducted a study of the pertinent literature to define Agile Construction Management, its methodology, and influence on building projects. The researcher then performed a questionnaire survey of a sample of engineering specialists that work for four major building project stakeholders: (beneficiary, supervising, designer, and contractor). The findings of this study indicate that it is possible to use the four Agile Construction Management ideals and eleven of the twelve Agile Construction Management principles to managing Indonesia construction projects.

A graph G=(V,E,ϕ) with p vertices and q edges is called Fibonacci graceful graph [FGG] if a function ϕ isdefined as ϕ:V(G)→{0,1,2,…,F_q }and the induced edge labeling ϕ^*:E→{F_1,F_(2,) F_3,…,F_q } defined as ϕ^* (uv)=|ϕ(u)-ϕ(v)| is bijective. In this article an examine is done with Fibonacci graceful labeling [FGL] for some special graphs. AMS Classification : 05C78

In order to accurately identify and recognize text, it is common practice to employ the Laplacian operation on video images to boost contrast. While Laplacian operation does enhance contrast, it also adds an excessive amount of noise, which is a fact. To alleviate this, the existing approaches provide different filters and enhancement methods. In this paper, we propose a generalized enhancement model based on fractional calculus to increase the quality of images generated by Laplacian procedures.By considering edges and their neighbor information, the recommended method creates a mathematical model for enhancing low contrast information in video and scene photographs. Experimental results on several datasets show that the proposed enhancement model significantly enhances the accuracy of text recognition and identification approaches. When the enhancement model is contrasted with traditional enhancement models, the proposed method outperforms the present models in terms of quality metrics. The effectiveness of the proposed model is validated using text detection and recognition tests.

The use of renewable energy-based power generation is the most versatile usage in the fast moving machinery world. It is also a secure and safe form of energy utilization. Nowadays, smart grid distribution of power systems is popularized and efficient usage is promoted on the utilization side. With the help of the IOT (internet of things), we monitor the power distribution in transmission lines and give the information to the TNGEDO Sector side with the help of the IOT (internet of things).Transmission lines fell down on earth for a variety of reasons, or faults occurred during their lives. The transmission line conductor is affected by rain, age fault, thunderstorm, and storm. For the swing and line to ground fault to occur for the above reasons, at that time, we didn’t clear the fault immediately. So in this paper we analyzed and overcome the issues of both consumers and TNGEDO's side communication system. Faults occur at the time of heavy loads and light loads. It depends upon the consumers utilizations. The human death rate increased at that time of line to ground fault occurrence due to the unawareness of human beings. These issues are resolved thanks to the smart sense meter and instant communication between the consumer and substation sides.

The development of health technology and advances in diagnostic equipment has led to the highly challenging process of medical analysis and diagnostics in multiple dimensions of large data sets. Based on large amounts of data, and data analysis, very complex problems are raised, from medical reasoning automatic knowledge extraction. This is mainly due to methodological problems inherent in multidimensional data analysis, as well as due to limitations due to the performance of computer systems. Therefore, it is often called the "curse of dimensionality". It can reduce the number of large datasets by reducing the number of analysis parameters (sizes) or by reducing the number of analysis cases. This dimensionality reduction, or feature selection method, can be used by statistical methods, primarily principal component analysis (PCA).PCA is used in many medical data analyzes. PCA performs principal component analysis and transformation of ECG datasets. Dimensionality reduction is achieved by setting a threshold or designation for the score to maintain the highest number of attributes. The PCA algorithm takes the original function as input and generates a new function. These are linear combinations of the original features selected.

In the classification of medical data, based on cardiac conditions such as heart rate variability (HRV), clustering is often used to see if HRV records have any rules in the crowd. They were used to determine the feature space and the rates collected at a particular location. Clustering is the process of dividing dataset D into groups of clusters so that objects in the same cluster are more similar to each other than objects in different clusters. Clustering ensembles (CE) are motivated by the fact that the most basic clustering algorithms' performance is highly data-dependent. Specific clustering algorithms can create partitions suitable for given data, proving weak due to some other data. In general, there are two main challenges specific to algorithm clustering. First, various algorithms can find different structures from the same dataset. For example, the K-means may be the best algorithm for spherical clusters. Second, a single clustering algorithm with other parameters can reveal different structures within the same data set. Therefore, choosing the best clustering algorithm for a given dataset is very difficult. Another mechanism to solve this difficult problem is a new method based on some basic partition combinations. The above process is widely referred to as the "cluster ensemble". It aim for a clustering ensemble to combine multiple cluster analysis models for better results than each clustering algorithm in terms of consistency and quality. Clustering an ensemble is usually a two-step algorithm. The first stage stores the results of some independent operations of the K-means method or other clustering algorithms. The second stage uses a specific consensus function to find the last partition in the stored results. Cluster ensemble issues can usually be defined as multiple clusters of a particular dataset, and it turns out that combined clusters have better yield performance. The problem of combining clustering contains some features of the classic clustering problem. The three main issues of the struggle are the functioning of consensus, the diversity of clusters, and the strength of the chemical composition clustering model.

In the dynamic environment of health and information and communication technology (ICT), it has served as a guide and has significantly affected all levels of the healthcare model. A combination of health information technology and information technology medicines uses computer hardware and software to collect, process, store, distribute, exchange information and make decisions in electronic form of audio files, images, Responsible for sharing texts and comprehensive digital information. The rapid development of technology and health information encourages various institutions to provide medical services to obtain the skills needed to provide highly qualified medical services. Electronic health records (EHR) and electronic medical records (EMR) are one of the most widely used technologies in health systems, and in addition, much research has been done on this topic in recent years. Electronic medical records and electronic medical records contain all information related to the health of citizens even before they were born after their death. It is continuously collected over time and stored electronically. All or part of the record can be granted by the authority holder, regardless of location or time. The electronic medical record EMR represents one of the most ideal medical information systems (MIS), which must be carefully designed and managed to meet the needs of society. The introduction and deployment of electronic medical records and electronic medical records is the ultimate goal of establishing health system information technology, but this always involves many obstacles and challenges. Using different perspectives, researchers have considered obstacles, acceptance, use, continued use, and implementation, operation, and implementation of electronic medical record systems, and have achieved a wealth of results. Machine learning methods have been adopted for diagnosis and disease prediction, but some challenges remain. Dataset quality is one of the key factors for supervised learning and unsupervised learning models. Models trained on incorrect or biased data can lead to poor quality predictions. Machine learning models require sufficient training data to have good classification and prediction results. However, medical data extracted from electronic medical records and electronic medical records often suffers from data loss issues for a variety of reasons, including testing equipment, disease progression, and other availability.

Modern wireless and mobile communication systems requires antenna which should have light weight, low profile, low cost, and easy to be integrated with RF devices. This demand is completed by microstrip antennas. This paper presents rectangular microstrip patch antenna for WLAN applications designed using IE3D software. The microstrip antenna is designed with dielectric substrate as Rogers RT/duroid 5880(tm) with ɛr = 2.2. This antenna will work on IEEE 802.11 WLAN 2.4 GHz band. The antenna is optimized to improve the performance measures like gain, return loss and efficiency. The IE3D result shows this antenna is suitable for WLAN applications.

Concrete has superior tensile strength but efficient compressive strength. Concrete can be referred to as sacrificial concrete because it acts as a strain-transferring medium for RC flexural elements below the neutral axis. High-grade concrete will not only be uneconomical but also unfriendly to the environment when used throughout the segment. Therefore, using M20 nominal grade concrete below the neutral axis may be advocated. The use of nominal grade concrete below the neutral axis causes early cracks. Early cracks would lower the serviceability limit condition. Fibres will be used as a solution to increase the initial crack load and reduce crack width. In this study, ICSF (Industrial Crimped Steel Fibres) and WTSF (Waste Tyre Steel Fibres) are used. To determine fresh and hardened concrete properties, fibre combinations of 0.20%(ICSF) + 0.05%(WTSF), 0.30%(ICSF) + 0.10%(WTSF), 0.40%(ICSF) + 0.15%(WTSF), 0.50%(ICSF) + 0.25%(WTSF), and 0.60%(ICSF) + 0.25%(WTSF) were used. For freshly laid concrete, slump tests and compaction factor testing were performed. 28 days on cured concrete, tests for flexural strength, split tensile strength and compressive strength were performed. Added 20% fly ash to mix to replace cement.

Pollution-induced flashover of outdoor ceramic insulators is one of the critical factors which governs power system reliability. Deposition of the air-borne particulates and condensation of vapors on the surface forms layers of pollution which cause degradation in their performance. To mitigate this problem, several techniques such as silicon grease coating, RTV coating, increase in creepage distance, and regular washing of the insulators have been tried with varied success. All these methods are quite expensive and add to the overall cost of power delivery. It is observed that due to the deposition of contaminants on the surface of the ceramic insulators, the field around the insulator becomes highly nonuniform which may lead to flashover. In the present work, a simple and inexpensive method that can increase the pollution withstand capability of the polluted ceramic insulators is proposed. As a part of the proposed method, ceramic specimens were prepared, conduction bands were placed and flashover experiments were conducted with and without conduction bands under clean and polluted conditions. From the test results, it can be concluded that by optimally placing the conduction band on the surface of the ceramic insulators, their pollution withstand capability can be significantly enhanced.

The polymeric insulator used in high voltage system, due to their organic nature suffers from the surface degradation under various environmental, electrical stress. When these insulators are used in marine, industrial premises, under fog, mist and dew leakage current starts to flow, it results in flashover, untimely failure of the insulators. So in order to improve its flashover performance under dry and wet conditions, work was carried out by incorporating various inorganic oxides with 0, 1, 3and 5wt% into the polymer base. These are Al2O3, CeO2, MgO, TiO2, ZnO which are synthesised in the lab using solution combustion and Sol gel methods. Their Nano size and phase conversion is ascertained with XRDE test. The lab fabricated dry, wet flashover test was developed. The composites with five different inorganic oxides are aged with environmental chamber with UV(320nm), Humidity (95%), Temperature(500c) and Electric stress(5.5kV) as per IEC 62217. The dry, wet flashover tests are conducted on virgin, aged silicone rubber composites. It was found 3wt% CeO2 has least variation in flashover under both the conditions. The good bonding between Nano fillers and silicone base has paved the way for depolymerisation, regulations of the energy gains of the free carriers and enhancement of water resistance capability of the composites. This makes Nano filled Ceric Oxide Silicone rubber one of the attractive alternative for high voltage insulation system.

One of the most important machine parts, gears can be found in a wide variety of rotating machinery, including transmission systems. Significant losses occur in the industry when such crucial components fail. The most popular technique for figuring out the kind and extent of issues with machines and components is vibration analysis. A safer operation is the consequence of early problem detection and identification, which allows for properly timed shutdowns to prevent catastrophic failure. Recently, a great deal of research has been done to build dynamic models of faulty gearboxes in order to better understand the mechanism of gear fault creation and subsequently devise efficient fault detection and diagnosis techniques. The focus of this study is on dynamic gearbox fault modeling, detection, and diagnosis. The vibration analysis of the gear box was obtained from various fault circumstances, speed and loads in the study of this study. For all conditions, vibration time domain and frequency domain signals are obtained, and the resulting signals for faults are compared to the signals of healthy gear. Examination tests on an experimental test rig were used to accomplish experimental validation of the suggested system. In addition, the model shows that changing the load and speed causes the same results as the trails. Finally, multiple signal processing algorithms were used to analyze the vibration data collected from an accelerometer mounted on the gearbox shell.

Ceramic insulators have been in use for overhead transmission lines for many decades and have proven their long life. Though the performance of ceramic insulators is excellent under dry conditions, they deteriorate under polluted conditions, leading to pollution-induced flashover. One popular technique to mitigate this problem is to coat the surface of the ceramic insulator with Room temperature vulcanized (RTV) silicone rubber coating. From the literature, it is seen that adding nanofillers to RTV will improve its electrical thermal, and mechanical properties. In the present work, Zinc Oxide (ZnO) nanomaterials were prepared in the laboratory using the solution combustion method. The XRD diffraction was performed on the prepared fillers to realize their phase formation in the nano range. The fillers were added to the RTV base in different weight percentages to form nanocomposites. Water absorption tests have been performed on these nanocomposites to check their affinity to water. These nanocomposites were coated onto the ceramic insulating specimens and were subjected to heavy pollution. These polluted specimens were placed inside the fog chamber and flashover tests were carried out. From the flashover test results, the performance of 1ZNRTV specimen is found to be superior in comparison with other specimens. From water absorption test results, it can also be seen that 1ZnRTV to be absorbing less water indicating the reason for its improved performance under polluted conditions. From the studies, it can be concluded that RTV with a suitable concentration of nano ZnO additives can be seen as an alternate solution for the pollution problem of ceramic insulators.

An increasing quantum of data available on the web, news websites, published articles in various fields of study, and electronic books have generated a valuable resource for extracting and analyzing information. The main challenge for researchers has been that of accessing accurate and reliable data. This information must be summarized to retrieve helpful knowledge within a reasonable period. Text summarization is the process of automatically creating a condensed form of documents and preserving its data into a shorter version with overall meaning. Text summarization is divided into Extractive and Abstractive Summarization. The extractive summarizer extracts the basic sentences or phrases from the original document. In contrast, an Abstractive summarizer generates a summary by rephrasing the original text with the new one, which is closed to the human-made. If we consider research for summarization in Indian Language mostly work done of extractive, now researcher move towards the abstractive summarization. Language tools such as Part of Speech taggers and Named Entity Recognizer for Indian languages are not very competitive. Hence, language-specific techniques do not perform very well for Indian languages. With the advent of deep learning architectures, many tasks relating to natural language processing have been achieved; hence can overcome these short comes. This paper reviews existing techniques applied for abstractive text summarization of the Indian regional Language and its significance. In addition, the survey represents the Deep Learning-based abstractive text summarization with valuable adoption of conventional approaches to uplift the abstractive text summarization.

Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. Under non-ideal conditions, existing segmentation technique which based on local operations cannot find true iris boundary. As a result, system failure will occur. To solve this problem, a new algorithm which is based on feature extractions, classification using vgg16 is introduced. The execution has performed on the MATLAB software and performance results carried out in terms of accuracy, precision, sensitivity. Proposed scheme has been tested on three well-known iris databases CASIA, MMU and UBIRIS-V2, and is shown promising results with best accuracy rate of 90.91%.

As the need for greater and better scalability grows, the usage of cloud computing is becoming increasingly widespread. Virtually all traditional applications are being moved to the cloud by consumers, service providers, and application owners alike. Customers as well as service providers gain from this. Customers benefit from cost reductions by achieving maximum scalability and providing additional features at the lowest possible cost, which in turn leads to higher customer satisfaction in the long run. However, these actions, as well as new installations, are drawing the attention of hackers and attackers. Recent attacks on significant services, such as search engines and storage services, as well as vital applications ranging from healthcare to defense, have all been documented and documented. The attacks may be limited to data research, data consumption, or even service destruction, or they could be more extensive. The most challenging part of detecting these attacks is figuring out what kind of connection request is being made. Defensive security is not adequate to protect cloud services; security as a service must be implemented automatically and constantly across all applications, services, and data centers to be truly safe. Several recent research have shown significant advancements in the detection of security breaches. Despite this, security breaches have occurred despite these precautions being taken. Additionally, the current techniques are not automated and thus cannot be included in the to detect any anomalies in request types, this research proposes an automated framework methodology for identifying the application traffic pattern, among other things. The primary purpose of this effort is to identify different types of attacks and prevent more cloud service damage while using little computer resources. In addition, this research identifies a preventive mechanism against typical assault types. The research also demonstrates how to use traffic pattern analysis to discover new sorts of attacks, which will help to make the cloud computing application hosting industry safer.

India has been home to world’s earliest civilisations as well as some of the most prominent ancient cities. Urbanisation in India is of peculiar nature and has been of immense interest to urban practitioners. Tracing back their roots to few centuries or more, Indian cities face unique challenges including sustaining fast paced development while retaining the cultural essence, modernising the urban infrastructure while retaining the centuries’ old identity, develop in a sustainable manner and be resilient to bear future shocks. These challenges necessitate careful planning both in economic as well as spatial terms. This study reviews the post-independence policies of urban India with respect to focus on urban development, economic advancement, and resilient growth. The study highlights the interconnectedness (or the lack there-of) of channelizing economic growth while managing the sprawl and designing/ creating urban systems which are resilient.

The article provides information on development and implementation of the system of identification and automated accounting of cotton lint bales in accordance with the world standards PBI (Permanent Bale Identification). Implementation of this system allows to form and dispatch homogeneous lots of cotton lint in terms of quality (grade, class, type, etc.) on the basis of quality parameters determined for each bale on instrumental systems SCITC (HVI). In contrast to the existing system, cotton lint lots of uniform quality are formed at wagon or container volumes at cotton terminals. The device (console) of permanent identification, as well as software for identification, accounting and sorting of cotton lint bales have been developed within the framework of realized project.

Uzbekistan is taking comprehensive measures to develop the vegetable oil production industry. Unrefined cottonseed oil contains coloring pigments of various composition and properties. The purpose of this work is to develop a technology for obtaining effective adsorbents for the adsorption purification of cottonseed oil from natural kaolin Sultan-Uvays and to study the change in mineralogical properties. Natural kaolin in its natural form has low adsorption properties; methods of its activation and modification have been applied. In order to effectively adsorptive purification of cottonseed oil, natural kaolin Sultan-Uvays is activated and modified by various methods, mineralogical properties are studied by infrared spectroscopy, X-ray diffraction analysis, and the specific gravity of the obtained kaolin samples is determined.

The Narrowband Internet of Things (NB-IoT) has been presented recently to offer inexpensive, low powered, wide area cellular connectivity for Internet of Things (IoT). With the integration of NB-IoT, low power wide area network (LPWAN) becomes a familiar low rate long range radio communication technology. But resource management such as power consumption reduction in NB-IoT is yet to be improved to attain energy efficient architectures for standardization and commercialization. This paper designs an optimal DBN for power consumption reduction (ODBN-PCR) model in LPWAN using NB-IoT environment. The proposed ODBN-PCR model intends to minimize the power utilization by the use of DBN model, which helps to improve the battery lifetime of the LPWAN device. The proposed model involves data pre-processing in the initial stage in two levels namely data splitting and data scaling. Also, the hyperparameter tuning of the DBN technique take place utilizing Water Strider Optimization (OWSO) technique and thereby improves the performance of the DBN model. For examining the enhanced performance of the ODBN-PCR technique, a wide range of simulations take place. The comparative results analysis portrayed the better performance of the ODBN-OPCR technique over the other others interms of power consumption.

The causes of the lung cancer to be common cause of the death among the people throughout the world. This is one of the diseases that causes the deadliest impacts which can also cause fluid to accumulate around the lungs, making it harder for the affected lung to expand fully when some-one inhale. This work has introduced one of the best ways to detect the lung cancer and the methods that are used to detection to increase the accuracy and yield and decrease the diagnosis time. The major things in this work consider two datasets, including the Lung Image Database Consortium and Image Database Resource Initiative. In this work it basically considers the two dataset the main dataset and public dataset. The identification of lung cancer a novel diagnosis method based on the Deep Transfer Convolutional Neural Network (DTCNN) and Extreme Learning Machine and Extreme is explored which has been trained with the Image Net dataset beforehand. When this work compares between DTCNN and ELM is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in lung cancer, distant recurrence identification still relies heavily on manual chart review. Two datasets, including the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) public dataset for LIDC-IDRI dataset, the experimental results Shows different type of results but the performance of the novel DTCNN-ELM model achieved the performance with an accuracy, a sensitivity, a specificity, an area under the receiver operator curve (AUC) and testing time per which has the most reliable results compared with current state-of-the-art methods.

Worldwide,Vehicular Ad Hoc Networks (VANET) technology plays a vigorous role in providing the optimized expedition between the source and target. Modern technologies and Intelligent Transportation Systems (ITS) are associated significantly with providing wireless-based communications between vehicles (V2V). The Vehicular Ad Hoc Networks (VANETs) technology is used to increase the fidelity of interactions, effective traffic management, and driver's convince. In this smart technology, confidential information is transferred between autonomous vehicles. The autonomous vehicular technology is controlled with great flexibility but in the public network, the VANET attackers can create and transmit malicious messages to control the entire vehicle. Requirements on the security and privacy of VANET applications need to be addressed by developing a novel attack detection approach. A more reliable and highly effective approach is required to detect and avert DDoS attacks in smart VAVET applications. In this paper, we are reviewing the Distributed Denial of Service attacks (DDoS) and possible control mechanisms (Machine Learning, Cryptography, Data Mining) in the VANET applications for increasing the performance on hazard discovery. Future, our approach is aiming to implement in various real worldssmart vehicular applications for detecting the various attacks with high accuracy and effectiveness.

The Internet of Things (IoT) is a cutting-edge technology with a wide range of uses, capabilities, and services for daily living. Utilize the industrial innovation and economic ecology of the Internet of Things in various markets. IoT, which consists of networked devices, aims to be a reliable global information network at all times. IoT uses various cutting-edge technologies to intelligently and effectively integrate information, data, and knowledge into process equipment. An innovative economy, particularly one that uses finance, has been characterized as being driven by the National Policy on Science and Industrial Innovation Technology Park. Using historical data from one of the most well-known high-tech industrial estate discoveries in the world, Sustainable Development in Innovation Ecology proposes an analysis and discusses the effects of the National Economic Science Park, an industrial park, and innovation policies: industrial park. Field Programmable Gate Arrays (FPGAs) and the Internet of Things are the foundation of an organized research policy based on innovation-based economics and technology policy (IoT). The science and industrial park technology is assessed over the long run by including economic value.

The changeable weather conditions as well as partial shading situation provide several obstacles in collecting the maximum possible electricity from solar Photovoltaic (PV) systems. The PV system's ability to gather maximum power through maximum power point tracking is hampered mostly by partial shade. In the literature, several MPPT techniques based on bio-inspired optimization approaches have been proposed. These algorithms' procedures vary, causing them to behave differently when assessing global peak power. This work offers a novel Hybrid MPPT model that combines the LSTM algorithm with the Perturb & Observe approach to extract maximum power from photovoltaic fixed overheads in response to variations in solar irradiation and partial shade. LSTM conducts the earliest stages of maximum power point tracking to reach a quicker approximation to the global peak, contributing to the total stage selection of the P & O technique. This method might be effective for mitigating the effects of partial shade. To demonstrate the efficacy of the suggested algorithm in comparison to standard methodologies, 12 hour irradiance characteristics are applied to a solar PV system. MATLAB/SIMULINK software was used to create the suggested MPO-MPPT algorithm-based solar PV system. The system results validate the theoretical analysis of the suggested technique, increasing the PV system tracking efficiency to 97.6%. .

Objectives: Raga's role in classical Indian music never ends. The music of the Indian subcontinent is known as Indian classical music. Raga recognition is crucial for automated Raga identification systems.Raga is a set of various distinctive notes (swara) with certain special characteristics. Methods:These method help us to define the specific raga as well as the musical instrument performed in a specified polyphonic audio signals.Despite earlier methods to this issue, which mainly rely on pitch features, this paper seek in this study to construct hybrid features that can learn the distinguishing traits of a raga from the prevailing pitch elements in a music. Findings: Here extraction of features is performed using spectral centroid, spectral crest factor, spectral decrease, spectral flatness, spectral flux features. Support Vector Machine (SVM) algorithm is use to classify the characteristics as the number of clusters in a specified dataset is estimated. Finally, the raga is labeled using a supervised learning algorithm called Support Vector Machine(SVM). .

The researchers in point set topology studied several types contra continuous multifunctions. Recently the authors discussed the concepts of upper b#-continuous, lower b#-continuous, upper *b-continuous and lower *b-continuous multifunctions. In this paper our primary aim is to introduce the notions of upper contra b#-continuous, lower contra b#-continuous, upper contra *b-continuous and lower contra *b-continuous multifunctions and to discuss their basic properties

Entrepreneurs are seen as national assets to be motivated, cultivated and remunerated to the greatest degree possible. Entrepreneurs develop innovative ideas that provide civilization with a large number of products and services which change the way we work and live. The benefits they offer are by creating job opportunities, improving standard of living and contributing to the overall growth of the economy. The present study focuses on scenario to of women entrepreneurship in the North Eastern state of India - prospects and challenges of woman entrepreneurship , along with the promotional policies relating women entrepreneurship for the future In view of the ever increasing problems facing the women entrepreneurs. The North Eastern Region of India (NER) is unique in terms of growth opportunities because it has the potential to become India's economic powerhouse, with a thriving source of energy, oil, natural gas, coal, and limestone, as well as the world's largest perennial water system in the Brahmaputra and its tributaries. As most of the population live in rural area in North East region due to lack of education and skill development courses of entrepreneurship, it is very difficult to growth of woman entrepreneurship in North East India. Education has been instrumental in increasing the participation of women in entrepreneurial activities. Government should provide batter educational facilities and schemes to women folk. A Women Entrepreneur's Guidance Cell should be set up to handle the various problems of women entrepreneurs all over North East Region.

New surface-active reagent has been synthesized on the basis of hexadecylamine and ethylene chlorohydrin. Composition and structure of this reagent have been identified by IR- and UV- spectroscopy methods. By tensiometric measurements, its high surface activity at the water-air border has been shown. By electroconductometric method, specific electrical conductivity of the aqueous solutions of the salt has been determined. The diameters of the aggregates formed by the synthesized surfactant in aqueous solutions have been determined via dynamic light scattering method. Petroleum-collecting properties of the synthesized salt (in the pure state and in the form of 5% wt. aqueous solution) have been studied on the example of crude oil from the Pirallahi oil field in the Absheron Peninsula (Azerbaijan). The surfactant (0.02 g) or its solution was added to a thin film (thickness 0.15-0.16 mm) of this petroleum on the surface of distilled water, fresh water and the Caspian Sea water (separately) in Petri dishes. By laboratory tests, its effectiveness for removal of ecologically-hazardous thin petroleum films from the water surface has been revealed.

The main aim of the article is to investigate the design and substantiates the principle of operation of a two-circuit electrode water heater, adapted for simultaneous connection of two heat-using installations when heating process water for hot water supply and heating systems. Justification is given for the hydraulic method of power control based on changes in the level of boiler water in the interelectrode space under the influence of thermal- and hydrodynamic processes. In this case, automatic regulation (self-regulation) of the electric power of the water heater is provided in accordance with the dynamics of changes in the thermal load of heat-using installations without the use of special regulating devices and automation tools. The possibility of step-by-step manual regulation of the heat power transmitted by the heat exchanger to the process water is shown. The results of experimental studies of the operation of a two-circuit electrode water heater in various modes are presented.

Green Accounting is the root which is followed towards the sustainable future. The sustainable level of income which is achieved without depleting the Natural Assets of the Nations. The Green Accounting term was first introduced into common usage by economist and Professor Peter Wood within the 1980s. The purpose of this paper is to study and analyze the available literature based on the green accounting and to understand how it has been studied and evaluated by different authors who are working in this area and the role of green accounting in developing Indian economy. Current literature focuses on Green Accounting - importance and it’s Concept in developing India. The study suggests that there should be wide empirical studies during this area so that, through the Green accounting we can analyze the environmental performance of each companies or organizations. One approach that's gaining momentum across the globe is “green accounting” where by national accounts are adjusted to include the value of nature´s goods and services. To take care of GDP growth as this is the perceived foundation upon which the future economic security of its growing population is based, but conversely, India must also take into consideration the prices of development and not self-cannibalize its rich natural capital wealth and jeopardize the very future of the people it is trying to secure. Over-reliance on GDP as a measure of economic health are often misleading. As noted way back by Robert F. Kennedy: “it measures everything, in short, except that which makes life worthwhile.” the most objective of the study is to understand the role played by Green Accounting in the development of the Indian Economy and also its objectives, stages, form, need and challenges. Methodology of the paper is predicated on the secondary sources with respect to the role played by green accounting in developing India. The paper is a conceptual research paper.

In the last few years, the Internet of Things (IoT) has proved to be an interesting and promising paradigm that aims to contribute to countless applications by connecting more physical “things” to the Internet. Although it emerged as a major enabler for many next generation applications, it also introduced new challenges to already saturated networks. The IoT is already coming to life especially in healthcare and smart environment applications adding a large number of low powered sensors and actuators to improve life style and introduce new services to the community. The Internet Engineering Task Force (IETF) developed RPL as the routing protocol for low power and lossy networks (LLNs) and standardized it in RFC6550 in 2012. RPL quickly gained interest and many research papers were introduced to evaluate and improve its performance in different applications. In this paper, we present a discussion of the main aspects of RPL and the advantages and disadvantages of using it in different IoT applications. We also review the available research related to RPL in a systematic manner, based on the enhancement area and the service type. In addition to that, we compare related RPL-based protocols in terms of energy efficiency, reliability, flexibility, robustness and security. Finally, we present our conclusions and discuss the possible future directions of RPL and its applicability in the Internet of the future.

Cancer is the second leading reason of death worldwide. Breast cancer (BC) is the most typically diagnosed and dominant explanation of cancer-related death in middle-aged women. Invasive ductal carcinoma (IDC) is the major diagnosed type of carcinoma. Pathological analysis of the biopsy means taking the sample from any part of body (some cells or fluids) and sent to a laboratory for the test observed under the microscope. Early detection and treatment can improve endurance rates worldwide. In this paper, we propose two methods for classifying BC. In the first method, whole slide images (WSI) [1] are input into a Residual neural network (ResNet) for feature extraction. Second, features are extracted and then fed into Support Vector Machines (SVM) for BC classification. We used the open Breast Histopathology Image (BHI) Dataset where we achieved 87.80% accuracy and an 88% F-1 score.

Mechanical properties of concrete depend on the curing condition of concrete. The ACI-308(2010) through ACI-308(2014) Code states that “internal curing refers to the process by which the hydration of cement occurs because of the availability of additional internal water that is not part of the mixing Water”, curing concrete means that water is not lost from the surface i.e., curing is taken to happen from the outside to inside. In contrast, internal curing is allowing for curing ‘from the inside to outside. Internal curing’ is often also referred as ‘Self–curing.’ Any negligence in curing will interfere in the strength and durability of concrete. Shrinkage reducing agents and lightweight aggregates such as Leca and Polyethylene-glycol, Silica fume and stone chips are used respectively to achieve effective curing results. It is observed that there is an increase in compressive strength by using polyethylene glycol (PEG) and light weight fine aggregate (LWA).

Wireless Sensor Networks (WSNs) consist of sensor nodes deployed in a manner to collect information about surrounding environment. In this paper design collaborative multi agent trust-based data mining technique for intrusion detection in wireless sensor network has been proposed .In their distributed nature, multi hopdata forwarding, and open wireless medium are the factors that make WSNs highly vulnerable to security attacks at various levels. Intrusion Detection Systems (IDSs) can play an important role in detecting and preventing security attacks. This paper presents current Intrusion Detection Systems and some open research problems related to WSN security. In order to achieve both a higher detection rate and a lower false positive rate of internal node intrusion detection in layer-cluster wireless sensor networks, an intrusion detection scheme based on the use of both a multi-agent system and a node trust value is proposed. In this scheme, the multi-agent model framework is established in both the cluster heads and the ordinary sensor nodes to perform intrusion detection. First, various, typical node trust attributes are defined and Mahalanob is distance theory is used to judge whether these attributes are normal. Second, the node trust value is calculated and updated based on the combination of the Beta distribution and a tolerance factor. Finally, node intrusion detection is realized. Simulation results demonstrate that the modified scheme has a higher detection rate and a lower false positive rate, even when several types of intrusions are present.

When a borrower's financial situation deteriorates to the point where an asset's value is diminished, the financial institution taking on the loan runs the risk of incurring losses. This study seeks to illuminate the fundamentals of a “credit risk management” (CRM) system for commercial banks (CBs) in an under developed country. with the ultimate goal of reducing the difficulties caused by delinquent borrowers' loan obligations. The primary data used for this came from in-depth interviews with CBs and bank’s management officials responsible for “credit management”, while the secondary data came from a slew of relevant documents. Credit risk can be controlled and reduced, as demonstrated by the investigation, so long as strong strategic approaches are adopted and followed. In light of this, it follows that a bank's strategy is crucial to the success of a CRM system.

The integration of markets at the international level brings with it the institutional transformation to adapt to new challenges and challenges resulting from the interaction between different economic agents worldwide. Education is an institution that is significantly affected by this global outpost. Economic liberalization and integration accelerated dramatically in the 1990s and generated significant impacts in political, economic, social, and cultural spheres. Thus, education acquires a strategic role in different countries by recognizing the importance of promoting academic instruction, which generates rational attitudes to promote economic thought in accordance with technological advances. It is not only about the homologation and educational adaptation between educational systems of the countries, but also the vision of the world that the new generations will have in the face of challenges in all areas of human action. It is a change in the face of new global needs that began to be glimpsed from the beginning of the sixties with the Keynesian economic current, which exposed that the growth of a country was originated to a great extent by the development of human capital (Becker, 1993) . , and later by the vision of technological development (motivated by research) according to the growth models of Solow (1957) . Regarding basic education, its priority for the economic and social development of a country is recognized in the understanding that education presents a rate of return that affects reducing inequalities from one generation to another (Becker, 2002) . In addition, from Durkheim 's (1991) functionalist perspective, the function of education is seen as the transmission of values from one generation to another, which does not occur only on the intellectual level, but also in the daily life from one ancestor to another successor.

India is the largest democracy with an amazing diversity in its population which is more than 3 million. India is having largest education system from ancient time as we can proudly say that taxila, nalanda were the part of indian education system. The population of india is growing and with this growing economy we have to set some standards for education because an educated society can accelerate the growth of any economy . Government of India has taken much initiative regarding e- learning because we have to change according to the changing technological environment. E-learning is the need of today’s world as no one should be away from education just because of geographical area or distance. Right to education given education a fundamental right of any Indian child of age group 6to 14.In this paper we have discussed different e learning initiatives taken by Indian government.

In recent years, recognizing human athletic actions has become an increasingly significant part of the analysis. FPGA-based recognition has been developed to precisely predict human actions for future training. To ensure the viability of such long-term athletics training monitoring systems, it is necessary to have intelligent surroundings in which human athletics actions may be automatically identified. This Artificial Neural Network (ANN) based Field-Programmable Gate Array (FPGA) is implemented in the system described here. The data acquired from a smart environment may be utilized for its machine learning algorithms to infer human behavior; however, these methods must first be trained on annotated datasets. Due to the required time and labor, the cost of creating data sets such as records and annotations is high. In addition, the difficulty of human athletics enables these athletes to precisely imitate more demanding forms of the sport. Hierarchical models have the potential to reflect relief more precisely, but determining the appropriate amount of complexity may be a challenging endeavour. Last but not least, to roll out an automated human behaviour monitoring system all over the globe, we need a model behaviour that can be applied to each new house and then fine-tuned so that it properly matches the actions of the people who live in that specific home. Instead, the technique is most beneficial for decreasing the need for annotations and maximizing the utility of annotations to acquire annotations when applied to a dataset that comprises weeks of data. This is because the approach can maximize the usefulness of annotations.

Because of the limits of single-path routing, the Multipath Routing (MR) approach is widely used in Mobile Ad-hoc Networks (MANET). In MANET, however, achieving energy-effective secure MR is a huge problem due to the need for trusted centralized experts and also limited resources. However, such systems have difficulties due to constraints such as packet loss rate. In this research, a low-power routing protocol that takes its cues from the well-known AOMDV protocol and a bio-inspired procedure, which is named as the Adapted Gear and Steering-based Rider Optimization Procedure (AGS-ROA) has been presented. Paths are found from the class of the fittest nodes with adequate energy for broadcast, lowering the chance of route failure and the rising sum of dead nodes brought on by larger data loads in the proposed AGS-ROA-AOMDV. After each transmission cycle, the AGS-ROA updating operator examines the nodes based on their remaining energy and changes the classes accordingly. Since gateways were chosen based on the total number of nodes, the EC was relatively high. So, a Fuzzy-based Clustering Algorithm (FCA) is suggested here. With this method, FCA is carried out using the Weighted Clustering Algorithm (WCA). Additionally, the FCA method estimates the total weight value using a number of characteristics (node degree, EC, latency, etc.) that are also used to assess the clustering quality.

The modern world is running forward in achieving the challenge of minimum area. So many efficient structures are employed in the traditional designs so that the max speed can be included. Most of the structures will have multiplier as the basic blocks , which will run with less speed because of its huge structure . in practical, not all applications need accurate results such as in image processing and digital signal processing etc. so approximate multipliers are employed. by considering these two points a scalable approximate multiplier, called truncation-and rounding-based scalable approximate multiplier (tosam)is presented, which reduces the number of partial products by truncating each of the input operands based on their leading one-bit position. In the proposed design, multiplication is performed by shift, add, and small fixed-width multiplication operations resulting in large improvements in the speed compared to those of the conventional multiplier. To improve the total accuracy, input operands of the multiplication part are rounded to the nearest odd number. Because input operands are truncated based on their leading one-bit positions, the accuracy becomes weakly dependent on the width of the input operands and the multiplier becomes scalable. higher improvements in design parameter (less area )is observed.

An organisation is more susceptible to a malicious insider threat. Because a malicious insider can have a significant negative impact on the data loss, it is essential to find them. Malicious insider threats are rare but extremely devastating. The primary goal of this research is to, identify the internal danger posed by hostile individuals in a business. Detecting malicious insider threats in cloud computing environment making use of Deep Convolutional Neural Network. Malicious insider threats are very hazardous to every cloud based organization. Detecting such threat is essential for ensuring security of an organization. Malicious insider threat incidents cause major damages to any organization. Earlier existing methods are lack in real time detection of threats. In this research work, Deep Convolutional Neural Network (DCNN) is utilized for enhancing security in cloud environment by combating against malicious insider threats and anomalies. It involves in the process of malicious insider threat prediction as well as classification malicious activity from non malicious activities. Anomaly score and threshold values are utilized for classifying threats from the legitimate environment.

The magnetic oxide thin films of composition Ni0.3Cu0.7CoxCrFeO4 (0.0x1) are synthesized by SILAR method and sintered. Structural properties like X-ray diffraction, cation distribution and Infrared spectra studied. Dielectric properties such as dielectric constant, and dielectric loss factor are studied. Magnetic properties were studied with MOKE. It is observed that the thin films are spinal structured. The lattice constant increases with increase of Co.

Analog-to-digital converter (ADC) based on CMOS Flash and Threshold Inverter Quantization (TIQ) for system-on-chip (SoC) applications. TIQ technology uses two cascaded CMOS inverters as voltage comparators. However, this TIQ technology must evolve with the latest SoC trends, which require the ADC to be integrated on-chip with other digital circuits, with a focus on low-power, low-voltage implementations. TIQ comparators minimize the effects of process, temperature and supply voltage fluctuations. The TIQ Flash ADC achieves has a higher speed and resolution. TIQ Flash reduces or controls ADC power consumption. You can save a lot of energy by controlling the power consumption of the comparator. The new comparator also has great advantages over TIQ comparators in reducing power consumption and noise. The results demonstrated that the modified multi-base TIQ flash ADC operates with small size, reduced power consumption, and lower voltage than existing flash ADCs while achieving quick conversion. The proposed method presents a new ICG based design methodology that also reduces dynamic power in the power domain.

Food security refers to availability and accessibility of adequate food and nutrition to all people at all time. It is regarded as the backbone of national security and well-being. The need for achieving food security is felt significantly in India in recent years due to the pressure from increasing population. Food availability, accessibility, stability and utilization are necessary conditions for food security. India has achieved self-sufficiency in food grain production. But it is yet to achieve food security as a large proportion of its population (21.9% in 2011-12) are below poverty line. Hence, this paper attempts to assess food security situation in India, trend in Monthly Per Capita Expenditure (MPCE) and identify challenges to food security and suggest policy implications. The study is mainly based on secondary data. The data were collected from the reports of various rounds of National Sample Survey Organisation (NSSO). It compares the Monthly Per Capita Expenditure (MPCE) and distribution of households and persons over the MPCE range separately for the rural and urban sectors of the country. The data were analysed using percentage, ratio and descriptive statistical methods. The study found that there were a huge difference in the monthly per capita consumption expenditure and consumption pattern in rural and urban areas. Further, it was found that the expenditure share of most of the food item groups in total consumption is higher in rural than in urban India. But in case of non-food items,the expenditure share is higher in urban India.Over the 18 year period from 1993-94 to 2011-12,cereals have the largest decline in expenditure share from 24% to 12% in rural India and 14% to 7% in urban India.On the other hand, expenditure share of durables,fuel and light,miscellaneous goods and services showed a rising trend.Alongwith these, the paper tries to draw the attention towards various challenges for achieving food security in India and their possible solutions.

Many educational technologies and methods are widely used today. The main goal is to achieve educational efficiency and develop high knowledge and competencies of students. The main goal of project-oriented educational technologies is that learners perform practical activities and directly apply their knowledge.

The World Health Organization (WHO) statistics say that the population of countries is ageing fast. The fact that the mortality rate of people is going up, means there are lot more people who might need constant health monitoring. The best remedy for this is to set up an E-Healthcare system that helps monitor their health regularly. Social-IoT can be an enabler in giving effective and efficient services to the people who need them. The fact that Social-IoT is used, the trust between each device will play a crucial role in determining whether the services are coming from a reliable function. The gadgets in a system connected to the internet need to work autonomously without any human intervention and have to recognize and authenticate each other to ensure the integrity of their exchanged data, which promises legitimate data and services. After the authentication is successful, the devices need to decide which data should be sent to whom based on the relationships they have with the other devices. To achieve the same, we propose the security advantages provided by the Blockchain, which will be used to identify and trust, and the Social-IoT to decide the relationship establishment and management.

INTRODUCTION: Globalization made every industry to go digital without exception. This revolution had a significant impact on the banking and financial services sectors. The banking system has a significant role in a country's financial stability, economic growth, and ability to diversify its sources of investment capital. This development offers customers services like Net banking, mobile banking, and block chain, among others; the development of technology will continue in the future as well. RESEARCH METHODOLOGY: The analysis were based on primary data from 68 digitalised banking service users. Descriptive analysis like mean and percentage were used. Inferential analysis like correlation is used to identify the relationship between the factors of influencing digital banking users' and customer satisfaction. ANALYSIS AND INTERPRETATION: Majority of the respondents are Male, between the age of 20- 30 years who are working under private sector, belonging to income group of Rs.50,000 to Rs.1,00,000. Factors like Accessibility and Relative advantage have positive and significant relationship with customer satisfaction of digitalized users. Digitalised bank need to concentrate more on accessibility without any hindrances in the digital world to attain higher satisfaction of digitalized users and to concentrate more on the relative advantage like cost effectiveness and time saving. CONCLUSION: The study revealed Accessibility and relative advantage has a positive relation with customer satisfaction, thus contributing towards better satisfaction among the digitalized customers. The anywhere anytime facility helped in attracting huge customers towards digitalized banking system. Despite the bank's many security precautions, the risk and threats need to be taken into account as the main disadvantage. To encourage clients to use the digitalized banking services, it must enhance its safety and security procedures.

In this scientific article, a study of the effect of solutions of a scaling and corrosion inhibitor of a complex action on the swelling of clay particles is given, which makes it possible to assess the possible negative impact on the reservoir characteristics of the reservoir.

The article discusses the developed methods for accounting for stochastic water consumption, as well as mathematical models for accounting for stochastic water consumption in water supply and distribution systems.

Evolutionary computation (EC) is a set of global optimization algorithms based on natural evolution. EC is a subfield of AI techniques and soft computing. EC techniques, which are inspired by principle of evolution and Darwinian theory. In this paper we present the following algorithms as Genetic algorithms, artificial bee colony algorithm and Particle swarm optimization of Evolutionary computation technique. Genetic algorithm (GA) is the simplest and well known optimization techniques, which imitates the natural process. Particle swarm optimization (PSO) algorithm is a bio-inspired search algorithm which provides an alternative solution to non-linear optimization problem. Artificial bee colony (ABC) is also a metaheuristic global optimization technique which simulates the foraging nature of honey bees. This paper presents a competitive study among EC algorithms for solution of Non-linear unconstraint optimization problem and solved numerical example of unconstrained optimization problems using software MATLAB.

One of the key factors that are responsible for the success of deep learning is the methods or group of methods. In the recent past, Autoencoder (AE) and Generative Adversarial Network (GANs) has grown vast popularity in deep learning community. Autoencoder (AE) and GAN is employed to generate images in various domains like as computer vision, semantic segmentation and medical field. In this paper we compare and implement the three autoencoders model simple autoencoder, vanilla autoencoder and convolutional autoencoder with different architecture. The first autoencoder, simple autoencoder with only bottleneck layer, second autoencoder is with one hidden layer and bottleneck layer and third autoencoder is the convolutional autoencoder. We use convolutional layers in convolutional autoencoder and in generative adversarial network, that is better to capture the spatial information in image rather than using one or more hidden layer as in simple autoencoder and vanilla autoencoder. We have generated the images using these autoencoders and compare the training loss, validation loss and accuracy.Image is also generated via Fully Connected Generative Adversarial Network (FCGAN) for a particular batch size.

Current study aims to determine the role of social media advertisement in influencing thebuying behaviour of youth. To meet the objectives of the current study, data was collected randomly from chosen college students (n=150) using survey method at West Delhi. A pre structured questionnaire has been used to collect data from the students in the age group 17-22. Findings reveal that liking towards social media advertisement has a greater impact on buying behaviour of youth.Results of the current study are beneficial for the marketers which intend to promote their products and services through social media channels.

Every person has a unique finger vein pattern existing within each finger. Unlike facial features or finger print, finger vein authentication systems aren't vulnerable to forgery. Finger vein authentication systems are more secure and reliable, and less expensive, than biometric security systems using finger print. This paper presents a novel security framework based on finger vein pattern. Finger vein pattern in used in id basedcryptography to generate the keys for data encryption. These keys are combined withgenerator of elliptic curve cryptography (ecc) to exchange thekeys using diffieHellman key exchange algorithm. Once the keys are exchanged, the data is encrypted using advance encryption standard (aes). This framework is tested in internet of things (iot) environment for enhancing the security. The iot based security systems implemented in the banks and other organizations can be enhanced considerably using the proposed security model.

This paper deals with the implementation of a low-cost Maximum Power Point Tracker (MPPT) solar charge controller to constantly calculate and maintain the maximum amount of power from a solar panel using a DC/DC buck converter and a microcontroller. The MPPT algorithm has been implemented using an Arduino Uno with the incremental conductance method. The voltage and current of the panel are taken to the system using voltage and current sensors to track maximum power point. When irradiance and temperature are constant or slowly vary, the incremental conductance method tracks MPP steadily and calculates the operating point at which the battery is capable of producing maximum power. In this method, the controller provides the PWM signal to adjust the duty cycle to finally adjust the voltage. Adjustment is done by Buck converter. If the power increases, further adjustments in that direction are tried until power no longer increases. More over the system protected the battery from under charge and over charge. To do that two MOSFET (IRF250) and two Optocoupler (PC817) has been used. The proto type of the system has been developed and its performance has been studied. Finally, a cost analysis has been done to show its cost effectiveness.

Surface runoff is an important component contributing significantly to the hydrological cycle, design of hydrological structures and morphology of the drainage system. Due to advancement in computational power and growing availability of geographical data, predicting the surface runoff has become more accurate. The Soil Conservation Service - Curve Number (SCS-CN, 1985) method is a versatile and universally accepted approach for quick and accurate runoff estimation. The advance application of Remote Sensing (RS) and Geographic Information System (GIS) techniques lends to estimate surface runoff based on different parameters. The study area, Mehsana district watershed area, is a water scarce semi arid region. The daily rainfall data was collected and used to forecast the daily runoff using SCS-CN method and GIS. IRS P6 LISS III satellite data was visually interpreted for the identification of different geomorphologic features and land uses in the study area. The present research is mainly intended to evaluate different rainfall-runoff characteristics on agricultural watershed area. The result indicates that the minimum and maximum value of yearly average runoff is 20 mm and 307 mm respectively. The analysis shows that the variation in runoff potential depends on various important properties of watershed i.e. land use, antecedent soil moisture conditions, soil permeability etc. The research reveals that the coarse textured soil of the study area is enhancing soil permeability, which generates less runoff. The developed rainfall-runoff model can be used to recognize the watershed and its runoff flow pattern integrating with remote sensing and GIS application.

The major source of any social media to improve their promotions or sales is to construct a strong network that has more influence nodes. There are cases where the network is big and but the influence of the nodes has very less influence. Our previous work has implemented the enhanced frog leaping algorithm which has the drawback in terms of execution time and affinity values with the increase of network. The proposed work in this paper utilizes an ensemble neural network that can handle high-dimensionality data at a faster rate. Since the influence maximization is a connected graph, to identify the similarities among the connected nodes, the statistical approaches fail because it has to compare n-1 pairs. The neural networks use the concept of weights and bias the model trains and extract the similarity features and classify the necessary nodes using the last layer of the neural network. Designing all the layers of the network as dense will help the model to reduce the execution time with more influence nodes. The best estimators for each layer are identified by applying the Cuckoo Search, genetic approach. Integration of genetic algorithms with neural networks has reduced the time to identify the influence nodes. It also reduces the memory utilization to store the connectivity nodes because the general tendency of the neural network is to connect the nodes with dot products between them. When compared to the genetic approach, the execution time of the proposed model is reduced by nearly 6%.

Unmanned Aerial Vehicles (UAVs) are utilized for a wide range of tasks, such as search, traffic monitoring, package delivery, military combat engagements and rescue operations. In each of these situations, the UAV is used to independently navigate the environment without human involvement, perform out particular tasks while avoiding obstacles, and collect data. To avoid the traffic for delivery service, some of them using the Unmanned Aerial Vehicle (UAV) to deliver the products. In collaborative control systems for unmanned aerial vehicles (UAVs), path planning is an important study field. UAV path planning is the process of looking for a route which will enable a UAV to follow a better flight path from its beginning point and eventually return at its destination point within the context of a particular mission. This flight path must fit the UAV's limitations while avoiding obstacles and aggressive threats. Thus, effective path planning is necessary to achieve traffic free delivery of services. Planning a practical three-dimensional (3-D) flight plan for unmanned aerial vehicles (UAVs) is a significant problem for following management and decision making. Hence in this work, Multi-Doped Pattern Learning (MDPL) Based Real Time 3D Path Planning of Unmanned Aerial Vehicles Using Integrated Probabilistic Pattern Modelling (IPPM) is presented. This approach will optimally find the correlation between the vertices from different sensor data and map with the pattern modelling to form 3D visual effect which helps to drive the UAV and prevent from accident by obstacles.

The purpose of this article is to present some of the most significant image processing technologies that are utilised in dental restoration, as well as to emphasise the relevance of image processing technology in dental restoration and its applicability. The following are examples of information sources: Using the search phrases "tooth colour," relevant English papers that were published between the years 1990 and 2021 were located using a computer-based internet search of HighWire biomedical full-text electronic resources on the internet. During this time, we searched for related Chinese publications using the keywords "computer aided design, tooth, digital" and "image processing, tooth" in the VIP database from 2001 to 2007 and the Wanfang database from 2000 to 2021, respectively. Both of these databases covered the years 2001 to 2007. Choices Made Regarding Research: Over two hundred and fifty articles were compiled, and from those, the top ten were selected. The first kind of research included written materials on tooth bleaching as well as the components and production process of baked porcelain teeth. The second type of research included basic investigations, case reports, or repeated studies. Only articles that discussed the following topics were read in their entirety: 1 the current state of tooth damage in China and the importance of dental restoration; 2 the acquisition of digital tooth images and preconditioning of tooth images; 3 the three-dimensional image reconstruction of general tooth films; 4 computer-aided colour matching technology; and 5 current tooth restoration products both in China and abroad. Extraction of Data In order to conduct this research, a total of twenty-six different pieces of literature on a variety of subjects, such as digital dental image capture and preconditioning, three-dimensional image reconstruction of general dental films, and computer assisted colour matching technology, were selected as samples. The Process of Information Synthesis: Digitized X-ray imaging technology is the current development trend in the field of dental diagnostics. In picture preconditioning, the most common ways are geometrical alteration and image enhancement. In dental restoration, additional essential approaches include three-dimensional image reconstruction and computer-assisted colour matching. According to the findings, the composites with particular monomers (Digital image processing, meth acrylamide, methacrylic acid, and 1-6 hexanediol methacrylate) for group (D) that were reinforced with silica nanoparticle have elevated flexural properties as compared with other types of the prepared nanocomposite, where these values reached to (144 MPa and 5.4 GPa) for flexural strength and flexural modulus respectively.

Glaucoma has become a critical problem in the healthcare industry in recent years. Although it is not life-threatening at all times, it is possible for glaucoma to cause permanent blindness. Glaucoma rarely leads to blindness. But once the vision is lost, it is irreversible. Hence, it is essential to diagnose the condition in its early stages in order to minimize the patient’s risk factors. The major goal is to conduct a comparative examination of the accuracy of deep learning models in glaucoma detection. Deep learning is a subcategory of machine learning, which is essentially a neural network with three or more layers. Deep learning models are extensively used in the field of medicine to make diagnoses and prognoses. This work employs the Resnet50, which has an accuracy of 90% when trained and tested with fundus images. ResNet50 outperformed the CNN algorithm. Therefore, the proposed system is believed to provide an effective model that detects glaucoma.

Because of the advances in vehicle and infrastructure communication technology, vehicular ad hoc networks (VANETs) have emerged. The VANET has the potential to greatly enhance road safety if drivers are allowed to report traffic accidents and violations via vehicles. We proposed a CA-IDS framework for Intrusion Detection systems (IDS) in vehicular ad-hoc networks (VANETs). The collaborative architecture (CA) has been created with Markov Reputation (MR) based and enhanced Ring Signature algorithm (RS). The intrusion detection has been done with the Reputation Mixture Transition Circulation algorithm (RMTC). The proposed method optimizes offloading tasks based on the proposed architecture. The simulation results obtained show that CA-IDS performs better than the other existing schemes concerning parameters such as packet delivery ratio (PDR), Delay, overhead, and end-to-end delay.

Communication is vital for transfer or sharing of knowledge between the individuals. Before the invention of telephone and other technological communication device the traditional methods of communication used to be radio or direct meeting of the individual. Later on telephone, mobiles and other devices came to play to minimize the burden to reach out the individual at the same time security became an issue as the channel used by the mobiles and other devices can be tampered so security for the communication also became an factor that is to be kept in mind.There are several methods and attacks that an attacker uses on the network that is used for communication to disturb or alter the communication between the sender and the receiver. The network that we are going to discuss is Mobile Ad-hoc Networks (MANET). The attack that is simulated is Gray Hole attack. Gray hole attack is high level transformation of general black hole in networks that are performed on Wireless Sensor Network (WSN).

Soil is the upper part of the ground which is affected by organisms, water, wind, and climate, and thus, is to some extent continuously transformed. The soil can also be regarded as the part of the ground used by plant roots, and thus, constitutes the basis for plant growth. In Mother Nature, we see many different shapes that to a large extent depend on the soil properties. Changing the physical and chemical properties of a soil might have a negative influence on the environment, mainly in the flora, fauna, and trees. Furthermore, increasing demands for more gentle techniques and technologies with less negative impact on the environment ask for development and implementation of new processes and new machine solutions. The increasing interest in developing agriculture management approaches that are based on gentleness to the environment requires better understanding of the interaction between the off-road heavy vehicle and the terrain in the working process.

Unit Commitment Problem (UCP) deals with the scheduling of the power-generating units to fulfill the load demand at the lowest possible cost. UCP is a nonlinear, mixed integer optimization problem solved in a constrained environment. The prime objective of the traditional UCP is to minimize the total operating cost of power generation units. The integration of the wind power generating units with thermal units has made the unit commitment more complex to solve in a constrained environment. The present work investigates the effect of wind power generation on the operating cost of thermal units using a Multipartite Adaptive binary Real Quantum-Inspired Evolutionary Algorithm (MABRQIEA). A repair-based constraint handling is also used in the suggested work to deal with the constraints of the power-generating units and the system. The suggested MABRQIEA is tested on different test systems along with wind power generation and the result shows the effectiveness of the algorithm. The test systems include the 10, 20, 40, 80, and 100-unit systems. The results obtained are found to be competitive as compared with various well-known optimization techniques.

The requirement for aluminium (Al) metal matrix composites is a result of composites with advanced mechanical and wear properties and applications (MMCs).Metal matrix composites (MMCs) made of aluminium (Al) are currently thought to have the greatest strength for use in structural and functional applications. Aerospace, automotive, thermal management, and the military all use composite materials using aluminium matrices. Applications for advantageous properties have increased due to low cost. Al is reinforced with various metallic, non-metallic, and ceramic reinforcements to provide the desired physical and mechanical qualities, such as high hardness, high strength, high stiffness, and high wear, abrasion, and corrosion resistance. Wear plays a significant part in the production of pistons, connecting rods, engine cylinders, disc brakes, and drum brakes. Here, wear plays a significant influence in how well these components work since excessive wear of the mating components might occasionally result in catastrophic failures. Utilizing specific reinforcement materials like Flyash, Zirconia, Al2O3, Graphite and SiC allowed hybrid composites to have improved mechanical, particularly tribological, capabilities. Therefore, a study of aluminium metal matrix composites (MMCs) reinforced with various particles as reinforcements is presented in this paper, with an emphasis on how these reinforcements affect the MMCs' physical, mechanical, and wear behaviour.

Biometrics is a mechanization which is an emerging breakthrough in case of security to identify a person’s unique personality. It is utilized to determine a person’s uniqueness by means of his/her materialistic attribute such as finger print, retina, facial features, iris, dental traits etc. Other features which can be considered as biometric are external pattern of the ear, hand veins, gait, keypad touch, voice recognition etc. Additionally, biometrics is also considered as a major authentication in enterprise security system. This scientific knowledge is predominantly used for surveillance purposes. There is discrete category of biometrics such as Biological Biometrics, Morphological Biometrics and Behavioral Biometrics. Biometrics is considered to give us immense accuracy rate, however the task is extremely crucial taking identical twins into consideration. The Research Literature is on identification of Monozygotic twins by considering their biometrics of facial features and external ear pattern by using the hybrid model of RNN (Recurrent Neural Network) classification, CNN (Convolutional Neural Network), PSO (Particle Swarm Optimization) and MSVM (Multiclass Support Vector Machine).The features of the face i.e., nose, mouth and eyes are studied for analyzing the two identical twin images. This paper pulls together the literature review in the distinct field, implements with the assembled datasets and intimates’ feasibility for the forthcoming research study.

Controlling the speed of Brushless DC (BLDC) motor is one of the demanding and essential research topic in ancient times. Because, the BLDC motor has been increasingly used in many application domains due to its significant advantages and features. The main intention of this paper is to implement an advanced DC-DC converter and controlling topologies for controlling the speed of BLDC motor with increased efficiency. Here, the performance of resonant DC-DC converter is validated and tested with two distinct switches such as Si and SiC MOSFET for providing an appropriate voltage support to power the motor. In this work, the DSP is separately programmed to transmit the appropriate pulses to the MOSFET switches of converters. Then, the effectiveness and performance of both Si and SiC switches used in the resonant converters are validated in terms of output parameters, efficiency, and speed of motor. Due to the tiny stator inductance, the high speed BLDC motor has an increased harmonic distortions in the winding currents, which increases the motor losses. Hence, the proposed work objects to increase the switching frequency of Pulse Width Modulation (PWM) by using the sine wave filter, where the SiC MOSFET converter is used to obtain high voltage gain. During experimentation, the microcontroller has been designed and optimized by using the SiC MOSFET driving circuit. Moreover, the performance of both SiC MOSFET and Si IGBT are validated and compared by using various evaluation indicators. The obtained results indicate that the SiC MOSFET used in the resonant converters provide an increased switching frequency and controlling speed.

In this paper we have designed boat for cleaning rubbish floating on the water surface. A cost-effective solution, as well as robustness and durability, are three crucial factors to consider while building aquatic vessels. We developed the vehicle construction to give high stability, good maneuverability, and easy trash collection due to the nature of the cleaning work. Flowing in between. A pontoon shaped hull works best for this case and fulfills all the hydrostatic, structural stability criteria This boat is a battery operated boat and is charged using solar panel. Solar energy is one such renewable resource, which is harnessed by nature through the photosynthesis process.. This project is conducted to help clean the environment, specifically swimming pools, lakes, rivers, etc using renewable energy, the sun, as a more efficient power source.

Floods have destroyed infrastructure worth millions of dollars in the past, and this is still an issue today. Despite all of the research, there is currently no single, worldwide system that can be used to gather data, store it, analyze it, and predict floods. Researchers all across the world are attempting to create a solution that will allow them to collect, store, and analyze massive volumes of flood data in order to forecast the outcomes of flood-based prediction systems. This study created a water and disaster management system based on the Internet of Things, which used a deep learning model and a hybrid categorization technique. First, the input data is drawn from a vast collection of data called as the flood big data set. The system was built using four Internet of Things sensors: the Water Flow (WF) sensor, the Water Level sensor (WL), the Rain Sensor (RS), and the Humidity sensor (HS). Following that, HDFS map-reduce is utilized to reduce the quantity of redundant data in the IoT sensed data. Following the removal of repetitive data, the data is pre-processed using missing value imputation and a normalizing algorithm. As a consequence, a rule is created that makes use of a mix of attributes and attributes approach. At the last step of the classification process, a hybrid classifier that blends Convolution Deep Neural Network (CDNN) and Artificial Neural Network (ANN) classifiers categorizes the rules as a) probabilities of a flood occurring and b) odds of no flooding occurring. Several criteria, including sensitivity, specificity, accuracy, precision, recall, and F-score, are used to compare the results of the proposed approach. Furthermore, when compared to current algorithms, the suggested technique yields considerably more accurate findings.

Brain Electrical Oscillation Signature Profiling (BEOSP), also referred to as Brain fingerprinting developed by Dr. Farwell in 1991, is a neuro-psychological method used for studying the response of the Brain to the presence or absence of a piece of information. It is based on the detection of P300 or P300 MERMER response elicited on encountering a stimulus previously stored in the brain as a form of memory. It is a noninvasive, unbiased and efficient method of interrogation of suspects and militants. It is a variant of guilty knowledge testing. It results in the form of information present or information absent which is then used to determine if the person is guilty or not. Unlike polygraph testing it is not based on autonomous nervous system (ANS) response thus avoiding emotional manipulations and outbursts. It is solely computer based technique performed using a digital electroencephalogram that measures the presence or absence of electrical brain waves in the brain through electrodes placed in the scalp region of the forehead. It has shown 100% accuracy in various tests conducted by Dr. Farwell in 2012. Though there has been criticism on the methods of Farwell but till date there is no such countermeasure or denial of the technique. Hence it is one of the most promising techniques in the field of neuroscience and criminology. Besides, we can make it more effective and widespread by performing more independent tests and making the technique cost effective and available to all.

Solar energy is receiving wide attention around the world due to its importance among renewable energy sources. But rising ambient temperature has a negative effect on the performance of solar cells, as it leads to a decrease in output power production and harms the life of the cells. In this work, drinking water delivered to homes was used to cool solar cells. The experiment will be conducted in the summer of 2020, the sixth month. It was noted that the temperature of the solar panels increased to double the ambient temperature, and this increase in temperature led to a deterioration in the performance of the solar panels. After using the cooling system, (85%) of the lost energy due to the increase in temperature was recovered. The temperature of the solar panels dropped to 27 oC from 68 oC before cooling. Resulting in the rise in voltage and output power from (17.42 V ), (88.55W ) to (23.36V ), (110.92 ) respectively.

Agriculture is India's most essential activity since it serves people's food demands and the raw material needs of countless enterprises. Innovative agricultural approaches gradually increase crop output, increase farm profitability, and decrease irrigation waste. A dependable remote monitoring solution is essential. Two goals are covered in this study. First, a survey on IOT based agricultural model and its algorithms. Second, a comparative analysis of various authors proposed solutions with accuracy. This review paper is a machine learning-based IOT agricultural data analytics. In agriculture plant is heavily influenced by three factors: moisture, temperature, and relative humidity. An IoT device with cloud capabilities is created from data gathered by sensors deployed in a farm's field and sent to a microprocessor or Arduino. The DT approach is a powerful machine-learning tool for predicting outcomes from field-sense data. Farmers are provided the findings of the decision tree algorithm to assist further decisions in agriculture.

Alzheimer's disease (AD) is a complicated, irreversible, incurable neurological illness that has a positive global impact on human existence. It was the sixth killer in the United States, and no immunizations were available. According to research, this fatal condition is incurable. The progression of the disease might be delayed by improving the patient's quality of life, consequently enhancing the patient's cognitive abilities. So we proposed EAD-HNN (Enhanced Alzheimer's disease Detection using hybrid neural networks) approach typically, an MR image of the brain is used to diagnose Alzheimer's. In this proposed system Noise removal using MLP with Histogram equalization, the segmentation has done with Edge based with Robert operator, the training has carried with CNN and RESNet50. The classification has done with CNN Algorithm. This enables the use of picture recognition techniques in many ways to promote and improve diagnosis. Automatic identification of any illness sample saves physicians time and improves their accuracy. This article, which discusses the functional criteria for the clinical diagnosis of Alzheimer's, proposes a weighted combination of positive and negative samples and a technique for learning a limited number of pieces to enhance the data set system. It builds a Deep learning model that gives enhanced image details and improves the model's generalization capabilities

Medical assistance as a form of automated software that uses computer technology and machine learning methods. Recently it is being developed to help detect cardiac illness at an early stage. The risk of mortality may be reduced if any heart-related sickness is discovered at an early stage. Medical data may be analysed using a variety of machine learning (ML) approaches. The sheer amount and complexity of healthcare data makes it difficult to make sense of it. ML algorithms are able to process large amounts of data and extract relevant information from it. Algorithms for machine learning make predictions based on historical data and learn from it. Using a machine learning framework like this to predict the onset of coronary heart disease may motivate cardiologists to act more quickly, allowing more patients to get life-saving medications more quickly. This paper mainly explores the heart prediction through ML learning algorithms and its mathematical analysis. First this research illustrated the cardio disease in theoretical manner and apply the few selective machine learning for the implementation of the cardio data (Taken from UCI Library) in MATLAB platform. This paper found that the MLP is the best predictor algorithm and suitable for cardio data analysis and modelling.

Image Enhancement is a technique which is widely used in the field of Image Processing and Pattern Recognition. Image Enhancement is the processing of an image to enhance certain feature of an image in order to obtain a clearer image. The image details can be more clearly visible when the noise and other artifacts from the image are minimized. In the presence of noise in the original image, image processors are faced with a variety of obstacles. They must address these issues while keeping the image quality while simultaneously improving its contrast and noise. In this paper, we addressed various contrast enhancement models that involves histogram equalization methods to enhance the images obtained from lossless compression scheme. The proposed analysis method is tested on various test images to show the effectiveness of each method on various imaging techniques. The results of simulation shows that the proposed analysis offers better perception for the researchers to choose what type of enhancement fits the image enhancement process.

The article improves the marketing management system for the range of products in business entities on the basis of the organization of departments of product planning, product creation, product development and sales support, which characterizes the demand management

The technological process of the production of a jacquard ribbon on a weaving machine using the mathematical method of rotatable planning of a second-order experiment has been investigated. The geometric interpretation of the mathematical model is studied using slices. The optimal technological parameters of the jacquard ribbon production have been determined. The level of breakage of the warp yarns is 0.05 breaks per 1 m of fabric with the filling tension of the warp -20 cN, the size of the overlap -15 mm. and the position of the rock is 25 mm above the breast.

The article examined and systematized the factors influencing the working conditions of builders working at height, also studied and defined the main working poses of high-rise builders, performed ergonomic analysis to ensure dynamic compliance of working clothes of high-rise builders

Chemical yarn and fiber are the main raw materials of the textile industry. The launch of chemical fiber production in Uzbekistan will increase the range of fabrics, knitwear, non-woven fabrics. This article is devoted to the study of the possibility of obtaining fiber from different mixtures of primary and domestic polyethylene terephthalate granules purchased from abroad, by dyeing and reducing its specific surface electroplastic resistance under laboratory conditions. The possibility of dyeing the fibers in the mass with a “masterbach” and in solution with a disperse dye – all those possibilities have been thoroughly studied in the present article. It is shown that the coloristic performance of the color formed in the fiber depends on the dyeing method used. Dyeing of polyethylene terephthalate (PET) fiber in solution and application of antistatic coating in a two-bath method reduced the specific surface electrification resistance of the fiber from 2.8 ∙ 1010 Ohm to 2.6 ∙ 106 Ohm. A mixture of different PET granules was proposed to form a fiber suitable for textile yarn in a dry method in an experimental laboratory device and dye it in solution and give an antistatic make-up bath.

The main purpose of workwear is to provide reliable protection of the human body from various production factors while maintaining a normal functional state and working capacity. When making special clothing intended for use in a dry, hot climate like Uzbekistan, with a significant change in environmental conditions, on the one hand, and with a wide change in the thermal and moisture-physical properties of clothing materials, on the other, it is necessary to take into account all these factors. Workwear should not constrain the worker during the working process and meet the needs that arise when used for its intended purpose, and should also be comfortable. Comfort affects not only the well-being of the worker, but also his working capasity and efficiency. The article examines the ergonomic movements of workers in the development of workwear with high hygienic properties for workers in the automotive industry, and on this basis a new model of workwear was created. The recommended modes of processing workwear are given.

: The article presents the theoretical study of the passage of materials trash particles through the screening surface.It indicated that the small impurities content in seeds is relatively low and their selection takes place through small holes. Because of the low probability of passage of particles in a dense layer of the seed through the holes of the screening surface, the efficiency of separation of fine impurities can not be high.It is also pointed out that the efficiency of allocation of trash increases with the length of the screening surface. With the increase in diameter of the holes of the screening surface difference probability sieving particles of various lengths through the same hole decreases.It was noted that the fine waste in the seed is relatively small and its separation takes place in small holes. Since the probability of the waste passing through the holes of the waste discharger through the dense seedbed is relatively low, the waste removal efficiency will also not be high.Also the efficiency of separation of waste mixtures depends on the length of the mesh surface.As the diameter of the discharge surface holes increases, the difference in the probabilities that different lengths of waste will pass through the same hole decreases.

The article states that due to the low moisture content of fiber produced in ginneries, there is a need for additional humidification, and so far no effective wetting equipment has been developed to meet the demand. It is shown that the reason for the complexity of wetting cotton fiber is that it belongs to the type of non- wetting material, water condensate is in the form of droplets in the fiber and the bonding strength depends on the friction force at the point contact. It has been shown that in the transmission of wetted fiber in the air stream, the droplets on the surface of the fiber can separate under the influence of aerodynamic force and pass into the air, resulting in a decrease in wetting efficiency. The one-dimensional motion of the drop on the fiber surface along the horizontal axis, the conditions of separation from the fiber surface were theoretically analyzed. The laws of motion have been determined. The conditions under which a drop of water remains in the fiber or separates from it and passes into the air stream have been determined. The main factors influencing the separation of a water droplet from a fiber were identified as correlations between air flow rate, water droplet diameter, and mass. It was found that the air velocity, the diameter of the water drop, the amount of separation of water droplets increases with increasing mass, and the amount of separation decreases with decreasing diameter and mass of water droplets. Keywords. Water droplet, fiber wetting, wetting technology and equipment, water condensate, face resistance force, drop midel cutting surface, face resistance, lifting force coefficient.

The article deals with the issues of quality requirements and operation of treatment facilities and devices of enterprises of the pulp and paper industry. The possibilities of expanding the tasks solved from the position of automatic control on the basis of the achievements of the theory of automatic control, methods of mathematical modeling and optimization of technological processes have been studied. The scheme of wastewater treatment of a pulp and cardboard mill, which consists of a multitude of observed objects, the technological processes of control of which is carried out using multiparameter search systems, is presented. According to the approach proposed by the authors, to correct the estimates of the state of each object after receiving the next observation, are used not one, but all measurements of this observation, taken with certain weights.

The article discusses the technology of footwear production using textile materials. The analysis of the production of leather and footwear products in Uzbekistan was studied. The efforts being made in the country to expand the volume and range of output of finished export-oriented products based on deep processing of leather raw materials, as well as providing the population with high-quality and affordable footwear and leather goods of domestic production, were studied. The basic properties of textile materials were determined. Analyzes of samples of materials used for the production of footwear using textile materials were taken. Experimental studies have been carried out on the possibility of using the developed antibacterial fabrics as a lining for special footwear for military personnel. The teak-twill fabric was woven in the “Weaving” laboratory, the dyeing of the fabric was carried out with various compositions in the laboratory, and the teak-twill impregnation with experimental antibacterial compositions was carried out in the laboratory of the Tashkent Chemical-Technological Institute. The results of studies of the effect of antibacterial impregnation and dyeing of twill fabric on its physical, mechanical and hygienic properties made it possible to draw a conclusion about the possibility of using unpainted teak twill as materials for the main lining and insert insoles in special shoes. The concepts of breaking load, breaking elongation, abrasion resistance, impregnation resistance to abrasion, air permeability, surface density were defined. State standards for sampling and sample sizes for the experiment are given. It was concluded that the treatment of twill fabric with various antibacterial compounds does not affect the physical and mechanical properties of the fabric, but air permeability is significantly reduced. Research has led to the conclusion about the possibility of using unpainted twill, impregnated with antibacterial regulations as materials for the main lining and insole in special shoes.

This article is dedicated to identifying the characteristic anthropomorphological signs of older women, by which one can judge about the features of their physique, which affect the quality of clothing design. Anthropometric measurements were carried out, according to the results of which the body types of women were identified. The influence of physique traits on the shape and design of clothing details has been established.

Selective Catalytic Reduction (SCR) is an advanced active emissions control technologies that offers an economic and effective means of reducing Oxides of Nitrogen (NOx) emissions from flue gas by injecting a reductant agent through a special catalyst into the exhaust stream of flue gas. SCR is typically capable of removing 80 to 90 percent of NOx emissions from fossil fuel power plants, and is widely considered the most effective technology demonstrated up to date for this purpose. SCR technology is designed to permit nitrogen oxide (NOx) reduction reactions to take place in an oxidizing atmosphere. It is called "selective" because it reduces levels of NOx using ammonia as a reducing agent with in a catalyst system. The chemical reaction is known as "reduction" where the flue gas is the reducing agent that reacts with NOx to convert the pollutants into nitrogen, water and tiny amounts of CO2. SCR technology is one of the most cost-effective technologies available to reduce NOx emissions from power plants. All Power plants installed after 1st January, 2017 must meet the latest Environmental Protection Agency (EPA) emission standards. Out of all emissions, the oxide of nitrogen (NOx) has to reduce less than 100 mg/Nm3 levels from 300 mg/ Nm3. In order to achieve emission norms set by Ministry of Environment and Forest (MOEF), Government of India for NOx level from the newer power plants, BHEL has decided to establish a SCR based in-house test facility which can handle higher ash content of Indian coals. The objective of this project was to establish the 20 litres capacity Selective Catalytic Reduction (SCR) test facility in Advanced Pressurized Fluidized Bed Gasification (APFBG) test facility and also evaluate the performance of 11.8mm pitch Honeycomb type catalyst for NOx reduction by operating APFBG test facility in combustion mode with high ash Indian coal. The Honeycomb type catalyst was tested with high dust conditions by varying the flue gas temperature from 300 Deg. C to 350 deg. C. The results showed that the SCR test facility with honeycomb type catalyst has been established successfully and also achieved very satisfactory performance with high dust conditions. The honeycomb catalysts was removing minimum of 67% and Maximum 80% NOx from flue gas with 30-52 grams/Nm3 dust concentration. Ammonia slip was measured in the range of 4 to 6 ppm and the ratio of ammonia (NH3) to oxides of nitrogen (NOx) was maintained as 1.0 for all experiments.

The flywheel assume a tremendous part in mechanical particular framework is massive, that can virtually paintings on the easy interest of mechanical strength framework.Simultaneously, modern-day innovation on strength capacity innovation stipulations are step by step excessive and the utilization of appealing levitation innovation likewise has made speedy flywheel strength capability be far and wide challenge and exploration Maintaining CAST IRON is most important vast. The flywheel expect a full-size part in mechanical precise framework is colossal, that could truely paintings on the easy interest of mechanical energy framework The collection of weight is started out in upgraded model diverged from base model as according to the need of underneath yield point stretch that is 700MPa and furthermore the collection of evacuation is impelled in superior version appeared differently in relation to base model that's lower than the6 mm as per the important.

The development of new software is always considered to be a high-risk effort with a high failure probability. The process of developing software is a dynamic activity. The process of developing software is a dynamic activity. During the lifecycle of a project's development, the requirements that the various stakeholders have continually changed. The software industry recognizes this change in requirements as a significant risk element that might occur during the system development process. Requirement It has been discovered that changes have a major impact on the overrun of both schedule and expense in software projects. Requirement Change is necessary to make the software more usable and to raise its economic value, but it may also be a key cause of project failure due to a lack of knowledge of the reasons for change and an inability or unwillingness to handle this requirement change properly. On the flip side, requirements changes are inevitable and must be efficiently managed till the customer reject the product for not meeting his expectations. The importance of prioritizing requirements is widely acknowledged in the area of requirement engineering. The main aim of requirements prioritizing is to identify the most vital requirements that can be applied and, Consequently, give the client the best value possible. Mostly prioritization of the given requirement is done after the elicitation phase. To improve the chances of a successful project development, each requirement change in the requirement should be taken on priority basis. This study uses a fuzzy approach to develop a requirement prioritizing technique from the standpoint of its four measures. This approach emphasizes to the requirement change request.

The aim of the present paper is to investigate the Riemannian manifolds which admit a concircular vector fields. Also investigate some special Finsler Spaces which are connecting functions of concircular vector fields.

Multimodal biometric systems have been widely used in various applications due to its ability to deal with a number of limitations of unimodal biometric systems, such as noise, non-univarsality, sensitivity and lack of invariant representations. The multimodal fusion allows to improve the results obtained by a single biometric characteristic and make the system more robust to noise and interference and more resistant to possible attacks. In many areas where personal identification is important, security is of great importance. Biometric or multi-biometric systems, which include the physiological and behavioral features of individuals, are more preferred because traditional methods are insufficient and cannot provide security. In the study, a new approach of multimodal biometric identification is proposed consisting of the Fingerprint and finger knuckle print (FKP). A hybrid feature extraction technique is utilized along with the gray level co-occurrence matrix (GLCM) and wavelet moments. Extracted features are classified by Random Forest classifier to obtain the simulation results in terms of precision, sensitivity, accuracy, specificity and F-Score.

For the purpose of generating a new ideology and corollary on Non-Moment problem, we take into account equation as the standard form of an equation representing itself as the generator of non-moment problem function. To make our result more accurate and widely considerable, we would like to introduce some other facts and finding coherent to the convergence of result and making our formula as a very common men discussion.

In India and other Asian nations, the prevalence of ulcerative colitis (UC) is rising. It is an idiopathic, chronic, inflammatory condition influenced by genes, the environment, and the immune system. Endoscopy and colonoscopy are used to support gastroenterologists' diagnoses. However, UC detection is a very challenging process. This study aims to use computational techniques to determine Ulcerative Colitis remission without the support of medical experts. Finding the optimal classifier for multiclass image classification issues is more difficult due to the high-level, in-depth properties of the images. An innovative pre-trained Inception V3 model is used in this case for better classification UC images into many categories. The major goal of this research is to automatically identify and effectively learn key aspects from the UC picture. In this research, diverse stages of the UC images are classfied. The proposed method is contrasted with current state-of-the-art procedures, and the results and analysis show that the research is quite successful by achieving a 95% accuracy rate.

Present experiment study focused on manufacturing and properties of Alkali Activated Coarse Aggregate (AACA) by method of pelletization. The manufacturing of AACA using Ground Granulated Blast Furnace slag (GGBS) and alkali activator solution. The sodium hydroxide and sodium silicate were alkali activator solutions. The concentration of sodium hydroxide maintained as six molarity and ratio of sodium silicate to sodium hydroxide was 2.5.AACA were manufactured using drum mixer by maintain a 45 degree angle and speed of the mixer was 20 rpm for 2.5,3,3.5,4 and 4.5 minutes. It is observed that the speed of mixer is also affects the properties of aggregate, especially loose bulk density and absorption of water. Specific gravity of AACA is between 2.6 -2.7, which is similar to Natural Aggregate. From SEM image show that the packing density for NA is higher to AACA may be the reason of low water absorption of NA

In the present scenario, performing the task of speech confirmation in relation to the care of speech cues makes this field attractive. There is a problematic effect to the variety of routine components used to achieve confirmation of the accuracy of any speech. HMMs have been used to improve and review inputs to provide this type of speech recognition, which has been brought about as a system for verifying the accuracy of speech inputs as well as developing more. This paper takes the input of speech signals and analyzes their performance in different platforms using HMM process. But many speech signals involve ambiguity, which can affect the continuity of uninterrupted parts. To overcome this type of irregularity, various researchers have proposed different types of techniques in the past, but this paper analyzes the performance on different platforms using HMM technology to improve the regularity of speech signals.

The easiest way to distinguish from person to person is the face. The personal characters of a person is used to identify individual from others, this technique is used in face recognition. There are two phases in recognizing the human face. First is the face detection and the next one is introduction. In the face detection and the processor runs a quick scan to identify the face. The next step involves identifying the person with the database and matching it. The features of the fees are unique for each person. These are merged weather database present. and developed. The popular methods that are currently used to develop the face recognition model are Eigenface method and Fisherface method. This is an exercise to identify the face and also the organs in the face, so that the taking attendance can be automated in the classrooms. This paper focused on the face detection system with the recognition of the phase using their techniques of image processing. To implement the paper MATLAB tool is used.

The scientists are even not cent percent successful for finding the ways to preserve our natural resources. So to minimize these problems and issues which we have been facing since hundreds of years, latest technologies and researches are required. For a fixed system, photo galvanic effect was noticed taking Safranine as photosensitizer along with the oxalic acid (reductant). There was a notification of current voltage correlation in dark as well as in light under forward and reverse biases. Number of variations were noticed which works for the production of electric energy, photo potential and photocurrent. The efficiency of this cell being investigated by assigning an outsourced burden to manage potential and current at the point where the origin of rays was displaced until the result was only half of the “power point” under the shade. It was concluded that considered cell is able to function at the 105.0 minutes power point even in the dark and its conversion efficiency is 1.23 percent. The “photo potential and photocurrent” being produced was found to be 1430.0 mV and 190.0 µA, respectively

The effect of induction hardening on the microstructure and hardness of Inconel 718 nickel alloy is investigated. The main phases of Inconel 718 are gamma prime γ’ face ordered Ni3 (Al, Ti) and gamma double prime γ’’ face ordered Ni3Nb.Comparative studies are done for received sample (NR), induction hardened at temperature 850◦C with oil quenched (IHT1) and induction hardened at temperature 1000◦C with oil quenched (IHT2). The samples that underwent induction hardening at 1000°C showed an increase in grain size and induction hardened at 850°C, SEM findings indicate that Nb is more segregated near grain boundaries. However, X-Ray diffraction studies indicated that there is an increase in hardness of induction hardened samples due to evolution of γ' and γ" precipitates.

Global warming is result of human related activities affecting environment at large. Since last few decades, environmental concern is growing rapidly and has drawn attention of researchers. Inventory management system is a vital activity of the society fulfilling needs of human life. Inventories are managed to maintain balance between demand an supply chain. Supply chian system is one of the factors releasing carbon emission which directly affects the environment by increasing green house gases (GHG) level. The storage system of inventory needs support of high preservation technologies to reduce deterioration rate of deteriorating items to enhance life of the product to maintain long period demnad-supply chain. Continuing with all activities vital for the needs of human life, one has to focus on the environmental issues which are more vital for the survival of human lives and in this direction a lot of work is being done in reducing the carbon emission. Motivated by many research papers related to environmental issues, this paper consideres two warehouse storage management to maintain balance between demand and supply considering hybrid market demand rate which is combination of selling price and frequency of advertiesment, the most important factors affecting demand of customers in the market. The paper also considers to deal with uncertainity of various cost factors affecting average inventory cost. Shortages during stockout period are included and are partially backlogged at constant rate which is a fraction of total demand. The focus is arround the dvelopement of model towards sustainable environment by incorporating all activities related to carbon emission and also the cost incurred for reducing carbon emission. Optimality condtions are utilised in finding optimal solution to achieve goal and model is validated by numerical illustration showing concavity of profit function, which is another objective of paper together with environmental aspect.

As the Technology is raising and awareness is step forward in all domains and especially in medical science raised people precaution in all cases. Still in Gynecology we are facing issues with fatal birth cases. The primary issues is the stress bared by the fetal at the time of Labour. Cardiotocographic (CTG) monitor is a device which is used to recursively monitor the status of both the maternal pulse rate and fetal heart rate at the time of labour. In general due to lack of oxygen for the fetus which may be raised due to maternal uterus contraction. This may lead to abnormal issues and sometime raises to the risky case of the labour or sometimes lead to death as well for both the lives. Hence, by using classification techniques the abnormality features are diagnosed with the help of Cardiotocography (CTG) signal. The former classification methods are difficult to process the non-stationary data from CTG and the dataset imbalance. Here we are introducing a novel methodology for Time-frequency (TF) features from the raw data of CTG. This study makes use of the following to evaluate fetal distress: 1) DT 2) SVM 3) RF 4) NN 5) GB We present in this paper a new algorithm which improves the accuracy to 95.7% which is higher than what was obtained in previous research.

Tourism is a dynamic industry that has helped several nations' economies in recent years. All factors of tourism business is depended heavily on the hotel sector. E-tourism assists user in making trip arrangements inline and provide accurate recommendations based on prior feedback from hotel stays. For this the component for E-tourism is that the tourism platforms that are online must work with hotel management. Because of covid-19 there is a sharp decrease in roaming ratio of the user for past few months. According to business talks this pandemic has affected all the factors of the travel industries, most importantly on hotel occupancy. In these troublesome situations, in order to maintain the company position and to attract users, travel and hotel platform management take users past preferences as considerations as this considerations have greater impact on comfortable stay. At present, the market trends explains that customer reviews is the prime factor for choosing a hotel. This article used to identify and recommend the proportions that helps more towards higher level of satisfaction to the customers in India top tourism hotels in city listed by survey by using regression techniques based on most recent tourism reviews and item-by-item collaborative filtering. The denouement of this article aims Indian hotel management is focusing more on the facets required to get finer reviews. According to the online travel platforms’ view point,this study advocates the contexts for the trip recommender systems to get information from customers.

Depression is a state of mind which causes simple sadness to suicidal tendencies so, dealing with such patients is important and time taking as long-time observations are required to analyze them. Our idea is to develop an application that acts as an observer and analyzer. It would be able to keep a patient under live observation analysis which generates time to time data of the patient and calculates them using suitable methods to assert the patient’s mental state based on emotions. This system would replace tedious work hours of doctors who observer their patients for hours to study them. Not only replacing, this would also suggest required action to be taken on the patient based on the levels of depression found in the patient

Many people are afflicted by pneumonia, a respiratory infection brought on by bacteria or viruses, especially in underdeveloped countries. In underdeveloped nations, where there are typically significant levels of pollution, unhygienic living conditions, traffic, and a lack of suitable medical facilities. Fluids flood the lung in a condition known as pleural effusion, which makes breathing difficult is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. However, examining chest X-rays is a difficult task that is vulnerable to subjectivity. In this study, we created a computer-aided diagnosis system that uses chest X-ray pictures to automatically detect pneumonia. We have used VGG16 model in deep learning. To enhance user-friendly, we have designed a web page that displays the results that whether the person is effected by Pneumonia or not.

The enormous potential for medical, military and defence, environmental, industrial, infrastructure protection, and commercial applications of wireless sensor networks has drawn a lot of attention in research and development. These applications include the ability for the sensors to interact with one another under remote control. Wide-ranging uses for a Wireless Sensor Network (WSN) include tracking communication targets and environmental monitoring. The wireless ports on the sensor nodes allow for communication between the nodes and another network. Security is a major issue in wireless sensor networks due to numerous limitations. The sensor nodes are open targets for numerous attacks when left unattended in a communication context. The dataset is analysed using the supervised machine learning technique (SMLT), which may capture a variety of data, including variable detection, univariate analysis, bivariate analysis, and multivariate analysis, as well as treatments for missing values. In order to discover which machine learning algorithm is the most effective at foretelling the types of WSN attacks, comparative studies of the algorithms have been conducted. The outcomes demonstrate that the suggested machine learning algorithm technique's efficacy may be compared to the highest levels of accuracy, precision, recall, F1 Score, sensitivity, and specificity.

Even though wireless sensor networks (WSNs) have been employed for more than a decade, it is currently widely utilised by a variety of contemporary applications, including medical observation, disaster management, and environmental monitoring. In addition to the low channel bandwidth, this form of network also has limited energy and a short lifespan. Due to the significant influence of communication costs on the power consumption of nodes, bandwidth poses the greatest hurdles to such systems. Clustering has shown to be one of the most effective strategies for conserving energy in WSNs. We proposed EEC-NP method to enhanced energy efficient head election protocol in WSN. The EEC-NP method has used Enhanced Non-Deterministic Polynomial krill herd (NP-KH), cluster header selection and waking latent algorithm. Simulation studies indicated that EEC-NP is capable of extending the period between the death of the first node in a sensor field, hence increasing the system's longevity and stability in comparison to previous protocols.

An M/G/1 reneging and balking feedback retrial queueing system with finite working vacation is examined. The server under this approach delivers three kinds of services. The arrival is determined by the poisson process. If the server is busy at the arrival epoch, he joins the orbit. If the server is available, one of the incoming clients begins service immediately, and the others joins orbit. The ES is provided by the server to all incoming customers. Following the ES, clients can pick between two forms of SOS or abandon the system. If a consumer is unsatisfied with the service after completing two kinds ES or SOS, he can instantly re-join the orbit as a feedback customer in order to receive another normal service with probability r. Otherwise, he may be forced to leave the system with probability(1-r). Soon after the system has been decommissioned, the server may either wait idle for a customer or take a limited working vacation. Following that, we consider balking to indicate that the server is busy or on vacation, as well as reneging to indicate that the server is on vacation. The carrier and excursion durations both follow favoured distribution. For this model, the SVT was utilised to calculate the PGF of the number of consumers in the line. Some generic display measures can be deduced. A specific case is being researched. Similarly, mathematical research is presented.

Statistical Process Control (SPC) is an effective tool for achieving process stability. Various control charts have been developed for monitoring uncorrelated observations to detect shifts in the mean of the processes when the quality of interest surveys an ND. In practice, it is only sometimes valid that the variable of interest follows the ND but may also follow non-ND. The variable of interest may have a non-ND, such as an exponential or gamma distribution, or any other. Control charts created for an ND may not be applicable in this case, increasing in the proportion of non-conforming items. In this paper, the Length Biased techniques have been used to the ETED to the applications of SPC to check the shows of the production process. The main objective of this paper is to introduce a control chart using Length Biased ETED to study the production system and monitor the same.

A stock market, often known as an exchange for shares, is one of the best places for businesses to raise capital. Stock investing serves as an asset for the future financial advantages of an individual or other company. Because of the numerous variables that go into stock market forecasting, including politics, interest rates, economic growth, and a host of other elements, it can be very difficult to create an accurate prediction. The true prediction of the share provides a huge opportunity for profit and serves as an incentive for this field's research and analysis. There are two main approaches to stock market forecasting or analysis. The technical analysis comes second, followed by the fundamental analysis. The primary focus of fundamental analysis is on textual data, such as earnings reports and financial news. However, technical analysis uses historical data to forecast future prices (i.e. it focuses on the direction of prices). We suggest a straightforward deep learning (DL) architecture that makes use of Long-Short-Term Memory (LSTM), a sophisticated Recurrent Neural Network (RNN) component that has gained a lot of notoriety lately for its superior performance with time series data and resistance to the vanishing gradient problem.

Let G(V,E) be a graph with p vertices and q edges. Let f:V(G)→{0,1,2,…,p-1} be a bijection. Define f^*:E(G)→N by f_sqdp^* (uv)=|(f(u))^2- (f(v))^2 |,∀uv∈E(G). If f_sqdp^* is injective then f_sqdp^* is called square difference labeling of G. A graph G which admits square difference labeling is called square difference graph. The greatest Common incidence number (gcin) of a vertex of degree > 1 is defined as the greatest common divisor (g.c.d) of the labels of the incident edges on v. A square difference labeling is said to be a square difference prime labeling if for each vertex v of degree > 1, gcin(v) = 1. In this paper we investigate the square difference prime labeling for Prism graph, Braid graph, Umbrella graph

Wireless Sensor Network (WSN) is growing emencly in this cutting-edge automation world that consist of Sensor Nodes with its sensing unit, transceiver,microcontroller and a current unit that sense, monitor & manage the environment conditions. The static & dynamic positioning of sensors do communicate wirelessly with its adhoc configurations for analysis, processing, storing and retrieving as when required. However, the limitations of sensors devices are its transmission range that can transmit maximum range of 100 meters. The primary monitoring parameters of sensors are humidity, temperature, wind direction and speed, pressure, sound & vibration intensity to the levels of pollutant and vital body functions. Each individual sensor with its equipped transceiver, transducer, microcomputer and Current source where the electrical signals are generated by transducer from the sensed environment whereas the microcontroller executes and saves the sensor output. The adoption of clustering technique that groups sensor nodes by forming clusters and selects cluster heads among clusters thereby each cluster communicates among the nodes to execute through the cluster head. The nodes consumes more energy while sending data to the single sink led to adoption of multiple sink. The multiple sink strategy balances energy usage while also lengthening network longevity. This strategy uses heterogeneous wireless sensor networks with more homogenous sensor nodes to create a more durable network that can manage sensor nodes with diverse capabilities, such as disparate computing Both strength and detecting spans are employed. The combination of both approaches increases the stability and energy efficiency of random networks. This review paper analyses the classifications of different clustering protocols as well as measures the similarity and dissimilarity and examine the future applicable design factors and scope of improvement can be infused in the proposed solutions.This proposed solutions will guide a prototyper to develop a new efficient energy oriented clustering solutions that can strengthen the Network further.

Wireless LAN (WLAN) is a wireless network without fixed infrastructure. Load balancing, channel assignment, handoff and its security are critical challenges due its exclusive features. There are many load balancing algorithms, channel assignment and handoff methods, secure routing protocols presented. A broadstudy of several load balancing algorithms, channel assignment and handoff methods, secure routing protocols in WLAN is presented in this paper. These are evaluated in terms of various constraintslikepacket delivery ratio, throughput, end to end delay, total energy expended, standardized routing load, packet loss cost, etc.

All mining harvesters on power equipment are divided into working with own power installation and using powered energy. The first include machines equipped with thermal power plants, mainly internal combustion engines, the second - machines with electric or hydraulic drives. In exceptional cases, we have to deal with machines that can operate both from their own power installation, and from the energy supplied [43]. So, for instance the KSM 2000R combine is powered from two energy sources. Its rotary working body with a power of 1100 kW is driven by an electro-hydraulic power unit connected to an external network by a six kilovolt cable. The own diesel engine with a power of 1000 kW ensures the operation of the remaining drives of the machine [44].

This paper presents the comparative performance analysis of a 1-bit full adder in two different logic styles CMOS & TG. In this work, the foremost concentration is to lower power dissipation & delay by using the technology scaling approach and developing the circuit to operate at low voltage levels. The circuit interaction is made possible for designing more consistent functional architectures at the minimum power supply of 0.45-1.8v. In the conventional method, two half adders are designed to construct a full adder using an XOR-AND gate to generate a SUM and CARRY( 1-bit) with 28 transistors in static CMOS logic, such a high transistor count to generate 1-bit implies requiring more transistors to generate 4,8,16,32 bit. To minimize the transistor count another logic style called transmission gate (TG) is introduced to design such complex designs in an easier manner. The simulation results of 1-bit full adder CMOS, TG logic are taken from CADENCE Virtuoso in 180, 90 & 45nm technology and the parametric analysis proved better results for TG. This paper presents the comparative performance analysis of a 1-bit full adder in two different logic styles CMOS & TG. In this work, the maximum concentration is to lower power dissipation & delay by using the technology scaling approach and developing the circuit to operate at low voltage levels. The circuit interaction is made possible for designing more consistent functional architectures at the minimum power supply of 0.45-1.8v. In the conventional method, two half adders are intended to construct a full adder using an XOR - AND gate to generate a SUM and CARRY (1-bit) with 28 transistors in static CMOS logic, such a high transistor count to generate 1-bit implies requiring more transistors to generate 4,8,16,32 bit. To minimize the transistor count another logic style called transmission gate (TG) is introduced to design such complex designs in a more accessible manner. The simulation results of 1-bit full adder CMOS, TG logic are taken from CADENCE Virtuoso in 180, 90 & 45nm technology and the parametric analysis proved better results for TG.

To address the problem of inefficient use of radio frequency spectrum by licensed users and spectrum scarcity problem of forthcoming applications cognitive radio networks is one of the best solutions. Cognitive radio network enables secondary users to utilize licensed spectrum in the absence of primary users. The most crucial and fundamental step in cognitive radio networks is spectrum sensing. Energy detection is the simple, efficient and most feasible spectrum sensing technique, where threshold plays a key role. The threshold selection should be optimum to achieve better accuracy in taking decision of primary users’ presence after sensing. Hard fusion technique is simple and requires less bandwidth but less accurate. Soft fusion technique is more accurate but needs more bandwidth. The proposed algorithm divides the energy statistics into four sub-regions. Each secondary user sends a two-bit judgement to the fusion center in the proposed triple threshold mechanism. Simulation results proved that the suggested triple threshold algorithm performed better with respect to cooperative spectrum sensing approaches using various fusion rules.

The electricity industry must be restructured due to the rising trend of electric power demand, resource restrictions, and the degradation of existing grid infrastructure. Besides, in addition to the numerous advantages, the introduction of Internet of Things (IoT) technology and the conversion of the electrical grid to a Smart Grid (SG) creates security concerns. This study aims to incorporate Machine Learning algorithm-based Intrusion Detection System (IDS) to battle cyber-attacks, as this is one of the ways ahead in detecting and mitigating security attacks. A Random Forest classifier is employed in this method to detect intrusion, and the proposed method's performance is compared to the XGBoost Algorithm. The suggested method outperforms other methods for Intrusion Detection in IoT-based SG in terms of Accuracy and Precision, according to experimental data.TheXGBoost classifier has an 80 percent accuracy rate, while the Random Forest classifier has an 83 percent accuracy rate. Similarly, the calculated precision rate for XGBoost and Random Forest models is 81 percent and 84 percent, respectively.

Soil category is one of most significant factors which aids to decide that which type of crop should be planted in order to get effective yield. Common methods used by the farmers are in adequate for fulfilling the growingdemands and thus they have to hamper the cultivating soil. Agriculturalists must aware of suitable soil types for a specific crop, for proper crop yield and which would affect the increasing demand for food. There exists several laboratory and field techniques for classifying soil, however these have limitations namely labour-intensiveand time consuming. There occurs a need of computer-based soil classification methods, which will aid farmers timely and accurately. This article develops an automated soil classification using red deer optimization with deep belief network (ASC-RDODBN) model. The presented ASC-RDODBN model mainly aims to recognize different kinds of soil, which helps in proper crop type mapping process. To attain this, the presented ASC-RDODBN model undergoes two stages of preprocessing such as bilateral filtering (BF) based noise elimination and contrast enhancement. In addition, data augmentation process is performed to increase the dataset size. For feature extraction, the ASC-RDODBN algorithm uses radiomics features. Finally, DBN model is applied for soil classification process in which the DBN hyperparameters are tuned properly using RDOmodel. Extensive simulations were carried out on soil type classification dataset from Kaggle repository to highlight the better performance of the ASC-RDODBN model. The experimental outcome pointed out the enhancement of the presented ASC-RDODBN model over recent models.

Rainfall is a seasonal period in India, especially in the delta region. The cyclone period cause more rainfall leads flood to affect human life and agriculture rescues more effectively to make destroy. To avoid this flooding attack in seasonal time, to analyses the weather series data analysis to take precautions. In previous cases, the non-relational feature dependencies reduce the forecasting accuracy. To resolve this problem, we propose a spatial rainfall-intensive rate using PSO optimized Dense net Convolution neural network. Initially, the Delta region weather dataset is preprocessed to marginalize the features. The rainfall rate was analyzed through spatial data using spatial rainfall intensive rate (SRIR). To analyze the entity relation hit rate was estimated through the Seasonal rainfall Hit forecasting rate (SRHFR). Through the feature weight, Adaptive PSO feature selection (APSO) is applied to select the spectral features and trained with a Dense net convoluted neural network (DnCNN) to forecast the result based on flooding risk by category. This proposed system effectively attains high-performance evaluation in weather forecasting to predict rainfall and flooding level. The result performance shows the prediction accuracy as well as precision rate compared to the other system.

This paper aims at studying how the networking conversation within the community control has been taken into consideration in the current research inside the field of Supply Chain Management (SCM) and different commercial enterprise control literature. The basic community control framework used in this network consists of 1) network techniques led from the community perceptions, 2) employer and 3) operating standards, in which the control component can be additionally elaborated through communication perspectives. As a result of this paper it became obvious that the contemporary studies do not aid or address the function of communication in inter-organizational relationships, or, if it does, the emphasis remains on operational communication. However, it changed into verified that the change of strategic records is crucial in dealing with commercial enterprise relationships, which in turn gives an upward thrust to a study’s question of the meaning of verbal exchange in those relationships. Moreover, this observation delivered forth the need for considering also different studies traditions that complement SCM research in particular whilst regarding the managerial characteristics of communications within the networked financial system.

Breast malignancy is an affliction when breast containers evolve cruelly without expiring. These disastrous units set the clump and deadlock different sane cells to drive perfectly and therefore restrict the usual serving of the body. These lumps concede possibly favorable or diseased, contingent upon the part of the breast where tumor composition begun. It is very authoritative to pinpoint this ailment at boot camp so concerning get suitable medication timely in accordance with its kind and level with a view to help the affected person to vanquish the affliction and win the conflict of continuation. To help humans for the same, few computers advocated structures are pre-owned to investigate the ailment to grace the time of oncologist that in spotting the affliction that may be used in medicating to a greater extent and bestowing them the reward of survival. Still there are some loopholes in these structures because of which they are not fully utilized. One of major reason being is its fearful and expensive procedure to be undertaken by women for collection of data such biopsy and mammography. This research provides the solution of the above mentioned problem which aims to design fuzzy rule based framework to diagnose the disease using the data which could be collected from the blood sample using fearless blood analysis report. This research is enhancement of previous research done by authors on UCI’s Coimbra breast cancer dataset using fuzzy rules to diagnose the disease which achieved the accuracy, sensitivity and specificity as 90.3%, 87.3% and 95% respectively to increase the accuracy up to 93.2%, specificity upto 88.6% and sensitivity as 96.6% by reducing the rules up to 435.The expert system uses fuzzifier of Mamdani interface system to fuzzify the input values and defuzzifier that pins the output as benign or malignant. It is straightforward tactic of diagnosing the ailment with cheering accuracy as a way to appeal the women with signs and symptoms to get it recognized at early levels thus help in lowering the mortality prices to a big fag give up.

Long phrases might be tedious to write, but text prediction technology built into keyboards makes this simple. Another name for the next word prediction is language modelling. The task at hand is anticipating the first word that will be said. It has several applications and is one of the main tasks of human language technology. This approach employs letter to letter prediction and says that it predicts a letter when letter is used to build a word. Long short time memory equation can sense prior text and anticipate the words that may be beneficial again for users to surround phrases.

In this paper, a few endeavors of the intuitionistic multi fuzzy normed primary ideal, as well as intuitionistic multi fuzzy semi-primary ideal, were depicted the same outcomes considering intuitionistic multi fuzzy primary ideal and semi-primary ideal are besides settled.

The concept of a Neutrosophic fuzzy set, along with C prime and E prime bi ideal over subring, is introduced in this study We went over each concept with thresholds condition. We outlined a few theorems using level set which characterize Neutrosophic fuzzy C and E prime bi ideal and investigated several related results.

In this paper, we have focused on defects in aluminum radiator core. Radiator is simple air cooler function is eliminating excess heat from the engine radiator is used in a turbo engine for cooling purpose. Radiator is made up of core assembly, a tube, fin, cover channel(CC) and a header plate(HP). The core is made up of aluminum with clad material. During the manufacturing by brazing a process is one kind of welding processes after lots of defects are encountered leads to the leakage of radiator core. The header and a tube joint problem to check out of a dry and wet leak test machine. The study based on the aluminum alloy of radiator core defects and leakages testing a machine.

The investigation objective of this study is to collect data for environmental impact around demolition sites keeping in view the direct, especially the workers and indirect, the people living in the three boundary residences around the civil structure being demolished. At all the three location of instruments (points) around the sites the air quality data was recorded, monitored and collected without break for concentrations of Particulate Matter i.e. PM10, PM2.5, gases Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) along with Noise Levels. Three locations at the main entrance and two flanks of the demolition sites were equipped. The three stages were before, during and after demolition to accurately assess the parameters being evaluated. The data has been recorded for 12 days i.e. 3 days before, 5 days during and 4 days after demolition for a total of Ninety Six (96) hours. The quantification is done at the laboratory. The result as was expected, the PM10 emissions is beyond permissible limits during peak demolition activities. Hence, it can be concluded that care must be taken regarding the health of the people working on the site as well as the people in the residences around the site.

Composite materials with an aluminium matrix and ceramic reinforcement are becoming more popular in Industrial application. The addition of AlN reinforcement increases the hardness of aluminium 7075 composite. In the current study, stir casting method was used to manufacture aluminium 7075-AlN composites by increasing the weight of AlN particles from 0 to 10 percent by a step of 2 weight percent. The composite matrix and As-cast alloy underwent a solutionized treatment for two hours with a temperature of 470° C, which was followed by extinguishing in different media such as air, water, and ice. Then the specimens are subject to artificial ageing at 120° C. Microstructure analysis and hardness test were carried out on both as cast and Al7075-AlN metal matrix composites. Al7075-AlN MMC’s shows the improvement in hardness when compared with Al7075 alloy.

These "virtual MIMO" techniques are used in wireless sensor networks to make communication more efficient, and they work just like real MIMO. A lot of people now use wireless sensor networks that run on battery power. Because they need to be energy efficient and last a long time, this means they must be good at these things as a way to solve this, a new architectural method called Virtual MIMO was made. To make an energy efficient network, this method is very important. It helps you figure out how and where it should be used. You can use MIMO technology in a single antenna system by taking advantage of the cooperative concept and its energy-saving method with virtual MIMO. This survey paper mostly talks about how to do this. To make a wireless sensor network last longer with the least amount of energy, this shows how Virtual MIMO techniques can be used to do that A wireless communication system with multiple in and multiple out antennas, as well as different S-parameters, are all simulated. Return Loss in dB is also shown for different frequencies, like S21, S22, S11, and S12. These frequencies have different Return Loss in dB. A lot of people now use wireless sensor networks that run on battery power. Because they need to be energy efficient and last a long time, this means they must be good at these things as a way to solve this, a new architectural method called Virtual MIMO was made. To make an energy efficient network, this method is very important. It helps you figure out how and where it should be used.

The outspread of corona virus made us wear masks wherever we go. But, people are not taking the precaution of wearing masks and it's difficult to find people who are not wearing masks. The author build a mask recognition system based on an image classification model using a machine model. More than 400 photos of people wearing masks and without masks were used to train the model. This model can be implemented within the mall, Cinema Theater, colleges, and other crowded places camera system to detect who aren't wearing the mask in the real time. Most of the companies use a traditional ledger, Password, or Biometric based attendance system for employees, which involves contacts that are not recommended in this outrage of corona virus. To ensure safety the model was developed to recognize the unique faces based on the image classifier and it improves by learning everyday changes in the faces of the public.

The suggested duty cycle MAC protocol in this paper has been applied in a wireless sensor network to lower the energy consumption of sensor nodes. For the energy usage in this study, MC-LMAC, Energy –conservation Medium Access Control must be used. The primary goal of this research is to improve sleep latency while also balancing energy usage among sensor nodes. This research compares the suggested MC-LMAC to the RI-MAC and presents the results, which are modelled in NS-2. The experimental results show that the suggested method outperforms others in terms of average energy, packet delivery ratio, duty cycle, and residual energy.

Due to augmented reality, people's perceptions of the world is evolving. Smart augmented-reality glasses with a high-resolution Optical Head-Mounted display add video, audio, or graphics to real-world views. Smart Glasses and other heads-up display devices are not new, but their use in various areas, including education, has increased in recent years. AR is one such technology that provides a novel instructional method for learners to build critical thinking abilities and a deeper understanding of concepts that underlie scientific investigation. Although augmented reality has been available in some form or another for decades, the first important examples of its use in education date back only a few years [8]. It has already proven to be quite beneficial to the learning process in such a short period of time, and this paper highlights a few of the most important ones. [9].

Due to the rising need for high strength/high performance concrete, alternatives to cement as a binding medium are becoming more and more important and then can be met by use of Supplementary cementitious materials (SCMs). To lower the cement content and give concrete more strength, these SCMs will be partially replaced which leads to smaller pore sizes densifies the pore structure which improves properties. In comparison to cement-only control concrete, this study examines the effects of SCMs including Metakaolin (MK), Flyash (FA), and Nano silica (NS) when employed in quaternary blends. The samples of size 150 x 150 x 150 mm was casted and tested at different temperatures of 2000C, 4000C, 6000C, 8000C, and 10000C for a duration of 4 hrs, 8 hrs & 12 hrs in an electrical bogie hearth furnace and compared with samples tested at room temperature. Studies included colour change, spalling, weight loss, and residual compressive strength. At 2000C, specimens' compressive strengths are nearly identical to those evaluated at room temperature; however, at 4000C, it was shown that, during a fire lasting four hours, specimens' residual compressive strengths increased by as much as 15.61% for quaternary blended concrete and 3.67% for control concrete, respectively. The residual compressive strength of concrete specimens exposed to temperatures beyond 6000C significantly decreased, and the lowest residual strength of about 10% at 10000C for a length of 12 hours for both quaternary blended and control concrete was observed at this temperature. Results showed a larger loss of compressive strength with longer exposure times at the same temperature. It was found that quaternary blended concrete had a higher residual compressive strength than control concrete, making it more fire resistant than control concrete.

Wastewater generated from industrial effluents poses potential damage to human health as well as causing environmental degradation. Chemically (Potassium hydroxide) modified Vetiver activated carbon prepared and used as an adsorbent material in the study, was characterized using FTIR and EDXSEM. Batch adsorption studies carried out to remove Lead (Pb+2) and Nickel (Ni+2) in binary feed and effect of variable operational parameters such as pH, contact time, adsorbent dose and initial concentration of metal ions were studied. The experimental data reported the highest adsorption of 96.34 % (91.33 mg/g) for Pb+2 and 95.67 % (59.62 mg/g) for Ni+2 at optimum values of selected factors being pH of 5.5, a contact time of 105 mins, adsorbent dose 0.10 gms and for an initial concentration of 40 mg/L, 60 mg/L. The process parameters were optimized using response surface method (RSM). A two-level four-factor (24) CCD model for factors pH, Contact time, adsorbent dose and initial concentration used for optimization, which showed maximum % removal by Ni+2 97.51%. The experimental data fitted well for Pb+2 metal having the highest R2 0.96.

Assesment of impact of climate change on water resources in river basin requires a proper estimation of availability of water and that can be achieved by hydrological modelling of the basin. The primary objective of this study is to develop a hydrological model to conduct a satisfactory rainfall-runoff study of Gumti river basin of Tripura, India. Besides this the physical characteristics of the river basin has been also determined. In this study a catchment simulation model Hydrological Modeling System, developed by Hydrological Engineering Centre, USA (HEC HMS) with soil moisture accounting algorithm has been calibrated and validated for Gumti river basin in North Eastern India for prediction of its hydrological response. The daily rainfall data and daily observed flow data of the two years 2012 and 2014 were used in developing the model. The catchment area has been divided into the 25 numbers of sub basins for detailed study. The rainfall in a particular rain gauge is considered as uniformly distributed over the entire sub catchments. Spatially distributed catchment characteristics have been obtained from the 90 m resolution SRTM digital elevation data. For computation of overland flows, the Soil Conservation Service’s (SCS) Curve Number (CN) method is applied in association with hydrometeorological properties, namely, soil type, land use, antecedent soil water conditions, and channel information. The simulated models by SCS method and Snyder’s UH are tested for two severe storms occurred over the catchment. The simulated results are found to match well with the observed value. The Nash – Sutcliffe model efficiency ranges from 0.61 to 0.911and the root mean square error 4.42% to 5.10% which indicates good performance of the model. Validation for the month of July 2014 was done by using two different transform and loss method to compare the results of simulation run. It showed that the second method which used SCS – CN loss method and SCS Unit hydrograph transform method produced higher peak values and also higher total monthly simulated flow values than the first method that used Snyder’s Unit hydrograph transform method at the outlet gauge Sonamura. Thus the study shows that the calibrated model performs well in simulating stream flow and that the model can be used for hydrological studies and water resource management of the Gumti river basin.

Groundwater is one of the most valuable natural resources, which supports human health, economic development and ecological diversity. Because of its several inherent qualities it has become an immensely important and dependable source of water supplies in all climatic regions including both urban and rural areas of developed and developing countries. Groundwater is a form of water occupying all the voids within a geological stratum. Water bearing formations of the earth’s crust act as conduits for transmission and as reservoirs for storing water. The groundwater occurrence in a geological formation and the scope for its exploitation primarily depends on the formation of porosity. High relief and steep slopes impart higher runoff, while topographical depressions increase infiltration. An area of high drainage density also increases surface runoff compared to a low drainage density area. Surface water bodies like rivers, ponds, etc., can act as recharge zones.Present study aimed various activities such as DEM map preparation, LULC map preparation, using ArcGIS 10.8 software and interpretation of the outputs. GIS and remote sensing technology is applied to prepare various thematic maps with reference to groundwater like drainage density, contour, and stream length. Slope, drainage and elevation maps were derived from the digital elevation model.

Recently, in connection with the increase in oil production and production of petroleum products, the process of alienation of land from agricultural use is gaining scale. Oil and petroleum products are recognized as priority environmental pollutants. Entering the soil, they primarily affect its biological properties. The chemical and physical properties of soils also deteriorate [1, p. 67]. As a result, soil fertility decreases, the soil becomes incapable of performing ecological functions. The aim of the work is: to study the influence of pollution by oil and oil products on the biological activity of ordinary chernozem [2, p. 31]. Scientific novelty of the work. A detailed study of the effect of oil and oil products on the biological activity and other properties of ordinary chernozems was carried out. A complex of microbiological, biochemical and other indicators of soil condition, a set of pollutants and the range of their content in the soil were also studied.

The article discusses the idea of a scientific and technical project won in the framework of a competitive application for 2020-2022 for the development of a combined tool adapted to the soil and climatic conditions of the Southern region of Kazakhstan for pre-sowing tillage and sowing seeds in order to increase their yield, reduce the cost of their cultivation, preserve and increase soil fertility. According to the results of the studies carried out at NPCAI LLP, it was found that the experimental sample of a combined unit for pre-sowing tillage and seed sowing is designed for processing row spacing to a depth of 10-12 cm. To ensure a processing depth of 20-25 cm, it is necessary either to increase the diameter of the milling drum, or to almost completely bury a small diameter drum into the soil. The first, as numerous studies show, is impractical due to a sharp increase in the cost of milling power and an increase in the metal consumption of the milling cutter: so, forest and swamp cutters capable of cultivating soil to a depth of more than 20 cm, and having a diameter of 600-800mm milling drum, with a small width of capture, require heavy energy-saturated tractors for their aggregation. In this regard, the combined unit developed within the framework of the project of the Ministry of Education and Science of the Republic of Kazakhstan for pre-sowing tillage and simultaneous sowing of seeds in one pass significantly reduces the level of costs compared to the traditional scheme of pre-sowing soil preparation and sowing. The advantage is to reduce the timing of sowing operations by half, as well as saving fuel by 20-25% and preserve moisture in the soil and reduce energy consumption during sowing.

On the basis of research, the optimal modes of cold conditioning of triticale grain of various vitreousity groups have been practically justified - the time and duration of sweating, which, when preparing grain for milling, will help create conditions for improving the confectionery properties of triticale. The optimal modes of grinding when milling grain samples have been determined. A structural scheme for the production of high-dispersion triticale confectionery flour has been developed, which allows the formation of a stable stream of flour with a given composition and properties, and its use for the production of sugar cookies has been scientifically substantiated. On the basis of test laboratory baking, it has been proven that flour confectionery products can be produced from triticale flour with 100% replacement of wheat flour without deteriorating the quality of finished products.

In distribution power systems, an optimal network reconfiguration (ONR) is utilized to reduce losses in the real power and enhance the voltage drops within the allowable interval. As a result, achieving optimal reconfiguration in distribution systems is considered the main aim of several researchers. The use of conventional heuristic algorithms of genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO), can minimize the active power losses and improve the network’s reliability. These algorithms reports more issues such as poor convergence characteristics, amount reduction in power loss, amount increasing in bus voltage. For this reason, this paper aims to suggest, the efficient optimization techniques of a salp swarm algorithm (SSA) and whale optimization algorithm (WOA) to increase the voltage of the bus, decrease the distribution losses, and then enhance the reliability of the network. To establish the optimal network architecture, the suggested algorithms are implemented and tested on IEEE 33- and 69 bus networks. The effectiveness of the studied techniques is demonstrated using MATLAB in steady-state circumstances, with advantages in active power loss reduction compared to existing algorithms. From the comparison, the SSA algorithm presents the best performance for power losses and the bus voltage improvement compared to the WOA algorithm.

Communities will grow more congested as civilizations become more wealthy and urbanized. As a result, trash disposal, particularly biodegradable waste, will become more difficult since it will be thrown in the streets, having a detrimental effect on both human health and the environment. This biodegradable waste can be turned into vegetable manure for the terrace garden, which would lessen the immediate family needs. Terrace gardening not only helps households obtain fresh produce free of chemicals but also returns some land to agriculture.

Graph theory is vital in a variety of areas of computer science, including networking, app design, information retrieval, image processing, clustering, data science, cryptography, data mining, and so on.Social networking sites have a sizable user base, particularly for mobile apps. This study comprises a poll on Google Maps, a popular mobile app used by mobile users all around the world. In particular, the shortest route problem, traffic density, and the detection of traffic conditions in Google Maps were the focus of this article's discussion of the use of graph theory in Google map design.Many specialists and academics have done studies to increase the effectiveness of road network route planning, and Dijkstra's method is a research hotspot. As a case study, we give an example of finding the shortest route between two locations in Chennai and used Dijkstra's Algorithm with C++ code to find the shortest route.

The market of mobile phone gets expanded with gradual increase in smartphone demands at an aggressive phase and this booming over smartphone industries have provocative an essential for realizing the complete features of the phone models and the brand. However, the electronic commerce has played a major role in improving the mobile phone sales and even prompted the pattern of consumer buying. The information at anywhere and anytime is the style mantra in usage with improved rate due to development of smartphone. Hence, there are numerous products manufactured by several dissimilar brands but providing relevant features to customer is hour of need. In addition, confessing of intricate relationship among entities has become an important to business decision, prediction and optimization. This relationship not only involves explicit based on co-occurrence and even considered implicit relation based on non-co-occurrences. In most of the association rule get focused on explicit relation that are considered as a rule mining based methods but the focus on relationship of implicit is addressed very low due to less frequency itemsets. Therefore, this paper has addressed the implicit relationship among items that occur rarely or doesn’t occur when each itemsets co-occurs with other identical items with high possibility. This paper has initiated with two phases namely explicit and hidden item dependencies which get integrated using algorithm of Integrated Implicitly Rule (IIR) that assist in seizure implicit relationship. The input survey of Google form has been taken from students and staffs of Anna University is provided with smartphone feature expectation based on recent trends. This analysis provide the data about requirement from customer with least frequency but high probability that assist the retailer and manufacture to focus on the particular features to cover all aspects of featured smartphone.Moreover, an implementation of IRR algorithm is to generate recommendations and illustrate the identified implicit rules which can increases recommendation reliability.

In this paper using the polar semiconductor in a magnetic field , the first excited state (ES) Landau level add a zero- phonon state is degenerate with the ground state (GS) Landau level add one longitudinal optical (LO) Phonon state at ω_0-ω_c , ω_c is the cyclotron frequency. The purpose of in this paper is to investigate the pinning effect in a GQD in two dimensions using the improved Wigner -Brillouin Perturbation theory (IWBPT) .Since Gaussian quantum dot has two parameters to play important role namely the depth and range. One would expect much richer pinning behavior in GQD. We apply our calculation to GaAs and InSb in QD.

The present work aims at studying mechanical property of the S-glass reinforced along the epoxy matrix composite which are filled with alumina filler at 0, 5% and 10% wt. percentage fabricated through hand layup techniques which will be making it like hybrid. To understand mechanical and physical abilities of prepared composite; the density, the tensile, flexural, impact and hardness tests are considered for ASTM standards respectively. The results obtained from the investigation reveals that, as the Alumina filler loading was increased to 10% wt. percentage in the S-glass epoxy hybrid composite, the mechanical properties were improved. The SEM analysis of the fractured tensile specimen was conducted, which showed the breaking of the particles, fiber pull outs and fiber breakage, matrix /fiber debonding of composites.

In the past, many researchers heavily relied on various small coverage area networks in real-world environments to evaluate the efficacy of different routing algorithms. The study of vehicular movement performance using various mobility pattern simulations is necessary given the recent, rising interest in vehicular ad hoc network communication (VAC). We present a study on the effectiveness of several routing algorithms with diverse mobility patterns for WiMAX and other broad coverage area networks, in contrast to many earlier works. Additionally, we're going to use a variety of routing algorithms with various mobility patterns, multiple input multiple output (MIMO), and the adaptive modulation and coding (AMC) technique to improve various quality of service (QoS) parameters like throughput, jitter, delay, packet delivery ratio, and packet loss ratio for different scenario networks.

For those with movement disabilities, a variety of hands-free mouse replacement systems have been developed, and during the past three decades, numerous advancements have been made. Over the past three decades, numerous authors have proposed alternatives to the mouse for people in the movement with disabilities who have not yet had a fair opportunity to utilise the standard input methods of a personal computer. In camera-based systems, the overhead of using head-mounted devices is reduced by using the web camera as the mouse. Tracking user facial expressions as they are being captured by the camera and accurately translating them into mouse cursor movement and click events are research challenges and opportunities. The current systems can only move the pointer in a slanting manner due to the user's accidental head movements losing the tracked feature. The movement has not yet allowed people with impairments the same chances as others to interact with computers. They have trouble using the input devices on computers due to their mobility problems. Controlling mouse pointer navigation is still difficult, despite the fact that on-screen virtual keyboards may be used to simulate a physical keyboard and speech recognition can be easily utilized to map mouse click events. The development of hands-free mouse replacement technologies has undergone much advancement. There are a number of limitations to mouse replacement systems that use a webcam as a mouse. In order to enable people with movement disabilities to use a standard PC, this research suggests enhancing the ability of camera-based hands-free computing systems to control the mouse cursor by predicting the user's selection of the target item in the GUI-based system using neural network techniques. Using samples where the mouse cursors predicted position values are closer to the user's actual selection region on the computer screen, the system is put to the test, and the anticipated outcome is achieved in every sample.

This paper offers a decentralized identity management system based on Blockchain. The purpose of the system mostly embraces identity verification, authentication, and reputation management. The advantage of blockchain systems is to make data more secure and reliable. In accumulation, we use ABAC (Attribute-based access control) with a decentralized identifier in the blockchain-based energy traction platform to write system rules to ensure the reliability of user information. We use the tesseract-ocr to extract text from images and document and store it. And we also use CNN (convolutional neural network) that has one or more convolutional layers and is mainly used for image processing and face recognition. Our system makes it possible for the user to securely manage their identity and reputation on the internet.

Due to Digitization, there are significant developments and changes occurs in the way people do purchasing, they prefer to buy products online instead of offline. Shopping at just one click of mouse makes everything easy, convenient and at the same time saves a lot of time. Here comes the first challenging task for E-Commerce Companies now a days is to handle bulk returns that is a part of reverse logistics and the second challenge is to make the best possible decisions regarding route and transport in low cost. This paper focuses and gives the solution to above problem by Predicting in advance which product and by whom it is going to be returned in future and after that optimizing the best possible route and transport for returned product is done using AdaBoost model. Computational calculations shows that our framework generates satisfying results in terms of prediction and route optimization. We further shows a comparison between adaboost and regression methods of machine learning by confusion matrix.

Unified Power Flow Controllers (UPFC) is one of the most versatile AC Transmission Systems (FACTS). It is used to control the transmitted power in power systems as it has many advantages in transmitting active and reactive power. Power is regulated by the UPFC on the transmit side of the plant system. This study evaluates the performance of UPFC for power oscillation compensation using particle swarm improvement (PSO) technique for DC link voltage management. A squared measure of the most effective PI control parameters required a particle swarm improvement technique. Once everyone establishes the squared measure of the significant power, i.e. the reactive power, the UPFC tries to maintain a proportional-integral controller (PI) and thus a command-adaptive neurofuzzy logical thinking controller (ANFIC). However, PI controllers and ANFICs cannot provide the required amount of energy in some power systems due to plant vibrations. These fluctuations are caused by ANFIC properties, i.e. PI controllers. During this work, the squared measurements of PI and ANFIC controllers were replaced with a particle swarm improvement approach. The objective function is used to reduce plant vibration. In our analysis, we used MATLAB/SIMULINK simulation code to demonstrate the usefulness of Algorithm Manager.

The Electricity machine`s (PS) transmission line losses may be mitigated via way of means of Var compensation. The relevance and alertness of Flexible AC Transmission Systems (FACTS) gadgets for editing line strength flows to relieve congestion and decorate universal grid functioning have elevated because of the beginnings of energy markets with transmission accessibility. The maximum entire and multi-variable bendy ac transmission (FACTS) tool is the unified strength-go with the drift controller (UPFC). It can adjust all of the variables influencing the transmission line's strength go with the drift simultaneously or intelligently (i.e., voltage, impedance, and segment angle). In this study, the nice strength manipulates parameters for the UPFC machine are received the usage of the Ant Colony Optimization (ACO) approach. The simple manipulate method is such that the shunt converter controls the transmission-line reactive strength go with the drift and the dc-hyperlink voltage. The collection converter controls the transmission-line actual strength go with the drift and the UPFC bus voltage. The actual/reactive strength managed through (ACO) techniques with inside the UPFC manipulate machine can gain excellent overall performance each at some point of temporary and strong conditions. ACO is implemented concurrently on manipulate layers and consequences are in comparison with classical tuning approach and nicely set up ANFIS manipulate technique, Particle Swarm Optimization (PSO) the usage of MATLAB/Simulink software.

In this work, we proposed a method for person identification using Texture features. We used fingerprint and ear features traits and fused. The proposed feature extraction technique like LBP, LTP, LQP and ELLBP was used to extract the features from ear and fingerprint images. These extracted features were stored in the database. We fuse the texture features using concatenation rule and select discriminative texture features by employing wrapper feature selection methods. For testing, the features were extracted and compared with the features stored in the database and matching was performed using KNN Classifiers with different distance measure. Performance of the system was analyzed with individual traits, fused fingerprint and ear features, and other feature extraction methods LBP, LTP, EEIPD and ELLBP. So as to improve accuracy rate and to provide a more reliable and secured multimodal system for person identification.

Cover Song recognition from audio signals is a recent research topic in Human-Computer Interaction. Here we developed a system to detect cover from the audio signals. Cover Song Detection is created by extracting various audio features and combining them differently to create feature vectors. The feature vector is created using audio features like MFCC, MEL, and Chroma. Further, we also worked on using single, two and three combination of features. The cognizing was performed through Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbor (KNN) along with employing the CNN. Importantly, CNN has shown notable improvement. The Proposed classifiers are compared with each other with combination of features. Their performances are compared using different classifier. Final proposed system works to identify best features and classifier for cover song detection.

Region is a contiguous geographical area, which has a fair degree of uniformity, in administration, economic linkages or natural environment. It is relatively a large area with hierarchy of settlement and varying landscape. The Planning Commission has also developed methods for regionalization and policy guidance for preparation of development plans for regions. This study focuses on to understand the concept of region with the help of Surat Metropolitan Region. Surat district administrative boundary has been taken as the regional area for the study purpose. There are 9 Talukas besides the main city that is Surat Municipal Corporation as the main settlement in the entire region. It is an attempt to understand each Talukas, with population projection and detailed study of certain sample villages with field visits and census-based data. Regional growth pattern is being examined with the help of identification of satellites growths in hierarchical order around the main central parent city using the Newton’s gravitational law. Analysis technique of ‘SMART’ Simple Multi Attribute Rating Technique has been applied to assess and rank each Taluka for the Sustainability based Key Performance Indicators (KPIs).

This paper focuses on the user satisfaction with electronic resources and services offered at the Institute of Management Studies (IMS), Noida and Greater Noida Institute of Technology (GNIT), Greater Noida's management institutional libraries. The main goal was to determine how satisfied people were with the utilization of electronic resources and services in both management institutions. Each Management Institution has a sample size of 100 users. Each of the Institutions received 62 responses from the sample size.

In this paper, we investigate some algebraic properties of real valued horizontally quasi-continuous functions. The notions of upper and lower horizontally quasi-continuous functions are introduced. Examples are provided wherever necessary. A few results on the uniform limits of horizontally quasi-continuous functions are established. AMS Subject Classification: 54C05, 54C08.

A natural language is one that humans speak, write, or sign for everyday communication, in contrast to formal languages. Natural language processing refers to the computational processes needed to allow a computer to process information using natural language. Nyishi part-ofspeech tagging is more challenging to answer than the English equivalent because it must be combined with the word identification problem. A POS Tagger assigns the appropriate tag, such as a noun, adjective, verb, or adverb, to each word of the input sentence. We incorporate Penn Treebank's tag set concept of word tagging format. A POS tagger's Tag set and Disambiguation Rules are essential components. The lack of a corpus for computational processing makes POS tagging for the Nyishi language challenging. Here, we discuss our work on first-order, fully linked hidden Markov models-based Nyishi part-of-speech tagging. For training and testing, a corpus of about 30,000 Nyishi characters is used. A Viterbi-based word identification algorithm divides an article into clauses and subsequently into words. The following experimental findings are presented for various testing scenarios 89% of the words in the testing data can be accurately tagged by the system.

In this study, gold nanoparticles (AuNPs) were synthesised biologically using cell-free extracts of the cyanobacterium Nostoc sp. strain HKAR-2 isolated from an Indian hot spring. UV-VIS spectroscopy, FTIR, X-ray diffraction, scanning electron microscopy (SEM), and transmission electron microscopy-selected area electron diffraction (TEM-SAED) were used to determine the morphological, structural, and optical properties of green synthesised AuNPs. The absorbance peak at 540 nm was observed due to the reduction of Au3+ to Au0 by cyanobacterial extract, indicating surface plasmon resonance (SPR) of the synthesised AuNPs. Characteristic In the XRD pattern, Bragg peaks at (111), (200), (220), and (311) facets of the face centre cubic (fcc) confirmed the crystalline nature of AuNPs. FTIR results revealed that proteins and amino acids play a role in the reduction of Au3+ to Au0 as well as the stability of AuNPs. A charge of -2.39Mv on the AuNPs was confirmed by zeta potential. The results of SEM and TEM confirmed the large agglomerated shape of AuNPs with sizes ranging from 10-100 nm. Their antibacterial, antifungal, and anticancer properties were also investigated in vitro against plant bacterial strains, fungal strains, and MCF-7 cells. AuNPs also demonstrated dose-dependent cytotoxic activity against MCF-7 human breast cancer cells, with an IC50 of 250 g/mL.

The Leukocytes which play a major role in the diagnosis of different diseases. Leukemia is a type of blood disease or so-called cancer of the blood that begins in the bone marrow; and usually caused by an excessive alteration in the production of malignant and immature white blood cells. Acute Myeloid leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Initial AML screening and considered as the first step toward diagnosis. AML and its prevalent subtypes, i.e., M0–M3. The proposed system takes as input, Color images of stained peripheral blood smears and identifies the class of each of the White Blood Cells (WBC). The segmentation process provides enhanced image for each blood cell containing the cytoplasm and the nuclei regions of the cell using Hybrid Color and Cluster base method. The process of feature extraction using texture feature. At the end system machine learning classification techniques use to differentiate the Acute Myeloid leukemia (AML) types from M0-M3.

Aim of this article is to characterize the partial orderings for the k−idempotent Intuitionistic fuzzy matrices. Also, using the g- inverses, we discuss some properties for the T−ordering, minus and space ordering in k−idempotent Intuitionistic fuzzy matrices(IFM).

A graph G with α points and β lines is known as a Hmg if it is possible to value the points z∈V with different values g(z) from 1,2,…,β+1 in such a way that when every line l=ab is valued with g(l=ab)=⌈2g(a)g(b)/(g(a)+g(b) )⌉ or ⌊2g(a)g(b)/(g(a)+g(b) )⌋ then the line values are distinct. In this case, g is known as the Hml of G. In this paper, we proved we prove that some special graphs such as the Path union of two cycles C_m, k- Path union of two cycles C_m, Path union of two crowns C_m^* and k- Path union of two crowns C_m^* all are Hmg.

Cloud Computing has become a popular choice with the magnified evolution to facilitate various service provisioning to users, and the resources are dynamically configurable and accessible from anywhere. Besides maintaining interoperability between heterogeneous platforms, the services get provisioned over the internet. The service discovery is a critical phase when the clouds cooperate in providing various business opportunities. Selecting cloud services is vital to ensure efficient migration in cloud environments. If cloud service selection is inappropriate, the customer may face vendor locked-in issues, portability, and interoperability problems, significant obstacles to adopting cloud services. A new meta-brokering model is proposed to avoid the complexities mentioned before. The full rank ordering recommendations using the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method enables the cloud users to decide and port services selectively and efficiently based on the weighted Decision Rank model with QoS analysis for service discovery along with distance criteria evaluated using the geographical location of Cloud Solution provider (CSPs). The proposed approach demonstrates the effectiveness of the combination of the Haversine and SPOTIS hybrid model and how SMI attributes affect the ranking recommendations. Our approach observes higher accuracy and outperforms other ranking methods for better service discovery.

Real-time & on-field crop imagery has illumination variations, specular reflections and shadows from neighbouring objects. To efficiently segment such images, a wide variety of deep learning models are proposed by researchers. But these models are either highly complex, or do not take into consideration multiple noise effects during segmentation operations. Moreover, the classification efficiency of such models is limited by their feature representation and classification performance, which limits their scalability when applied to real-time scenarios. To overcome these issues, this text proposes design of a Generative Adversarial Network (GAN) based segmentation model for efficient disease identification & severity estimation in apple crops. The model initially collects large datasets tagged with disease types & their severity levels. These datasets are used to train the Generator Network that assists in identification of noise types. These noise types are removed by the Discriminator Network via loss reduction processes. Segmented images are given to a Long-Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU) based feature representation model, which assists in estimation of highly variant feature sets. These feature sets are used to train a Recurrent Neural Network (RNN) for estimation of various disease types. Due to which diseases like bacterial blight, scab, fungus, etc. can be efficiently estimated under real-time scenarios. The identified images are further processed via a GoogLeNet based Convolutional Neural Network (CNN), which assists in severity estimation for each of the disease types. Multiple severity-level networks are trained in order to improve the estimated performance for different disease types. Due to which the proposed model is able to improve disease classification accuracy by 8.5%, while improving classification precision by 4.3% under different scenarios. The model was also able to improve severity estimation accuracy by 9.4%, and severity estimation recall by 8.3% when compared with state-of-the-art deep learning techniques. Due to which, the proposed model is capable of deployment for real-time scenarios.

Brain Tumour analysis without human involvement is a crucial field of study. Convolutional neural networks, on the other hand, can help with this (CNNs). They have excelled at solving computer vision and other challenges such as visual object recognition, detection, and segmentation. It aids in the diagnosis of brain tumours by improving brain pictures utilising segmentation algorithms that are extremely resistant to noise and cluster size sensitivity issues, as well as the automated area of Interest (ROI) detection. One of the key arguments for using CNNs is that they have a high level of accuracy and do not require human feature extraction. Detecting a brain tumour and correctly identifying its kind is a difficult undertaking. Because of its widespread use in image recognition, CNN performs better than others. Because a human-assisted manual categorization might result in erroneous prediction and diagnosis, brain tumour segmentation is one of the most important and difficult challenges in the field of medical image processing. Furthermore, it is a difficult process when there is a huge amount of data to assist. Because brain tumours have such a wide range of appearances and because tumours and normal tissues are so comparable, extracting tumour areas from pictures becomes difficult.

Grid-connected photovoltaic (PV) systems are increasingly attracting the attention of industry and academia as a means of providing an alternative to conventional fossil-fuel generation. In grid-connected PV systems, a key consideration is the design and operation of power converters and how to achieve high efficiency for different power configurations. The requirements for converter connection include: maximum power point, high efficiency, control power injected into the grid, and low total harmonic distortion of the currents injected into the grid. Consequently, the performance of the inverters connected to the grid depends largely on the control strategy applied. This chapter presents a comprehensive overview of power converter topologies and control structures for grid-connected PV systems.

By deconstructing biochemical pathways into intermediary components between genotype and phenotype, gene expression research bridges the gap between DNA information and trait information. These findings provide new paths for uncovering complicated disease genes and biomarkers for illness diagnosis and therapeutic effectiveness and toxicity evaluation. However, the bulk of gene expression data analysis techniques are ineffective for biomarker discovery and illness detection. We proposed BICC framework for cancer classification using transfer learning. The datasets are pre-processed by using decision tree regressor, the features are selected by using Random Forest (RF) with Logistic Regression (LR). The training and validation has done with RESNET50. Finally the classification has done with stacking ensemble classification. The experimental results demonstrate that very high classification accuracy can be attained by hybrid classifier with several biomarkers.

The current study focuses on spatial and temporal land surface temperature changes as well as "Land use / land cover changes in Bathinda, Punjab." Land use changes are highly active in nature and should be evaluated on a regular basis for long-term development. This paper examines surface temperature changes in relation to changes in land use and land cover in Bathinda, Punjab, over a 32-year period. This study is primarily based on multi-temporal (1990, 1999, 2010, 2022) satellite images (Landsat8 and Landsat5). Land surface temperature is an important indicator in the study of the earth's environment. Land use/cover changes have a significant impact on the environment. Supervised classification was used to analyse land use for various time periods. Using Land sat satellite images, various LULC classes, such as agriculture, open land, vegetation, built-up areas, and water bodies, were demarcated and mapped. Over a 32-year period, large-scale changes in land use/land cover were discovered in Bathinda city. These changes could be attributed to rapid urbanisation and other forms of development. Bathinda's built-up and vacant areas have grown during this time period. In comparison to other land cover characteristics, all land cover categories' land surface temperature has risen during this time period. The temperature has raised the most in the built-up and open areas. Temperatures have raised more in areas classified as built-up urban, as opposed to agriculture, open land, and vegetation. Agricultural land has decreased as most agricultural fields have been converted into built-up areas and vacant land (essentially acquired for construction and subsequently such vacant areas have also been converted into built up area). It is therefore critical that the study be very visible because extensive changes in land use and land cover change have an impact on the socio-economic scenario of the area under study and are required to provide the amenities that the transformed land deserves for overall development of the area.

The present paper discussed the Land use-Land cover classification of the fringe area of Hisar city. For the classification process, the Landsat satellite image data from three time periods i.e. 2000, 2010, and 2020 have been presented. Both primary and secondary data have been used for the mapping and monitoring of land use land cover. The primary data have been obtained from the actual field survey for site identification. Landsat images of three different dates from the last three decades were collected from Earth explorer (USGS). Different operations related to acquiring images were performed using Arc-GIS 10.2 software. This study shows the variability of land use and land cover in the city of Hisar during the last ten years. The study reveals that there is a significant growth in the Urban area from 2000 to 2020 i.e 12.8 %, the agricultural land shows a decrease from 13.8% in 2000 to 10.8% in 2020, Baren land also shows a decrease from 16% to 11%, Planted trees also effected from rapid urban sprawl and decreased from 16% to only 2% in 2020, with the growing demand of water facility in the urban area, the artificial water body shows a growth of 0.59% in 2020. Thus, this study shows that the expanding urban influence in the surrounding has led to significant changes in different categories of land use land cover, under which there has been an increase in certain categories such as urbanization, whereas agricultural land, barren land, and planted trees show a significant decrease.

The pattern of the ECG rhythm and heart rate represent the status of the cardiac heart. If appropriately evaluated, an ECG may reveal information about numerous arrhythmia illnesses of the heart. Clinical ECG observation might take several hours and be quite tiresome. Furthermore, visual analysis cannot be depended on, and the analyst risks missing critical information. The huge variety in the morphologies of ECG waveforms is the most challenging difficulty that today's automated ECG arrhythmia analysis faces. The goal of this study is to look at ECG arrhythmia classification using a neural network and numerous characteristics. The architecture demonstrated in this study, which is built on feed forward back propagation neural network with Logistic regression based weight updation coefficient, may be trained to create ECG signals that are equivalent to real-world ECG signals. The results reveal that the weight adaptation technique improves the performance of all classification networks. The suggested approach is useful in classifying various arrhythmias.

It is obvious and natural to identify a person based on his/her biological trait and we do it since our birth. But now,we came to know how our brain does this activity. Iris has always been one of the most precious, trusted and accurate meansof recognition due to its unique and ageless features. Traditional approach of iris recognition system had to face a lot ofproblems in image capturing, pre-processing and extracting features. But now, Artificial Intelligence is the new boss and it has brought a revolution by introducing Convolutional Neural network, which possesses a resemblance to the human brain.This paper has made use of a CNN model namely Alex Net, for feature extraction, which automatically extracts a huge matrix of features. These features were passed in SVM (Support Vector Machine) classification to classify images among the correct classes. The proposed method preferred to use iris database obtained from IITD to conduct the research. Here, CASIA-Iris-Interval V4 and the UBIRIS.v2 datasets are used for the experimentation. With the Alex-Net, the possible accuracy rates on the UBIRIS.v2 and CASIA-Iris-Interval V4 datasets are 92.76%, and 97.05% respectively.

The J-PAKE is a password-authenticated key exchange protocol that is included in the OpenSSL library and is currently in use. However, a future quantum computer utilising Shor’s algorithm for determining the discrete logarithm has posed a threat to the J-PAKE which its security relies on the difficulty of solving the Discrete Logarithm Problem. An algorithm similar to the J-PAKE is presented here. In contrast to the traditional J-PAKE, It uses floating-point numbers to generate a shared secret. The decimal part is used for the one-way function, transforming the protocol into a post-quantum key generation procedure. This is based on the fact that there is currently no quantum computer algorithm that can reverse engineer the one-way function. Performance is compared with the original J-PAKE. Theoretical analysis suggests that the proposed protocol is quantum-safe and offers reduced communication and computation cost as compared to the original J-PAKE.

In the current era, machine learning algorithms has taken a major role in classifying the disease and this has achieved a better result. In this work, Parkinson disease has been classified using machine learning algorithms. The novelty of this work is to implement the dimensionality reduction method before applying the classification algorithms. The dimensionality reduction is done through genetic algorithms in the form of feature selection. The selected features are then taken as input for performing the classification of the Parkinson disease. In this work, Parkinson disease dataset has been used for the prediction purpose and the machine learning algorithms has performed better with dimensionality reduction method. The gradient boosting algorithm has achieved higher accuracy of more than 97% during the classification with dimensionality reduction method.

In this paper a novel encryption technique is proposed based on the (x,y) points generated by circle for securing data in the cloud[32] . In this research a new mapping method introduced to convert an plaintext value to a point on a predefined circle over finite field GF(p) using a map table. This mapping technique is very fast with low complexity and computation, easy to implement and for low entropy plain text data, mapping will results a high distribution of different points for repetitive intensity values. Encryption and decryption process are given in details with implementation. After applying encryption, security analysis is performed to evaluate the robustness of proposed technique to statistical attacks [11, 12]. The targeted application for this crypto system is on cloud data.

A path P is a fuzzy detour if its μ - length is maximum among all the paths between them.In this note, we present fuzzy detour blocks, perfect detour and quasi detour number with relevant examples. We further demonstrate the existence of a nested chain of vertex-removed subgraphs, each of which has the property of being a fuzzy detour block. Finally, we point out that all complete fuzzy graphs are perfect detours.

The intrusion detection is the one of the interesting activities for providing the security in the computer. The nature of intrusion detection is to give alert to the user before the attack happens. There are different types of attacks that are encountered nowadays. With the advent of IOT many attacks are occurred and many managements have lost the money. So, these problems can be solved by using the best intrusion detection techniques and the attacks signatures have to be maintained. The machine learning techniques are the one which helps to detect the intrusions. Different machine learning techniques can be applied in intrusion detection. The data sets available today should be updated to maintain their effectiveness. In this paper we will review different classifiers which helps to direct our research in future

Most small and medium-sized Indian rivers do not have adequate water storage arrangements to harvest the excess water during floods. Failure of monsoon, unpredictable rains, encroachments across the catchment areas, diversions or blocks in channels, and ill effects of rapid urbanization gradationally lead to the disappearance of these rivers. Under these circumstances, an attempt is made in this article to review various factors involved in rejuvenating the dying river-Kousika.

In recent times, the power grids are being challenged with different problems of power quality and voltage fluctuations, which are occurred due to large-scale Renewable Energy Sources (RES) integration with the complex grid network and load demand variations. In concern with this monitoring and control of the grid, state operation is very crucial. Smart grid security is provided with electronics control because energy demand increases daily, and electronics cost increases daily. It will cause an electricity burden on the government due to the significant electricity demand. Development of new energy sources is required with S.G. development for electricity demand fulfillment. After a few years of engineering ideas, the smart grid system developed the energy & fulfilling the demand.In this paper we have performed the Matlab simulation to analyse the performance comparison of different SE techniques for SG security analysis.

Adopting Nature inspired optimization algorithms for image processing is on the double growth in the last decade. Artificial Bee Colony (ABC) approach is highly potential nature inspired optimization method mimics the bee’s foraging behaviour. Moreover, the popularity of classification and artificial intelligence in different fields leads to the employment of ABC algorithm in upsurge. Notably, Early detection of breast cancer through digital mammogram images is essential as it is the one the most common cause of humankind cancer deaths. the aim of this comprehensive survey was to methodically analyse the effectiveness of using ABC algorithm in medical image enhancement, segmentation, and classification. This study firstly gives introduction of ABC algorithm and its basic mathematical and biological principles and operations respectively. Furthermore, this academic study summarizes the ABC applications on image segmentation techniques like Otsu and image classification approaches like Artificial Neural Networks (ANN). Finally, this far-reaching study come up with the challenges while exercising ABC algorithm in medical image processing, especially in mammogram images.

A set in a topological space (X,τ) is said to be semi pre open if A ⊂cl(int(clA)). In this paper we discuss about the some more results of group acting on semi pre open set, contra semi pre open and Almost contra semi pre open sets and related theorems. The notions of SO (semi open), βO (beta open) sets, CβO (contra beta open), ACβO ( almost contra beta open ) and βC (beta closed) functions are used to introduce and study a new class of function in this paper.

A cross-sectional study on 200 nurses working in private hospitals in Punjab was done using the simple random approach. To collect the data, a standardised questionnaire based on prior research was used. The study used partial least square-structural equation models (PLS-SEM) to examine the data. According to the study's results, acquiescent, defensive and prosocial silence behaviour increased as perceived organisational support decreased and vice versa. In order to discourage silence behaviour and improve mental health, managers and decision-makers of hospitals can assess and address organisational and interpersonal challenges.

A seizure is an automatic change in behavior caused by a brief disturbance in the electrical activity of the brain. There are various sorts of seizures; some, known as focal seizures, only affect a specific area of the brain, while others, known as generalized seizures, impact the entire brain.Aberrant brain activity that causes seizures or episodes of unusual behavior, emotions, and sometimes an absence of consciousness is the characteristic of the neurological disorder epilepsy. A common sign of epilepsy, characterized by an imbalance in the brain's electrical rhythms, is recurrent seizures. If the activity before the seizure requires the highest level of concentration, such as driving a car, such seizures may cause situation-based accidents. As a result, severe circumstances like this cause fatal injuries.So detection and prediction of epileptic seizures has to be done as early as possible so that we can save a person's life from danger. Detection of seizures from EEG is a tedious task for neurologists manually because of complex and unexpected patterns and varying morphology of seizures. Large-scale EEG data can be used by ML and DL algorithms to effectively diagnose various seizure disorders and deliver results that are appropriate for neurologists. CNN-Convolutional Neural Network is used to classify the seizure and read accurate patterns from EEG. Utilize EEG inputs to identify seizures using CNN architecture, comparing time- and frequency-domain performance. In this paper, the author achieved an accuracy of 94.48% and 60.3% in frequency and time domain respectively for the CHB-MIT Scalp EEG database from physionet.org and 98.5% and 96.89% in frequency and time domain for the Preprocessed CHB-MIT Scalp EEG database from IEEE Data Portal submitted by the author for all 24 patients.

Cloud computing is an online technology to provides computing resources (machines) to end-users on demand for running their applications over the internet. Applications hosted in a cloud computing environment may face fluctuating workloads. To deal with such fluctuating workloads cloud resources are allocated automatically to applications. Allocating cloud resources to applications in an automatic manner is known as Elasticity which can be implemented using auto-scaling. Auto-scaling can be implemented as a reactive or proactive approach. Cloud providers use Virtual Machine based or Container-based virtualization to host applications. Some of the factors that affect the availability of the application are computing resources and users accessing those applications. It is required to allocate/deallocate resources at the right moment, else failing to it can lead to SLA Violation which can result in cloud service user dissatisfaction, negative review for the cloud service provider, etc. During the literature study, it is found that reactive auto-scaling decisions are taken based on CPU utilization threshold. In this paper, we have proposed a reactive auto-scaling algorithm that uses application level (response time) and infrastructure level (CPU utilization) metrics together. This work has been evaluated and validated using our custom microservice-based application. The result shows that our approach improves 4% of SLA achievement and 3% in request processing during a simulation duration of 15 minutes.

The extract of seeds (Coiandrum Sativum) was used to prepare Cobalt oxide nanoparticles by green synthesis in an aqueous medium. The main advantage of using "Coiandrum Sativum seeds" as a stabilizer agent in the extract is that it gives nanoparticles long-term stability by avoiding particle aggregation. The particles have been examined using X-ray diffraction (XRD), energy dispersive (X-ray) analysis, transmission electron microscope (TEM), scanning electron microscopy (SEM), and (EADX). The image of TEM shows that the CoO-NPs have an average particle size of 28 nm, which agrees well with the XRD readings. The chemical spray pyrolysis method proved successful in producing Cobalt oxide thinfilm. X-ray Diffraction (XRD) analysis of the Cobalt oxide phase demonstrated that the Cobalt oxide phase was prepared. Atomic force microscopy (AFM) photographs of the topography of the film with grains smaller than 100 nm were used to describe the surface morphology. Cobalt oxide thin films that have been deposited and then annealed at 400 °C have a flat, smooth texture, making them perfect for optoelectronic applications.

On a macro scale, the issue of the spread of infectious disease threatens society having a significant impact on both human lives and the economy. The pattern of contagious diseases differs from region to region because of different reasons where the disease incidence data collected by the country is considered for containing the information. Even though the application of big data has widened its wings into the fields of marketing and earth sciences, still the area of public health remains dependent on conventional surveillance systems, waiting to utilize the fruits of a big data revolution. The need for a new generation big data surveillance system has risen to achieve the flexible, regular, and rapid tracking of infectious diseases, particularly during emerging pathogens. The prime advantage of RNNs is the availability of contextual information during the mapping of IO sequences. This examination work brings out an enhanced RNN model for anticipating infectious illnesses. Enhanced RNN produces Accuracy 94.08%, Precision 0.92 and Recall 0.82. The tool used for execution is Python.

Nowadays, Spam or known as unsolicited in E-mails has become a main problem and it is difficult to discover by self. Spam showed raise by internet users rapidly day by day. However, machine learning algorithms can ability to classification and detection unsolicited emails or non-spam to avoid harm or any risk during communication by email via organizations, companies, or personalities. Therefore, it is most important to detect fraud emails. In this paper we focused to give background about emails and spam. Next we compared machine learning algorithms in our work and we have obtained the Naïve Bayes algorithms are the better in accuracy and precision. Thus, we have made preprocessing that such as cleaning data, Data transformation, Data Integration, and Data Reduction for instance Stop words, Tokenization, and Stemming. However, we have applied the Naïve Bayes to the classification of either spam or non-spam.

The effect of self-healing has been studied on Normal Concrete (NC) with Ordinary Portland Cement (OPC) and by replacing Recycled Aggregates (RA) for coarse aggregates. For this purpose, recycled aggregate replacement percentage of 50% has been adopted and studied. To provide workability, the adopted water cement ratio was preserved at 0.45. The samples were examined for split tensile strength, compressive strength, flexural strength, and UPVT (Ultrasonic Pulse Velocity Test) at the beginning, after micro cracks were made, and after they had healed. Water absorption tests and Rapid Chloride Permeability Tests (RCPT) were performed initially, 70 percent preloading, and then after healing to investigate the durability features of self healing concrete. Chemical examination of the chemicals responsible for the healing effect was performed using FESEM (Field Emission Scanning Electron Microscope), X-ray powder diffraction (XRD) analysis and EDAX (Energy-dispersive X-ray spectroscopy). The test results indicated that recycled aggregates, upto 50% replacement for coarse aggregates can effectively be used as a self healing agent without compromising on strength and durability aspects.

In recent years, IoT-based wireless systems have seen rapid growth in a variety of industries. IoT heterogeneous machines, which are usually battery-constrained, must operate for longer periods of time in most applications in order to complete their assigned tasks according to end-user requirements. Furthermore, because of the increased contact rate, large-scale deployment results in a higher energy demand, reducing the lifespan of IoT products. As a result, achieving energy efficiency in such devices becomes a major challenge in order to reduce energy usage and prolong the life of the systems. It is, however, important to identify energy-saving strategies in order to maximise their effectiveness. In this paper, we look at a number of energy-efficient techniques that have recently been suggested for green IoT-based wireless systems. IoT-based heterogeneous WSN, to be precise, which is at the core of IoT technology. We conducted a thorough literature review of the strategies used to optimise energy usage and scheduling in smart homes in this paper. IOT energy efficiency is influenced by a variety of elements that have been extensively addressed. Additionally, we examined the application of edge computing and fog in IOT for energy efficiency.

Drones and other crewless aerial vehicles (UAVs) have grown in popularity in recent years, and their use is expected to continue to rise. These devices are being used in a wide range of businesses worldwide and are becoming more sophisticated. This technology is expected to be used in Japan, where natural catastrophes often occur, for tasks like assessing disaster sites and looking for evacuees. These gadgets may one day be utilized to transport medical supplies and food to those who don't have easy access to grocery shops in countries with declining populations, especially in locations with declining populations and persistent labor shortages. Although there is currently no proof to support this claim, there is growing worried that these gadgets may one day be utilized for terrorism or other illicit activities. A major worry was raised in April 2015 after a tiny drone carrying radioactive material accidentally dropped on the top of the Prime Minister's official house in Tokyo. Drones have been known to crash into large events or well-known tourist attractions. As a result of these incidents, Japan has passed regulations restricting the use of crewless aerial vehicles (UAVs) near sensitive regions such as national facilities, airports, and urban areas. The legislation also establishes standards and rules for the safe operation of these devices. As a result, overseas tourists who are unaware of the restrictions and users who actively disregard them continue to fly drones without a permit. As a result, the number of accidents and other challenges that occur yearly is rising. This study addresses machine learning algorithms for flying objects in the context of current research and performance criteria. It includes reviewing current research and the various performance criteria for evaluating flying things.

Computer systems are upgraded with the technology of AI (Artificial Intelligence) to enable the computer to think similarly to the human being to handle the situation safely. Many researchers are concentrating on discovering various algorithms based on AI. The foremost objective of the method utilized is to diagnose the risk of different diseases in a single person concerning the answers provided by them to the questions raised with the help of deep learning algorithms in the end-to-end encryption process. The ultimate goal of this research work is to identify the diseases in between the trained dataset rendered by making use of classification algorithms. The dataset has been trained with the CNN classifier approach especially focusing on the lung disease dataset. The performance of the proposed model is estimated in the scale of accuracy. The result obtained using CNN was 93% and tool used for execution is python.

During recent times the role of healthcare industry becomes inevitable in one’s personal life. Discovery of new diseases alarms the whole world with the recent addition of corona pandemic. Though patients are not considered on knowledge of technical complexities their perception cannot be ignored as increased competition and the necessity to improve quality are vital components in healthcare market. This study reveals the perception of patients on various dimensions of quality towards private healthcare services. The present study is descriptive research with the data collected using a survey questionnaire collected from 100 respondents and analysed using statistical tools. Finally, suggestions have been made to healthcare providers to improve their service quality to retain and attract more customers for sustainability.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early cancer identification has shown to be very beneficial for effectively treating the illness. Using current advances in artificial intelligence, it is possible to create a lung cancer therapy prototype without negatively impacting the environment. It will save time and money since fewer resources will be squandered, and less labor will be needed to perform manual tasks. We proposed LCP-ML Framework for early lung cancer prediction using ML algorithms. The dataset has been collected from the Kaggle repository. The raw images are denoising using a multi-layer perception (MLP) algorithm. The image histogram equalization has been utilized with CLAHE. The segmentation part has been executed with Robert Operator. Finally, the image classification has been utilized with Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), and Naïve Bayes (NB) algorithms. The experimental results indicate that the classification accuracy of the approach proposed in this research can reach 87% using the Naïve Bayes algorithm.

There are now a lot of mobile and wireless devices that can connect to the Internet. Cell phones and PDAs are two of the most popular ones. So, in this paper, we'll talk about how to use design patterns and Java-based software components to make embedded software that runs on the two devices. Some parts can be used directly in an embedded software system, but most parts have to be changed before they can be used again. The developers have to figure out where these components differ. In order to make an embedded software system that can be used more than once, we will find a few variation points with some variants and talk about some useful design patterns that can be used to implement variation points. So, we can change the way an embedded software system works by attaching variants to the system's variation points. We use the property container, the strategy, the decorator, and the model-view-controller design patterns. J2ME is the technology we are using for the parts. J2ME is a specification for making apps for mobile devices. It provides a similar environment as standard Java environment. The Spotlet programming for the PDA and the MIDlet programming for the Java phone are both part of the components programming for J2ME. Along with XML, J2ME can also offer XMIDlet programming, which allows XML-based applications for PDAs and Java phones to be dynamically downloaded and run.

An aberrant cell disorder called cancer/tumor causes uncontrollably dividing abnormal cells that eventually destroy body tissue. One type of cancer brought on by the unchecked expansion of malignant cells in the brain is the brain tumor. Future prognosis and treatment planning depend on accurate tumor segmentation and classification. In the beginning, X-rays and CT scans were utilized to check for brain tumors. The use of MRI was made possible by the use of CT scans, which offer a radiographic phenotype of the tumor and have been utilized to extract and analyze quantitative imaging data.

In this paper, we explain how light electric vehicles (LEVs) designed for the next generation of mobility can reap the benefits of a bio-inspired control algorithm. LEVs are expected to play a significant role in the development of the next generation of mobility. The solar parking lot provides residents with an alternative to the practise of relying on fossil fuels as the primary source of electricity. As a result, individuals now have the opportunity to make use of renewable energy. In addition to this, it sets a higher standard for the habitability of urban settings. The energy management system (EMS) of an LEV is responsible for supporting not only the battery charger but also the primary and secondary chargers. This is made feasible by the control techniques that are the fundamental building blocks of the architecture of the vehicle. It is demonstrated by the control algorithm that it is possible to implement and manage many sources of information.

Multi-class Support Vector Machine classifier (M-SVM) method is proposed in this paper to achieve high accuracy, MCC, Sensitivity, specificity, and F-score. The Fuzzy C Mean clustering and OTSU threshold value are used for segmentation, Gray Level Co-occurrence Matrix is used to extract better features after elimination of noise from satellite images by using Dual tree complex discrete wavelet transform (DTCWT). The extracted features are exposed to the infinite feature selection algorithm in order to recognize the required arrangement of elements to the proposed multiclass SVM algorithm for ice type classification. Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) images are considered as test images for the proposed method. The effectiveness of M-SVM is compared with Randem Forest (RF), Neural Network (NN) and other methods. From the findings, the proposed method increases accuracy, MCC, Sensitivity, specificity, and F score by 1.90%, 3.06%, 0.81%, 2.40%, and 2.53% respectively as compared with RF method; 9.0%, 23.62%, 2.54%, 11.37%, and 11.75% as compared with NN method respectively.

In this paper, the design and implementation of a multi-agent system for improved transient stability of multiple microgrids is discussed. The purpose of this discussion is to create an optimal load scheduling network for a smart grid that is also known as a micro grid. The load agent, the control agent, and the DER agent are three examples of the many different control architectures that are responsible for taking prompt action for the control of the micro grid. The Java Agent Development framework includes an open-source toolkit called the multi agent controller that is used for the creation of agents (JADE). MATLAB is the software that is used to carry out the simulation. Under normal, fault, and overloaded conditions, the MACSimJX interface is utilized in order to communicate with the multi-agent Simulink model. The proposed multi-agent-based controller successfully coordinated with the various loads in the micro grid to meet the requirement for continuous power supply to critical loads throughout all of the worst-case scenario of sudden load deviations.

Purpose of the Study: The primary purpose of this study is to enhance accuracy, sensitivity, precision, specificity and error rate for an Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) images. Methodology: K-Nearest Neighbor classification (KNN) method proposed and implemented in MATLAB for AMSR-E sea ice images to achieve high accuracy, sensitivity, precision, specificity and acceptable error rate. Main Findings: From the findings, the proposed method increases accuracy, sensitivity, precision, specificity and error rate by 12.1%, 79.9%, 10.0%, 10.0%, and 12.1%, respectively compared to non-linear SVM; 17.6%, 74.8%, 10.8%, 11.1%, and 17.6%, respectively as compared with decision tree method. Applications: The study results help develop concepts or theories for the earth observation system at Polar Regions and critical notes for global warming situations. Novelty/Originality: K-Nearest Neighbor classification (KNN) method is newly introduced on polar region images.

The most essential concept in generating food for people, and the backbone of our country, is agriculture. The production of agricultural land decreases year after year as a result of various disease affections. Our daily lives involve the usage of turmeric plant materials for preparing meals, treating illnesses, and other things. A variety of expanding factors affect both the quantity and quality of turmeric output. In addition to field trials, crop simulation models are commonly used as research tools to analyze the effects of various technologies. A viable substitute for artificial intelligence and an additional tool to the widely utilized crop production models are machine learning (ML) techniques. Automatic plant disease diagnosis is aided by machine learning techniques like Random Forest, Bayesian Network, Decision Tree, Support Vector Machine, etc. In this study, we will investigate many machine learning methods to create the turmeric grow and diseases detection.

This work presents a methodology for distribution system feeder reconfiguration considering DG with an objective of minimizing real power loss. The reconfiguration of distribution network is attained for loss minimization by using Loss sensitivity Factor method. Maximum Loadability Index method is used for optimal location and sizing of Distributed Generation. The method has been tested on IEEE 33-bus radial distribution systems before and after reconfiguration to demonstrate the performance and effectiveness of the proposed method.

Diabetes is a chronic disorder, characterized by low insulin production and high blood sugar levels in people of all ages. Diabetes, if left untreated, may lead to a variety of ailments throughout the body parts. Diabetic Retinopathy (DR) is a symptomless eye disease caused due to diabetes, where vessels present in retina of the eye are destroyed and wall of the vessel becomes weak. It is very important to catch the signs of Diabetic Retinopathy before it becomes too serious. Prolonged Diabetic Retinopathy will lead to blindness if left untreated and after that it cannot be reversed.So it is verymuch crucial to detect the diabetic retinopathy in the initial stage. Many of the present automatic diagnostic approaches make use of the decesion from the clinical practitioner. So, an efficient Deep learning and Machine Learning based method to classify the grades of diabetic retinopathy by segmenting different retinal lesions is proposed in this work because Deep Learning (DL) does automated feature extraction and it produces more accurate and potentially useful findings, especially in medical imaging. The methodology used in this paper provides both multilesion segmentation and disease severity diagnosis using an ensemble framework which is fully automated and computationally efficient and hence this method can be potentially included in CAD(computer-aided diagnosis) tools used for clinical practice.

Recently computer science has evolved to a level wherein it is being tried to develop an artificial human brain with the help of BCI (Brain computer interface) which can help the society in various ways like help the patients to remove hearing problems, develop artificial hands for disabled patients, help in curing depression using EEG signals and much more. In fact, using machine learning a lot of unconventional sources of energy like wind energy, solar energy etc have benefitted immensely throughout the years. This is leading to a reduction in cost, improvement in forecasts and powers the return rate of their portfolio. And the similar positive trend is expected to go up in coming times. This study uses deep learning to undertake a systematic analysis of EEG categorization, resulting in an EEG brainwave dataset with five people' mental states and just a minute session for each mental state category to train and examine multiple approaches. Relaxed, Concentrating, and Neutral are the three main categories of states. We classified the three possibilities based on a few mental states identified via cognitive behavioural studies utilising the Muse headband with four Electroencephalography sensors namely, TP9, AF7, AF8, TP10. Only 44 traits out of a total of approximately 2100 are required, according to the results. And applying ML algorithms in Wind Energy to get the weather reports, they were able to anticipate output 36 hours in advance and this helped them enhance their energy's value by 20%. It has been observed that in the past classifiers like Support Vector Machines (SVM) and Random Forests (RF) attained a total accuracy of around 87% only. From the exploratory analysis of deep learning methods like BCI, confusion matrix, classification report it has been perceived that the EEG brainwave dataset model capitulates a test accuracy rate of 95.027% while classifying responses rendered by the brainthroughEEG signals.

A graph G=(V(G),E(G)) is said to be a product cordial graph if there exists a function g from V(G) to {0,1} such that if each line rt is give the label g(r),g(t), then the number of points with label 0 and the number of points with label 1 differ atmost by 1 and the number of lines with label 0 and the number of lines with label 1 differ by atmost 1. In this case g is said to be a product cordial labelling of G. In this paper we investigate the product cordial labelling of circular ladder related graphs and we prove that the graphs such as CL_((n) ) ⨀ K_1,CL_((n) ) ⨀ K_2 and CL_((n) ) ⨀ (K_2 ) ̅,k- path union of two copies of circular ladder, path union of m copies of circular ladder all are Product cordial graphs.

India promised at the Paris "climate change" summit to reduce carbon emissions by 33–35% up to 2030. Transport and energy are the largest CO2 emitters. Electric vehicles for public transportation and large-scale renewable energy production are viable solutions. In this paper, a newly developed SWEG model is designed for the EV charging station to address the issue of grid power dependency and carbon emissions. The proposed system is developed for a public EV charging station located on highways and powered by solar, wind, energy storage units, and the grid. The city of Bhuj in Gujarat State, India, is being considered for the evaluation of renewable energy sources. According to the energy forecast throughout the day from the renewable sources, various modes of energy flow among sources and EV load are designed. The system is designed to charge a total of 137 electric vehicles at a charging station. The goals are to achieve continuous charging of EVs, to reduce grid dependency, to maximise profit for the charging station owner, and to make the charging station environmentally friendly. The energy sources' cost-priority based energy management strategy (CEMS) for the SWEG model has been developed to charge the EVs. The proposed methodology is compared with that of the PSO and GA algorithms. The outcome shows that PSO optimization gives the best cost compared to GA. The amount of grid power used dropped by 37%, resulting in a 27.86 kg carbon dioxide reduction throughout the day.

Considerable consideration should be given to the sentiment analysis of customer reviews when formulating a company's growth plan. As the internet has developed over the past decade, massive amounts of data have been produced across all professions. These developments have given people new platforms for sharing their thoughts on things, such as Google Reviews, Blog Posts, Tweets, etc. Sentiment analysis is the technique of statistically recognizing and categorizing emotions stated in a piece of text, specifically to discover whether the writer's perspective towards a particular thing is positive, or negative. Social media provides a robust platform for gathering and analysing customer input, which can be leveraged to expand business opportunities and better serve customers. Therefore, the reviews left by customers will be examined closely in this study. Kaggle data is used to complete this study. The data includes comments about the restaurant and the reviewer's attitude towards the restaurant. The customer review is in string format. A customer's positive opinion of the restaurant is represented by the number 1, while a negative opinion is shown by the number 0. Certain processing methods, such as the elimination of irrelevant information, are required for the review's string data. Later, the Machine Learning (ML) model will receive the cleansed data. As an ML model, we've settled on the Support Vector Machine (SVM). To decipher how customers, feel about a certain restaurant, a basic SVM and an optimized SVM with Particle Swarm Optimization (PSO) are used. From the finding, we found that PSO-SVM architecture achieves greater accuracy of 85.5% accuracy. We also measured our model's efficacy using a variety of alternative metrics. When compared to SVM, PSO-SVM will perform better on all metrics.

Protecting the environment from air pollution is the greatest challenges faced globally. The Environment is continuously affected by poisonous concentration in air due to human activities. As a result, quality of air is declining day by day with increase in air pollution like PM10, PM2.5, S02, N02, Ozone, Co, NH3. Machine Learning and Artificial Neural Networks are used to estimate the air quality index. Air Pollution data is available abundantly in Central Pollution Control Board (CPCB) of different cities. The paper emphasis on how the pollution data set of a metropolitan city is gathered to develop a forecasting model to predict air quality index (AQI). The approach also identifies the best fit neural network training algorithms converging at a faster rate with maximum predictive accuracy with minimum iterations. The paper also focuses on evaluating and comparing the performances with respect to, convergence time, error functions, corelation coefficient, number of epochs, and predictive accuracy of three different Neural network functions such as trainlm, trainbr and trainscg. The application of Neural network training functions on air pollution data set proposes decision making capabilities for government bodies to take necessary actions in smart city to predict AQI. The task is carried out using MATLAB tool.

Now days, the global approach of vehicle’s quality is important by its interior noise as well as vibration characteristics. NVH is a process that desires the integration of customer device confidence with the design and improvement process. Millions more automobiles have been sold as a result of significant economic influence that automobile comfort and design have had over the past century. The interior design of automobiles is the subject of extensive investigation. However, the level of noise within the cabins has a greater impact on the comfort and travel of the drivers than many other factors. Low cabin noise level is a comfort factor that is very important. Low frequency vibration, to which commercial vehicles are sensitive and which the drivers can hear and feel, affects how comfortable it is to drive in the cab. Detuning the vibrating system to remove any problematic resonances is a useful technique. The most efficient method, nevertheless, is to stop the noise and vibration at the source. The sound pressure level within the cabin is forecasted using a coupled vibro-acoustic FEM. It is possible to redesign the acoustic material that reflects the most sounds to raise the sound pressure level within the cabin. Every detail must be carefully considered with an eye toward natural modes, natural frequencies, and potential contributions to the overall sound pressure level and the vibrations that affect driving comfort. The final step in reducing noise and vibration in the design of the automotive cabin is to choose suitable acoustic materials.

Cellular traffic prediction is one of the key research areas for telecom companies to achieve resource allocation and scheduling; also big data plays an important part in cellular prediction, as traffic prediction requires traffic involving thousands of cells. Furthermore, recent research shows the great potential of adopting the deep learning domain to predict traffic. However, training deep learning models for various prediction tasks is considered a critical task due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2 as metrics; furthermore, MLHN efficiency is proved through comparison with the state-of-art approach.

Face Recognition (FR), is one of the biometric techniques which is used to recognize any given face image. Several mechanisms have been recommended for FR; however, in real-time situations, it remains very tedious. In order to differentiate people, a primary approach relies on several conditions like posture variety, illumination, facial occlusion. To resolve these issues in the conventional methodologies, the Deep Learning (DL)-centric Efficient Face Recognition System (FRS) using Z-Normalization along with the Moore Penrose-centric Deep Convolutional Neural Network (ZMP-DCNN) algorithm is presented in this paper. The input image is enhanced by using the BIG-Weber technique to enhance the contrast. Cost Function-grounded You Only Look Once version 3 (CF-YOLOv3) algorithm is used to detect the face as of the enhanced image. Then, to localize as well as to represent the face’s salient regions, the facial points are extracted using the Multi-Gated Supervised Descent (MGSD) approach. Next, the facial parts are segmented as of the detected face using the Angle rotation Adaptive Viola-Jones Algorithm (A2VJA). The significant features are selected as of the extracted features using the Newtonian Constant of Gravitation-based Grasshopper Optimization Algorithm (NCGOA). To recognize the face, the selected features were given as input to the ZMP-DCNN framework. A similar process of input images was done for the query images. The query image is recognized using trained outcome of the input image. Finally, performance of the proposed technique is compared with the existing frameworks. Thus, in contrast to the other mechanisms, the proposed face recognition system achieves superior performance.

Internet of things is an emerging technology in current date; hence the communication plays a crucial role. For the larger data rate at largest spectral bandwidth is required. The latency, efficiency and combination of multiple access techniques place very important role along with the antennas the transmit the data and received. In the advanced 5G communication, FBMC is one of the most used, configuring the method for flexible applications. This paper proposes the importance of FBMC technique, let us used to solve the problems independently. In the first step, The signal is undergone preprocessing by the technique of OQAM, using. Staggering. In the second step, the filter bank is used to sense assist are transmitted signal. In the receiver side, the FBMC receiver with the filter banks are used to analyse the signals. Before the processing of signal, post processing is conducted with 2 main Techniques which are multiplication by sequence and real value to complex value conversion. In the complete technique, the common problems that raised is narrowband interference. This paper Proposes MIMO technique using OFDM method for the transmission of data in a doubly selective channel, to overcome the narrowband interference technique. The result graph shows how the narrowband interference is better than the Co channel interference for the original signals used. The original signal is also reconstructed back after the addition and removal of narrowband interference.

This research paper deals with the modelling process of nanocomposites using Digimat software. Digimat is a nonlinear multi-scale material and structural modelling platform. It is a resourceful and innovative software used by CAE engineers, materials scientists and specialists working in the manufacturing processes of various composite materials to accurately calculate and predict the micromechanical behaviour of multi-phase complex composite materials and structures such as PMC, RMC, MMC, nanocomposites etc. The main objective of the study is to formulate a digital model of the nanocomposites that will help in virtually determining the conditions at which specimen failure starts. The specimens subjected to experimentation consists of cement mortar in cement-sand ratio of 1:2 reinforced with MWCNT and HNC in 4, 6, 8, and 10% by weight of cement. The specimens are subjected to Compressive, Split tensile and Flexural Strength testing in non-sonicated and sonicated (10, 20 and 30 minutes) condition. The same is modelled in the software. In the experiment, the specimen is loaded only until 20% breakage and the load at which the specimen cracks are obtained. These values are used for analysis in the Digimat software. Microstructure analysis was done using ImageJ software to determine the post break length of the nanomaterials and the same was used as inclusion size in the Digimat software. Values of stress, and Youngs modulus are obtained from the software and corroborated with the experimental results. The average percentage of error obtained in case of compressive, flexural and split tensile strength is more for non-sonicated specimens as compared to the sonicated specimens.

This article examines the seasonal variations in groundwater quality along the northern coastal stretch of Kerala using physicochemical and geophysical methods. During the pre-monsoon, monsoon, and post-monsoon seasons of 2018, thirty groundwater samples were collected from various geomorphological units to understand the groundwater quality and the effect of seawater intrusion. Physicochemical parameters viz., pH, electrical conductivity (EC), total dissolved solids (TDS), salinity, calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), sulfate (SO42-), chloride (Cl-), total hardness (TH) and total alkalinity (TA) were determined and results were compared with the drinking water standards of BIS (2012) and WHO (2006). Physicochemical studies show seasonal variations in the quality of groundwater in the study area. Subsurface resistivity layers have been identified in geophysical surveys. Among eight locations surveyed for electrical resistivity, two showed good groundwater quality. The fresh-salt water interface in the study area varies from 0.9m to 42.4m meters in the pre-monsoon season and from 38.9m to 41.5m in the post-monsoon season.

The huge increase of vehicles on our highways with increased numbers of heavy goods vehicles in recent years, has led to traffic congestion and higher delays and also could lead to a higher rate of traffic accidents. To overcome such situations and to improve the efficiency and safety of the highways, a good understanding of the traffic operations on those highways is needed. This paper presents a newly developed model to compute vehicles’ numbers and classify them and also to determine their speeds based on video cameras. For the purpose of this study, the new model has been built from scratch by using the PYTHON programing language. Several sets of field traffic data taken from different highway sites in Basra City were collected and analyzed. The collected data was used in the development and verification processes of the developed model. The experimental results are promising and showed that the proposed model can compute vehicles, classify them, and determine their speeds, with an average error not exceeding 3%.

progressive collapse is defined as the sudden load which lead to initial failure at local positions in the frames that may cause sequence reaction through the elements of the structure. seven story building is chosen designed under the gravity load as per ACI 318 Code. The building were analyzed to progressive collapse condition under two damage cases; corner columns damage, edge columns damage. The building is loaded as described according to General Services Administration (GSA) guidelines which require linear static analysis. The results show the variation of demand capacity ratio (DCR) of columns. The damage aspects in this research concerning the section size damage cases with 0% column loss (full damage), 60%, 80% of column section size. The GSA guidelines for atypical building with DCR values more than 1.5 indicate critical damage potential condition in the structural member(column). It is concluded that the effect of the reduction of column section size in specific plan A is decreased according to plan distance from the specific plan. The columns DCR exceeded 1.5 show damaged condition represent actual threat possibility with nomination C22,C29 and C23 in plans A and B which present mid column lower part of the frame.

Attacks on network infrastructure include attacks on the integrity and confidentiality of network packets, their destinations and origins, and attacks on network availability. A distributed denial of service (DDoS) attack poses a significant risk to service providers. A DDoS attack is designed to disrupt and limit legitimate users' access to services by flooding the target with enormous malicious requests. A cyber-attack of this magnitude would almost certainly result in massive economic losses for companies and service providers as operational and financial expenses rise. Machine learning (ML) approaches have been increasingly employed to counter DDoS attacks. Indeed, with ML approaches, many protection systems have been changed into smart and intelligent systems that can fight DDoS attacks. This paper presents a DDOS Attack Detection (DDAD) framework for detecting IoT DDoS attacks using ML techniques. The Label Encoding with standard scalar technique is used to preprocess the datasets. An improved firefly algorithm is used to extract the characteristics. A mixed ML Algorithm was used to determine the feature significance. Finally, classification was performed using ML algorithms such as RF, DT, SVM, and LR. The experimental findings are described in conjunction with several categorization metrics, including accuracy, precision, recall, and f-measure.

The main intent of this study is for analyzing how image processing filters denoise images of children with autism taken at different times. Digital images usually contain noise during the image capture process and must be preprocessed before they can be used for any purpose. These captured photographs are upgraded with the help of various preprocessing filtering approaches particularly mean, median, Gaussian and FABF filters. The analysis on the working of these images is validated against quality of the picture’s performance metrics such as mean squared error [MSE], peak signal-to-noise ratio [PSNR], and structural similarity index [SSIM]. It is clear from this work that the FABF filter gives superior results compared to other approaches of filtering namely mean filter, median filter, and Gaussian filter used in this work.

Drug-Drug Interactions (DDIs) are a leading cause of morbidity and treatment failure. The ability to forecast DDIs in order to avert negative consequences is a critical problem. Because there are so many drug-drug interaction pairings, it is hard to test all of them in vitro or in vivo. The high expenditures of DDI research are a restriction. Many medication interactions are caused by enzyme changes in drug metabolism. Cytochrome P450 enzymes are the most frequent of these enzymes (CYP450). Medicines that change the metabolism of other drugs might be substrates, inhibitors, or inducers of CYP450. To overcome the drawbacks and to improve the analysis, the proposed method is implemented. The proposed approach is mainly based on Drug-to-Drug Interaction based on deep learning approach. It is based on Spotted Hyena Optimization Driven Fuzzy Optimized Recurrent Convolution Neural Network (SHO-FORCNN-D2D). The SHO-FORCNN-D2D is executed using PubMed database into the platform of Python. The obtained accuracy of SHO-FORCNN-D2D is 96% and loss is 0.12 that are comparatively lower than other existing techniques.

In this paper the effect of the ratio of active medium effective beam area to the saturable absorber effective beam area (A/As) on absorption activity of saturable absorber hase been studeid . Fiber doped by thulium used as a active medium , whil Cr+4:YAG used as a saturable absorber .For simulation the system, rate equations model hase been solved numerical using Runge-Kutta-Fehalberge method. The simulation show the absorption activity of each level in SA reaches to converging in value and the optical bleaching state occurs at advanced time with the increase of A/As .

In this current era 5G technology used in system wireless access method for communication. In this research paper proposes and designs develop a lightweight NN for the recognition of facial expressions. Where a visual system that can be implemented into devices with a limited computational capacity to perform face expression classification while also reducing the number of parameters used in the classification process. Enhancing the accuracy of the findings including encapsulating the models into a consumable service that can be used by other collaborating systems. The genetic resource creation frameworks for the face expressional approach may be planned to meet the needs of various security models including criminal exposures, governmental confidential security problems, and so on. this model will clearly opposing effect on detection and classification. and obtains the good detection results by identifying images inside the dataset, which proves that the model designed in this paper is suitable for multi- classification of facial expressions. In general, comprehended a visual system that can be integrated on devices with low computing power to attain result classification and reduce a large number of parameters. After comparing with the algo in recent years, the accuracy of our algo will higher than theirs, and it has achieved good detection results in images inside the dataset after the experimental results.

Cancer is one of the deadliest and most ubiquitous diseases, causing a high number of fatalities each year. Lung cancer has the greatest fatality rate of all cancer kinds and primarily affects the pulmonary nodules in the lungs. Early lung cancer detection is crucial for improving patient survival. A radiologist can benefit greatly from an intelligent computer-aided diagnosis system for detecting, forecasting, and diagnosing lung cancer. Numerous researchers from around the globe have identified dozens of volatile organic molecules as biomarkers for lung cancer. The human body odour is influenced by eating habits, the environment, and a number of other factors, resulting in a great deal of diversity, and the sample sizes were inadequate, so the conclusions of different researchers were not consistent. Analysis of exhaled breath can be utilised in the early detection of lung cancer. CT scans are utilised for the diagnosis of lung cancer because they provide a detailed image of the tumour and follow its progress. Fuzzy logic, support vector machines, and statistical classifiers perform pattern matching and confirmation to improve the accuracy of the identification phase. In the following, we describe the methods and procedures utilised to identify lung cancer.

Objective- A software tool is developed to facilitate data entry and to monitor research projects in under-resourced countries in real-time. In this paper, we are working in three projects which have used ODK platform- PMA (1), CNN (2), PMA in Agile (3). Along with this, we are also emphasizing the fact that in the modern era, data collection can be done without paper-pen. Method- The e Management tool “Open Data Kit, (ODK) is written in the scripting language Python. The ODK is lightweight and uses minimal internet resources. It was designed to be used with the open-source software ODK. The users can easily configure ODK to meet their requirements, and the online interface displays data collected from ODK forms in a graphically informative way. The tool also allows users to upload pictures and laboratory results and sends text messages automatically. User-defined access rights protect data and privacy. (4) Results- We present examples from four field applications in Tanzania successfully using the e Management tool: 1) clinical trial; 2) longitudinal Tuberculosis (TB) Cohort Study with a complex visit schedule, where it was used to display missing case report forms graphically, , upload digitalized X-rays, and send text message reminders to patients; 3) intervention study to improve TB case detection, carried out at pharmacies: a tablet-based electronic referral system monitored referred patients, and sent automated messages to remind pharmacy clients to visit a TB Clinic; and 4) TB retreatment case monitoring designed to improve drug resistance surveillance: clinicians at four public TB clinics and lab technicians at the TB reference laboratory used a smartphone-based application that tracked sputum samples, and collected clinical and laboratory data. Conclusions The user-friendly, open-source ODK planner is a simple multi-functional, Web-based e Management tool with add-ons that helps researchers conduct studies in under-resourced countries.

Shoes should provide normal conditions during the entire cycle of socks. In the work, the main objects for research were: acrylic emulsion A-1, polyvinyl ethinyl dihydroxychlorosilane, industrial oil IA-20, penetrator and widely used polyethyl hydrosiloxane. Based on them, a composition of water repellents was prepared in various initial ratios. Water repellents were prepared by sequentially mixing the above materials at a temperature of 20–22, ° C for 3-4 hours. It seems to us that in order to determine the role of sorption and permeability of materials in the implementation of moisture exchange processes, it is necessary to compare the amount of human skin's moisture loss while wearing shoes with the ability of the material to absorb moisture and remove it through vapor permeability. The analysis showed that natural experimental hydrophobized skin in all treatment options, unlike the control one, is able to remove a significant amount of moisture from the shoe area due to its high sorption ability. However, a comparison of the human skin’s moisture loss with the sorption capacity of natural skin showed that sorption capacity alone is not enough to completely remove the released moisture. Since the possibilities of increasing the sorption capacity of genuine leather are extremely limited, the only way to improve the hygienic properties of shoes made from genuine leather is to less disturb the high permeability of this natural material as little as possible.

Sentiment analysis is a powerful data mining technique used to determine user’s opinion regarding an organization, event, person or product. Micro blogging sites & other social media platforms are gaining massive popularity now days & millions of people comment positively or negatively about various events person in their comments or parts. Micro blogging sites facilitate the public to share & review their opinion & file events in their posts. Sentiment analysis or opinion mining on micro blogging data mining feat. The proposed system is designed to perform sentiment analysis on micro blogging sites using machine learning techniques with variable weighted grading to various definitive words to be positive, negative or neutral. The positive or negativity of words in a tweet is scaled on a factor of 1 to 5. Assigning weight to determined sentiments allow more natural/fuzzy jurisdiction than a simple binary system. Also, a neuro fuzzy inference system is used to compute a grade for specific search keywords which may be on event/entity etc. Thus their presented tool becomes indispensable for anyone who is interested in public sentiment on a event/entity such as NGO’s, social frame works, marketing agencies, manufactures, art industries such as film industries, political parties etc, whoever is affected by public opinion.

Affective computing and artificial intelligence both rely heavily on facial expression recognition. Although human face expressions vary so often that existing techniques rely heavily on feature expression to recognize them effectively. Recently, Facial Expression Recognition (FER) has attracted a lot of interest as facial expressions are taken into account. as the informational medium with the quickest means of communication. Facial expression analysis, which is currently popular, uses deep learning techniques and provides a better comprehension of a person's thoughts or viewpoints. Compared to conventional state-of-the-art systems, the accuracy rate has significantly increased. Deep learning focuses on the stimulation of the organizational structure of human brain nerves that incorporate low-level characteristics and is currently a popular issue in the field of machine learning. In this study, we concentrate on using a conventional neural network to develop a facial expression recognition system that aids in the identification of deeper feature representations of facial expressions and therefore achieves automatic recognition. This article explores the most recent and current reviews in FER employing Convolution Neural Network (CNN) algorithms and provides a quick summary of the various FER fields of application and publically accessible databases used in FER.

The use of artificial intelligence (AI) in waste management has enormous benefits for the entire ecosystem and additionally diminishes the stress on the public health system given the substantial amount of waste being produced today. This project addresses this important social problem of solid waste segregation where an attempt has been made to classify plastics and non-plastics using SSD-MobilenetV2. SSD-MobileNetV2 uses depthwise convolution and pointwise convolution that make the various channels smaller and the residual block, decreases the amount of data on the network, making detection times faster than other models. The SSD model is trained on a custom dataset to obtain an efficient a deep learning model that can retain the spatial organisation of the input image at a lesser resolution while extracting semantic meaning from it. The research focuses on boosting the performance of pretrained models on a custom dataset and in this work, the deep learning MobileNetV2 is incorporated into SSD to achieve efficient and fast detection.

Video is actually a sequence of frames played in a continuous motion. Videos play a major role in day-to-day life. YouTube and Social medias mainly depend on the video data. The video should be short and precise, so that the people will show interest in watching them fully. A shot is a scene change from one to another. Specifically, a continuous recording done using a single camera is said to be a shot. In order to break a long video into chunks, it is necessary to find out the shot boundaries. By finding out the shot boundaries, it is easy to interpret a scene with the help of start and end frame of a particular shot. The scene change is broadly divided into two types: 1) Abrupt change and 2) Gradual (wipe, fade-in, fade-out and dissolve) change. In this work, a sliding window method is used in both machine learning and deep learning techniques to find out the shots. The performance measures such as, precision, recall and F1-score is used on the results obtained from standard BBC planet earth dataset.

Today's never-ending industrial expansion and the sharp rise in urban pollution have led to environmental and water contamination. The industrial sewage water gets mixed with streams, ocean and ponds etc. that causes several ailments in living things. Proper treatment of sewage can prevent these problems. Sewage treatment is the process of removing contaminants from industrial/domestic wastewater. Physical process (such as filtration, sedimentation and distillation), chemical processes (such as flocculation and chlorination) and biological process (slow sand filters or biologically active carbon) are used to remove contaminants so that water becomes safe enough to get released into the environment. By using these sewage processes we can not only avoid the diseases but also we can recycle the industry wastewater (sewage process) for further uses (garden, car cleaning, etc.). There are conventional methods for sewage treatment, which occupies more spaces as well as involve higher constructive cost. The activated sludge process is modified by the "fill and draw" Sequencing Batch Reactor (SBR). The complete treatment procedure takes place in one tank as opposed to separate tanks for each process. The benefits include less expensive plant construction due to tank reduction and smaller footprint compared to alternative treatment options. This paper aims to monitor and automate various stages of sewage treatment carried through sequencing batch reactor. In general, industrial automation refers to the use of technology for process control in a variety of operations, including chemical and petrochemical, power, iron, and steel, food and beverage, waste water treatment, and other plants. One of the main needs that reduces the need for labour is plant automation. Currently, SCADA systems and programmable logic controllers are widely employed in industry. In order to improve the efficiency of the sewage treatment process, the application of PLC is investigated in this study.

The purpose of this study was to focus on the statistical methods to modify data by utilizing the ANOVA test. This study has been focused on specific statistical software to model data and compares data by utilizing the ANOVA model. Through utilizing the ANOVA model, this study has analyzed the statistical differences between independent and dependent variables. This study has focused on the 0.05 significant levels while interpreting the data by utilizing the ANOVA test model. This study has focused on the p-value while interpreting the statistical differences between independent and dependent variables. This study has been identified that during data modeling through statistical models when the p-value is lesser of the level of significance (0.05), it indicates significant differences are identified between “dependent and independent variables”. Further, when the p-value is “higher than 0.05 values”, it indicates that a significant difference is not present between the two variables. Further, this study has identified the process of null and alternative hypothesis explanations. It has been identified that when the “p-value” is less than the “significance level” it indicates acceptance of alternative hypotheses. Furthermore, when the “p-value” is higher “than the significance level” it accepts the null hypothesis. Therefore, this study has been focused on each the factor of ANOVA model to analyze statistical data.

In this review a notable contribution is made to medical diagnosis to classify Parkinson which is the second highest occurrence neurodegenerative disease .The objective of this study is to discuss the various machine learning algorithms that are used for classification of PD patients from Healthy patient list. We demonstrate the work to compare the efficiency of ML models to identify the Parkinson disease. In this study various dataset from different sources have been considered for the analysis .Now a days there exist many classification methods have been proposed to detect the Parkinson’s Disease from prodromal MCI (mild cognitive impairment ) phases and Healthy Controls(HC).A study of 48 papers have been explained with different machine learning models with results comparison and found the limitation that most of the paper have used the MRI images as input without any image enhancement techniques.

Congestion is a common phenomenon in the field of computer and communication networks. Congestion will occur when the rate of incoming traffic to an interface exceeds the rate of outgoing traffic from an interface. The permanent solution for congestion is often increasing the speed of server or providing an additional server to serve the customers who is waiting in the queue. But, increasing the speed of service is not possible in many real life situations. To overcome this difficulty, a modified queueing model is considered in this paper. Arriving customers are served through an classical M/M/1 queueing system until the queue size reaches the predefined capacity. An additional server is added in the queueing system when the queue size is exceeds the predefined level. An explicit expressions for steady- and transient- state probability distribution for the number of customers present in the queueing system are derived. We also derive the mean and variance in steady state. Further, numerical illustrations are presented to understand the model behavior.

The Vehicular Adhoc Network (VANET) is a communication model that is used today that takes place between the vehicles for exchanging the messages or information about road traffic and road conditions, to make the driving comfort. In the VANET the security and decision making are the two important promising challenges and factors. This paper proposes the identification of Sybili attack and it is challenging task in VANET. The sybili attck, where the user uses the multiple identities at the same time and it broadcast the messages to other vehicle that were connected in the network. This attack is very difficult to identify in the vehicular network. In the previous work, the message verification approach is used, but the problem here it is, this approach not able to detect the attack of the Sybili in the vehicle in prior. To solve this problem, the Diffie Hellman Key algorithm is used for the Sybili attack detection. By using the secret key in the vehicles it reaches the correct destination in VANET. The simulation experimental results were shown and it identifies the Sybili attacks in Vehicular network efficiently and effectively.

Vaidya and Barasara proposed the concept of edge product cordial labeling. This motivates us to define a new labeling called -square product E-cordial labeling which is defined as follows: Let G(V,E) be a simple graph. A labeling f:E(G)→{1,2,3,4} with induced labeling f^*:V(G)→{0,1} defined by f^* (v)=∏▒〖{〖 f(uv)〗^2/uv∈E(G)}(mod 2).〗 The labeling f is called 4-square product E-cordial labeling if |v_f (0)-v_f (1)|≤1 and |e_f (i)-e_f (j)|≤1 where v_f (0) and v_f (1) is the number of vertices labeled with 0 and 1 respectively; e_f (i) and e_f (j) is the number of edges labeled with i and j respectively. A graph which admits 4-square product E-cordial labeling is called 4-square product E-cordial graph.

Recent studies shows that major earthquakes has end up in the severe damage of the structure, which led engineers in find of techniques which not only provide people’s safety, but also aspire to cut-down damages in the structures. In this article, a single bay, ten-story concentric braced frame with non-buckling bracing is developed to study the energy dissipation. Particularly this evaluation is done to check their stability after earthquake loadings. Traditional lateral force resisting systems include moment resistant frames and frames that are concentrically braced. This Traditional lateral force resisting systems is in use for long period of time, but the result obtained from these methods is average level. The insufficient stiffness of moment-resisting frames and the inadequate ductility of concentrically braced frames have propelled significant research efforts toward the advancement of novel lateral resisting systems that include more stable hysteretic behavior, suitable ductility, control of damage, and energy dissipation capability. Conventional Moment Resisting Frames (MRFs) become softer and as a result energy dissipates in higher amount under seismic design analysis. There is a linked damage in important areas of the key structural members as a result of the earlier complaints. This collision could cause serious damage and long-lasting tremor following the earthquake. To prevent the drift, a self-centering damage evasion initiative known as Resilient Slip Friction Joint (RSFJ) was created for steel Moment resisting Frame. The RSFJ achieves both the behavior of self centering and the task of energy dissipation, there-by resulting in seismic resistance. Added to that, an innovative energy dissipater, present inside the RSFJ as the central fuse, supports the building. Self-centering friction-dampening brace members, as opposed to conventional brace members, make the greatest use of the available characteristics in steel structures with concentrically braced frames. This results in minimising residual deformation and withstand earthquake load without replacing any of the member. The present research compares the performance of moment-resisting braced frames with and without RSFJ dampers during seismic activity. The purpose of this research is to determine whether RSFJ dampers for steel moment resisting brace systems are a good replacement for the current standard bracing systems with dampers and give performance advantages.

A patent is an exclusive privilege given to an innovation, which is a product or method and usually provides a new way of doing things or proposes a new technological solution to an issue. The processes for filing patent applications and applicant privileges differ significantly according to national laws and mutual agreements between countries. In these cases, the exclusive opportunity given to the patent holder is the ability to prohibit anyone from making, utilizing, selling, or sharing the patented technology without permission. Applications for inventions are going to a patent court. That is a federal or inter-governmental body in charge of awarding patents in a nation or territory in dispute. Patent office’s that grant or deny patent applications based on whether the application meets or does not meet the patent's criteria. The patent arrangement differs based on the patent office the innovation is registered with. However, these parts are contained in most records related to patents. The most important one is the segment on “claims”. Every Patent will have one Argument or more. The Argument specifies rights provided by Patent as well as particular creative features of innovation that need to be covered.

Colon cancer is a kind of cancer that originates in the large intestine (colon) and is highly lethal accounting as the third dominant reason for deaths related to cancer globally. Cancer detection at a premature stage substantially increases the probability of survival. With the increased use of image processing tools in medical imaging, several techniques have been developed to detect colon cancer at a considerably less cost and time consumption. In this paper a highly efficient hybrid optimization technique is introduced using Convolutional Neural Networks (CNN) with transfer learning for colon cancer detection. CNN with transfer learning is utilized to analyse the segmented images of the colon and classifying them as cancerous or non-cancerous. The CNN is used with the hyper parameters from trained models, like Alexnet. A new Remora Shuffled Shepherd Optimization Algorithm (RSSOA) algorithm was introduced by incorporating the Shuffled Shepherd Optimization Algorithm (SSOA) and Remora Optimization Algorithm (ROA) for updating the weights of the hidden neurons in the CNN. The devised approach is accessed for its performance by considering different metrics, like accuracy sensitivity, and specificity, Confusion matrix.

When it comes to human pose estimation in computer vision, this is regarded as one of the most fundamental and challenging areas. In the realm of analysing human posture, recent advances in machine learning (ML) methods have yielded impressive results. However, researchers are working hard to find ways to improve event classification and adaptive posture estimate. Classifying remote sensing events and performing adaptive posture assessment using our method is novel. To minimise noise and improve classification accuracy, Laplacian of Gaussian filtering (LoG), background subtraction, and body parts detection are used in the pre-processing stages of the procedure. Energy, Cartesian perspective, angular geometric and skeleton zigzag characteristics were then used to the multi-fused data. To improve classification accuracy, we use the Henry gas solubility optimization (HGSO) technique to choose the most important characteristics in our feature vector. SVM and k-NN are examples of expert systems that use ensemble classifiers like k-NN, Random Forest, and RF to evaluate a feature set. Events that span multiple Olympic sports and UT-interaction datasets were divided into categories for our analysis.The RF achieved 83.8 percent of accuracy and 82.3 percent of accuracy, SVM achieved 92.5 percent of accuracy and 91.67 percent of accuracy on these two separate datasets.

An efficient method of Pneumonia screening is the Low Dose Computed Tomography (LDCT) imaging modality but there are certain perils involved in the LDCT screening method. One such risk factor is the occurrence of FP in the tests, which leads to avoidable conditions such as surgery and complications. There are also chances of over diagnosis. The repeated screening for patients may increase the chances of developing secondary Pneumonia in a patient who are not affected by Pneumonia. Pneumonia detected at early stages can be treated more effectively with surgery. The survival rate of the patients diagnosed through LDCT screening test at an early stage was considerable compared to the patients diagnosed through chest x-ray screening test. When the Pneumonia is localized the five year survival rate is 56%. LDCT screening makes an early detection of Pneumonia which decreases the mortality rate by 14% to 20% among the high risk population. Diagnosis of the Pneumonia patient not only relies on the LDCT images but also on the report of the radiologist. In order to ameliorate the diagnosis of the Pneumonia by the radiologist there is a need to design a CADx for lung nodule classification. This CADx is an aid to the radiologist providing a second opinion on the conclusions made by the radiologist regarding the status of the patient‟s condition. In a nutshell developing a CADx for lung nodule classification will prove to be an effective tool for the radiologist to conclude their diagnosis with increased accuracy.

Alzheimer's disease (AD) is a leading cause of dementia and other forms of cognitive decline in the elderly and middle-aged, and the rising prevalence of the illness will place a strain on healthcare systems. Due to its rapidly ageing population, China now has more Alzheimer's disease patients than any other country. Therefore, it is of paramount importance to find a way to make an early and accurate diagnosis of Alzheimer's disease and to take effective action. Also over the last few decades, many automated technologies and approaches have been created for the diagnosis of Alzheimer's disease (AD). In order to mitigate the condition's effect on the patient's mental health, there are diagnostic strategies that prioritise speed, precision, and early detection machine learning and deep learning have significantly improved the diagnostic performance of medical imaging systems for Alzheimer's disease. However, the existence of highly correlated anatomical features of the brain poses a significant challenge to multi-class categorization. Nevertheless, the vast majority of deep learning models fail to deliver acceptable results in real-world situations. So in order to tackle this situation, we have built an ensemble classifier to improve Deep CNN's learning outcomes by fusing two models, CNN and VGG-16, into a single model. Based on OASIS dataset, we've gathered 8,980 MRI images to test our suggested technique. According to our research, we have observed that an ensemble based model (CNN+VGG-16 Net) outperforms individual deep learning models (Traditional CNN and VGG-16 Net).

Most engineering structures and equipment vibrate, and the dynamic behaviour of these structures must often be taken into account during design. Because of this, the study of vibration, which examines the vibratory behaviour of bodies, is becoming increasingly important in various engineering applications, including nuclear reactor technology and aviation. A minimal amount of research has been done on visco-elastic triangular plates compared to the fields of rectangular, square, and circular plates. In this work, the free vibration of a visco-elastic triangular plate with a simply supported boundary condition and parabolically increasing thickness is studied about taper constants. The Cartesian product of parabolic variations is considered the two-dimensional thickness variation. The frequency equation is derived using the Rayleigh-Ritz technique. The findings are displayed graphically.

Indium Phosphide (InP)-based hybrid plasmonic waveguide for nanoscale optical confinement and long-range propagation at wavelength 1.55 μm has been proposed in this paper. In this structure, the graded hybrid SP structure (GHSP), the InGaAsP layer is designed as a ridge with a gold cap by intervening a ridge of the InP layer between them. InGaAsP lattices are suitable for that of an InP substrate. In1−xGaxAsyP1−y was graded in refractive index by changing the mole-fraction. Using a graded index, the confinement and the propagation length can be improved compared with the conventional one. Propagation length of 40 µm is achieved with better confinement as compared with the traditional hybrid ones. Changing only the y parameter with constant x, we get a propagation length greater than the inverse case, that is, changing only the parameter x, as well as better than the base case when the scaling is not included in the design. This is in parallel with good results in the mode effective area (A_eff) and Figure-of-merit (FoM) for the same condition of changing y which expresses a good confinement condition. COMSOL Multiphysics was used to simulate the proposed graded index hybrid surface plasmonic waveguide (GHSP).

The resource integration system of the industrial clusters, which is powered by the internet of things, has gradually transitioned into a research centre over the course of time. The Internet of Things platform has also been fast cementing itself as the foundation for the cluster that is developing in the tourism sector. This article develops the paradigm of industrial clusters by making use of the platform provided by the Internet of Things, beginning with the concept of conducting specialised research inside the tourism sector cluster. The Internet of Things tourism industry cluster now has its own resource integration mechanism as a result of the merger of the resource integration game model. The simulation research is carried out with the assistance of SPSS, and the information platform for industrial clusters is constructed with the assistance of the information management paradigm that is used in the Internet of Things industry. According to the findings, the method of resource allocation that is utilised by the platform for the Internet of Things generates an overall greater amount of industrial revenue than the conventional method of resource allocation and also discusses Connected Devices in Worldwide. Furthermore, this mechanism for resource allocation integrates the resources of the tourism industry cluster in an effective manner. The theoretical framework is presented in such a fashion as to seem like this. The fourth industrial revolution will have an immediate and considerable influence on the tourist industry, which is often regarded as one of the most dynamic businesses in the world. This point is driven home by the role that the internet has had in the development of this sector as an economic sector. It is essential to have the Internet of Things (IoT) and other disruptive technologies in order to regulate and comprehend this business, particularly the supply and demand dynamic. The wide variety of applications for the Internet of Things that can be found in the travel and tourism industry is a key factor in determining how competitive the private businesses that are involved are, as well as the areas that are evolving into Smart Destinations as a logical progression from Smart Cities. These areas are shaped by the travel and tourism industry, and Smart Destinations are a logical progression from Smart Cities.

The purpose of this piece is to examine the development and shifts that have taken place in the travel and tourism industry on a worldwide scale. The travel and tourism industry is often regarded as both one of the most important and influential economic subsectors on the whole world. It helps to preserve both social and economic growth, in addition to advancement, and it contributes to the upkeep of a peaceful environment. Because of the nature of this sector of the economy, job opportunities are available to hundreds of millions of individuals all over the globe. In certain island economies, the travel and tourist sector not only provides the majority of jobs, but it also acts as the only source of employment. This is because the international economy relies almost entirely on tourism. The objective is to be of assistance in the process of developing economies that are sustainable. The travel and tourism sector is very diverse due to the fact that it is comprised of millions of different firms and people. These businesses range from some of the most recognisable brands in worldwide travel to some of the smallest tour guides and innkeepers in the world. When we cooperate, we become a powerful force, and when we speak with one voice, our opinions are heard at the highest levels of society and government.

As a consequence of the ongoing requirement to adjust to shifting circumstances, the clothing that is used in the tourist industry has been significantly improved. When it first came out, the internet and all of the advances that followed it triggered a total shift in every industry. The Internet of Things is now at the point where it may have a significant effect not just on commercial enterprises but also on tourist destinations. The goals of this article are to: (1) determine the effects that the Internet of Things will have on the tourism industry; and (2) present a strategy for streamlining the sector with regard to visitor mobility. Doing so will allow for a more accurate evaluation of the experiences that tourists have while on vacation. There are a large number of people involved in the whole tourist circuit, which indicates that tourism is a big industry. There are several opportunities in this line of work to engage in conversation with a wide variety of people and settings. The most recent technological advancements have the potential to serve as the unifying factor that binds the whole journey together and provide a solution to the challenges that are presented by the collection of trustworthy data in the travel and tourism industry. The research is organised into three parts: an introduction, a discussion of how the Internet of Things will affect various segments of the tourism industry, and an examination of the implications of IoT in various Smart Devices, which is followed by a model recommendation. The final part of the study is a suggestion for a potential solution.

Different damages and air pollutions caused by chemical gas leaks from industrial sites and automotive exhausts, demand for gas monitoring method has recently increased. Because of the production of toxic gases by industry, vehicular emissions, and higher concentrations of dangerous gases as well as some matter in environment, air is becoming increasingly polluted. This research proposed novel technique in air quality analysis by environmental gas sensing by semi-conductor type gas sensor using micro electromechanical systems (MEMS). The monitored data has been collected based on IoT module and processed for classifying the air quality based on monitored environment data. The classification of monitored data has been carried out using multilayer convolutional perceptron neural network (MCPNN). Based on a sensor simulation model, we offer a characterisation scheme for analysing the resilience of several machine learning models for ambient gas sensing. Behaviour of semiconductors, heights of surface barrier potentials, and gas sensing properties of sensing layers has all been studied. Interactions of humidity with semiconductor oxides are studied in order to correct for its effect on gas detection using an algorithm.

A vital role will be played by the “Internet of Things” in the future. Everything linked to the internet has an individual identity and is capable of interacting with other devices on the internet, thanks to the global network architecture that underpins the “Internet of Things” (IOT). A "thing" in the “Internet of Things” paradigm is one that has been combined with electronic components such as software, sensors, and a network connection. A number of communication infrastructures may be used to transmit data between these "things" and servers, centralized systems, and/or other linked devices. The internet is used to transfer IoT data from sensors, nodes, and collectors to a cloud server. Customers, healthcare professionals, companies, and the government all rely on IOT devices in their daily lives. It is predicted that there will be 31 billion IOT devices in operation worldwide by 2020. Vulnerabilities related to the IOT are on the rise as the number of devices rises. Based on studies, the IOT paradigm faces new security problems due to broad use and integration with new technologies. This article contains future research on IOT security issues and open questions.

In this paper, evaluated the risk associated with the fireworks are examined based on the consideration of production, operation and risk activities. The examination is based on the consideration of risk activities in the firework industries in the Virudhunagar firework industries in Tamil Nadu through the examination of the risk activities in the firework industries between the years 2001 – 2016. Behaviour-Based Safety (BBS) is an information management approach employed for improved workplace safety through observations in safety measures. BBS is focused on the attention of the workers based on their own and peers’ safety behaviour process. The primary goal of the BBS is to increase the organization’s employee safety in industries. The concept of ABC is termed as Antecedent – Behaviour – Consequences. The firework industries in Tamil Nadu are subjected to fatal incidents that lose the huge life and it is necessary to develop an appropriate scheme for the workplace safety. The correlation examination carrying and weighing are highly related to the chemical mixture and lighting. Through the derived examination, the ABC model is implemented for the improved safety in the firework industries. This paper presented a BBS model with the ABC analysis approach for the increased risk in the firework place of Virudhunagar district in Tamil Nadu.

In this research article, an analytical study on determining the functional potentiality of non moment resisting braced frames to minimize earthquake induced damage has been presented. A self-centering damage avoidance idea called a Resilient Slip Friction Joint (RSFJ) was created for steel Moment Resisting Frames employing a unique Resilient Slip Friction Joint (RSFJ). The RSFJs make it possible for a gap to open up in the connection during loading, and they re-center the system when loading has been completed. An analytical model was constructed in order to precisely anticipate the behaviour of this system in terms of its moment-rotation relationship. A single-bay ten-storey concentric braced frame with diagonal Non-Buckling bracing is chosen for studying their effect during earthquake loadings. This study aims to analytically obtain the seismic performance of non-moment resisting braced frames with and without RSFJ dampers. The frames were modelled and designed in ETabs software and a pushover analysis & time history analysis was performed.

In the era of an energy crisis, reliable and robust Solar Radiation (SR) prediction has become an integral part of thermal systems for renewable and clean energy production. Machine learning models are widely used as a precise effective solution in SR fo9rcasting. In this paper, Artificial Neural Network (ANN) is employed to predict hourly SR. An open-source dataset available from the NASA hackathon task is used for evaluations. The models' effectiveness is evaluated using several evaluation metrics, and its efficiency is compared with Logistic Regression (LR) and AdaBoost models. The outcomes indicate that ANN model has a higher ability for predicting SR than LR and AdaBoost in the training and test stages.

The sites of construction are considered hazardous as occupational injuries as well as casualties have been recorded at higher level. It has been observed that these accidents in larger manner have occurred more importantly in the sites of residential & commercial building. As the labours of construction were most important and dynamic elements in an industry of construction & the cost of them have represented almost half of cost of construction in overall manner. This condition means improving the workers’ ergonomic performance have become a good level of target for many companies of construction. Hence, for assessing the environmental effects on the worker’s performance, the extension of the particular risk which has been associated with tools/equipment’s manual handling, equipment & tools’ details & other kind of issues which has been related while the workers have been engaged in the jobs of construction in this particular context, ergonomic principles in an appropriate level which has been needed to be applied for minimizing the hazards of health for the purpose of ensuring the workers’ comfort & safety. This study endeavors to recognize and explain the human factors influencing health and safety performances in Construction industry. Survey questionnaires were used with delivery and collection methods in the present study. Construction practitioners who wish to improve their firms' safety records may find the results of this study useful.

As the number of cyber threats and attacks increases, there is a growing demand for network attack analysis today. Due to the scalability and adaptability of its internet-based computing resources, cloud computing is favoured by businesses around the globe. Protecting hosts, organisations, and data from increasingly sophisticated digital threats is a top priority for scientists, who are focusing more and more on the security of cloud data. Over the past couple of decades, researchers have experimented with the framework for Intrusion Detection (ID), resulting in a plethora of methodologies. In the future, however, these methods will not be sufficient for the intrusion detection framework. The objective of this research is to employ ensemble model of Effective gradient boosting decision tree (EGDT-boost) performs a classification to determine whether or not an interruption in a framework has occurred. This model created an ensemble classifier using an Decision tree and a Gradiant Boosting classifier. The gradient boosting methods improve the performance of the Decision Tree classifier by reducing the number of detected errors. This article examines the proposed classifier and compares it to established various classification techniques. In terms of Precision, Recall, F-Measure, and Accuracy, the proposed model produces superior results compared to existing methods.

A popular technology, integrated e-pharma, enables patients, medical practitioners, and pharmacies to combine their systems in order to provide more effective service delivery to their respective audiences. When it comes to IT infrastructure, cloud computing has emerged as a flexible, scalable, and low-cost alternative. The integrated system contains patient data, medical data, and pharmacies, all of which are accessible through a single login. The integration of health-care institutions, pharmaceutical systems, and insurance systems results in sensitive data being accessed and stored in the cloud, and this data must be protected in some way or another. This publication proposes a scalable and strengthened key aggregate cryptosystem (SSKAC) that is capable of providing optimal security for health care data in the integrated e-pharma system while maintaining scalability. This technique addresses the issue of sensitive information leakage by building a secure integrated e-pharma that can save, retrieve, and preserve health and medical prescription data in an encrypted format. This technique is applicable to both personal and commercial information. When encrypting data, the double encryption method with cypher text-id named class is used to increase security by using the same cypher text twice. The key owner possesses a secret hidden key that will be used to extract the private key from many classes on a single computer. The patient is provided with a single aggregate key, which is then utilized in the decryption operation to protect his or her identity. The encryption text is created dynamically using Elliptic curve depending on the amount of data being processed at any one time.

This paper intends to study Algorithms created for real-time object identification applications to increase detection accuracy and energy efficiency by combining Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform. Object detection has been a research community focus for many years and has achieved substantial breakthroughs in its trip thus far. There is a vast array of applications that might benefit from greater advancements in the field of object detection. The authors of the current study implemented real-time object detection and worked to increase the detection mechanism's accuracy. We utilized the ssd v2 inception coco model in this study because Single Shot Detection models produce much superior outcomes. A dataset of more than 100 raw photos is utilized for training, and labellimg is used to produce xml files. The produced tensor flow records are sent into training pipelines that employ the suggested model. OpenCV collects real-time pictures, whereas CNN executes image convolution processes. The real-time object detection achieves an accuracy of 92.7%, which is an improvement above some of the previous models provided. The model identifies hundreds of items at the same time. The suggested methodology greatly improves on existing techniques in practice in terms of object detection accuracy. There is a large dataset available to assess the correctness of the suggested model. The model might be beneficial for a variety of item detection applications, such as parking lots, people identification, and inventory management. CNN (Convolution Neural Network) is a subset of deep learning algorithms that can take as input a sample picture and execute convolution operations to extract characteristics from the image and distinguish one object from the others. Rajaraman et al., 2019; Alganci et al., 2020). Consider tracing our misplaced phone in an untidy and cluttered residence to examine the application domain of machine learning systems. It appears to be a cumbersome and frustrating task for anyone. It needs only a few milliseconds to track the Location of mobile. Well, this is precisely the power we can harness from these amazing object detection algorithms, which are at the bottom of heart the deep learning algorithms. The current research work focuses on proposing an object detection model that can take input from the web camera, find location of the object through webcam, and classify object on screen for its appropriate category.

Medical care industry continually faces many issues, for example, patient information access, drug capacity log, clinical records, or logs these are a couple of the numerous issues medical clinics and other clinical establishments should manage. Medical services industry should offset patient consideration with data protection, security. The medical services industry faces significant issues like, putting the patient at the middle, security and access, culmination of clinical data, cost, production network the executives, drug records. Clinical record keeping has developed into a study of itself, despite the fact that the conventional technique for putting away information over a concentrated data set can be hurtful as referenced in the segments above, it very well may be inclined to hacking or even a solitary point disappointment. One more issue with putting away clinical information in these data sets is that, when these data sets need an update in programming every one of the servers are transiently down until the updates are done. This little window can prompt be extremely deadly as medical services is an all day, every day work. Block chain innovation and digital forms of money are being promoted as the "arrangement" to issues in a wide range of, unique areas all through different ventures. In this paper the objective is to recognize block chain innovation applications in medical services industry by conduction a precise writing in view of which a system will be proposed. The commitment of block chain stretches out into the medical care industry permitting a change of the ongoing framework and its utilization of data innovation.

Cloud computing has become an active area of practice and research over the last few years. Over the last years, the adoption of cloud services such as Software-as-a-Service (SaaS) models had an exponential surge. SaaS delivery model has evolved into one of the three leading categories of cloud services, besides Platform-as-a-Service (PaaS) and Infrastructure-as-aService (IaaS). SaaS is a software distribution model in which the costumers’ applications are installed and managed by an external service provider and accessible through the internet. In this way, companies are disposed of setting up and operating programs on their local machines or servers, which revokes the cost of purchasing hardware or installing software and even its maintenance. Today's cloud sector is becoming more and more dependent on it, which has given rise to both issues and solutions in plenty. One of the many difficulties in cloud computing is cloud migration, and it is essential to build effective ways to progress it through time. Researchers who work on data migration try to move data from various geographic areas that have troubled architectures, large data volumes, and short time windows.

Fibre Reinforced Polymer Composites (FRPC) are widely used for retrofitting of deteriorated Reinforced Concrete (RC) in structural applications. The deterioration of beams has occurred due to several factors such as corrosion, lack of bonding strength, seismic activity and ground motion. The rehabilitation and retrofitting are done using several techniques and FRPC is most efficient method because of higher strength/ modulus and low weight with higher corrosion resistant and design flexibility. The structural members are failed due to surface cracks and deterioration of concrete because of sudden application of lateral load such as seismic and wind forces. The polymer composites are possessed high stiffness, high strength, better fatigue performance and corrosion resistance. In this research, mechanical properties of epoxy/basalt fibre polymer composite surrounded reinforced concrete column are investigated. The basalt fibre is surrounded the reinforced concrete in three different layer thickness and the thickness of Basalt Fibre Reinforced Polymer Composites (BFRPC) are 2 mm, 4 mm and 6 mm. The Scanning Electron Microscope (SEM) is utilized for confirming the uniform dispersion of basalt fibre in epoxy resin. The 2 mm, 4 mm and 6 mm thickness BFRPC are subjected to flexural test, tensile test and hardness test to analyze the mechanical properties. The mechanical characteristics, include hardness, flexural strength and ultimate tensile strength of BFRPCs are higher than the basalt unreinforced concrete.

Natural fibers are sustainable in nature and one such fiber is spunlace, which have many favorable properties like soft, light, breathable and cool. It is also incredibly hydrophilic, absorbing more water than other viable fibers. Hence, the tissue is used to produce a non-woven fabric with spun bond technique by incorporating chrysopogon zizoniondes (Vetiver) extracts. So the finished fabric will be of the properties like soft, disinfectant and anti-allergy for babies rashes and it feels freshness with has natural fragrance of chrysopogon zizoniondes. The fabric material application is planned to be as wet wipes to enhance the skin health. Developed product is objectively evaluated (Anti-bacterial activity) and that was found to be good.

The sensitive and personal information size is increasing where the data collectors gather the information. Those information are processed and stored within the premises of cloud. The cloud environment is susceptible to vulnerabilities and threats. The information engrossed within the cloud is huge and the information may be confidential or personal that may suspected to breach or hack. This situation necessitated an effective security system for protecting the information from eavesdroppers and hackers. The confidentiality of the information’s are secured by cryptographic based outsourced model for the cloud. In this article, harmonic encryption (HE) for outsourced data in the cloud computing is discussed and different HE technique in the context of cloud computing elaborated with their significant insights. The cryptographic schemes are compared and contrasted in terms of task, technique and verifiability. Finally, this article depict the research challenges and also gives the future direction for the progression of research.

Evidence indicates that the expansion of city size, the concentration of economic activities and the migration of people from rural to urban results in urban agglomeration. The spatial attenuation of the modem city pattern and its development compositions affects theholismofthebuiltenvironment.Thistransformationaffectstheculturalcontextoftheexisting area. Urbanization improves the artificial consumption of resources and thus further creates an adverse impact on the emerging built environment. Scriptural interpretations would not satisfy the modern mind steeped in the benefits of western society, technology and culture.

It is pertinent to form a mental organization of a neighbourhood and its perceived delineation to consciously build a place identity. The study argues that a good neighbourgood is made up of a complex combination of built infrastructure with perceptible level of legibility and order. It explores the various attributes that builds this collective construct at the neighbourhood level. Five discreet sites with varied built and natural characteristics are chosen for the study and the characteristics were studied and evaluated. The outcome of the study will identify the parameters which contributes to construct the place identity and aid resource allocation for development projects in creating unique neighbourhoods with identity.The possibility and means of creating an active and vibrant city life is deliberated and deduced through the study.

In the race for development especially in the urbanizing part of the world, much damage has been done to the natural resources, culture, identity, health, and its consequences are rampant in the Quality of Life of the people. It is imperative that we build systematic tools to regulate the development in a manner it is conducive to life and co-existence of all living beings in the current complex territorial system of urban environment. Urban Planning as a discipline has been in vogue for several decades now, and the interpretations of the traditional descriptive regulations in most circumstances have not brought the intended structured change to the urban environment. The Form Based Regulations will prove to be a viable alternative in providing the requisite structure and framework for development considering its versatility and clarity it can provide in shaping the urban environment at all scales without loosing the merits of traditional building codes.

The results of cyclic loading on beam column (BC) joints employing PP+Steel fibre with variable volume fraction are compared to the results of standard T beam joints. The test examined the performance of the hysteresis load versus defection curve, displacement ductility, and energy absorption properties. Three external beam-column junctions were studied and results were collated with traditional concrete specimen. From the experimental outcomes, the conventional concrete and (PP1/2+Steel1/2)2.0 runs for four cycle and have the energy absorption of 514.06 kN-mm and 569.72 kN-mm. Also the maximum performance released by (PP3/4+Steel1/4)2.0 specimen runs for fifth cycle and absorbs the energy of 764.18 kN-mm.

This study investigate the behavior of reinforced concrete beams under static flexural load developed with mono fiber and hybrid fibers reinforced concrete. Hybridation is to improve the flexural performance of simply supported reinforced concrete beams. In this investigation, five different mixes are investigated under flexure. Two mono fiber reinforced concrete beam with Steel fiber and Polyethylene Terephthalate (PET) fiber fiber of 2.0% volume fraction mixes. Hybrid fiber reinforced concrete mix with recycled PET bottle fiber of 0.5% hybrid with 1.5% of steel fiber, PET fiber of 1.0% and steel fiber of 1.0% and PET fiber of 1.5% and steel fiber of 0.5% are the other three mixes under investigation. The flexural properties of concrete beam without fiber reinforcement is used as the base mix for comparison. From the results, it has been studied that hybrid fiber reinforced concrete beams performed better than the all the other mixes.

An increase in global warming potential and other environmental concerns are demanding new environmentally friendly refrigerants. For investigation of system performance of newly developed refrigerants, the convective heat transfer coefficient in two-phase flow for both boiling and condensation is required. In the literature, numerous works have been done on in-tube condensation of various refrigerants and it is noticed that most of the works are based on conventional test rigs involving dual refrigeration arrangement without a compressor for the in-tube condensation analysis. This work is mainly focused on developing an innovative low cost highly efficient in-tube condensation test rig for flow condensation analysis of refrigerants. The in-tube condensation test rig is successfully fabricated, and the experiments are conducted for R134a refrigerant. The experiments are conducted for mass flux varying from 150 -320 kg/m2s with different vapor qualities ranging from 0.3 to 0.9. Further, the results obtained are validated against MM Shah’s unified correlation and the comparative results are also presented it is observed that the experimental results are in good agreement with that of Shah’s correlation results.

Technology development has been a continuous process and multimedia transmission in the public network has been advancing; it demands the security for the data being shared. Image is one of the very common media in the move since decades and various encryption techniques like AES, DES, RSA, chaos technique etc are proposed to secure the image content. Encryption techniques act on different parts of the image like Least Significant Bit (LSB), entire bit stream, selected rows and columns etc. The implemented encryption techniques are analysed with various parameters like Peak Signal to noise Ratio (PSNR), Mean Square Error(MSE), histogram analysis, etc. A brief discussion of such recent techniques has been presented in this article.

In this paper, Laplace transform method is applied to solve a linear initial-value Neutral delay Differential equation of second order and is verified using an example.

Air pollution is garnering significant attention as a possible hazard to human health. As a result, ensuring adequate air quality performance becomes a pressing public health priority. Governments and communities are getting more worried about air pollution, which has detrimental effects on global human health and sustainable development. Today, the most popular technique for estimating air quality is CNN. However, these methodologies provide poor results, compelling us to predict air quality using deep architecture models. This study used the ARIMA model and regression technique to construct a deep learning approach for predicting air quality categorization (AQC) in big data. For final categorization, the Linear Regression approach was used. Using a Deep Neural Network, a substantial prediction model is created. DNN can analyze and remember consecutive data over time, such as daily data on air quality. Unlike typical time series prediction models, this model can predict air quality at every station at the same time and exhibit seasonal stability. The supplied models outperformed the suggested strategy in forecasting air quality. This research identified PM 2.5 characteristics for using real-time sensors with IoT. People are becoming more aware of the amount of air pollution in areas such as hospitals, schools, and other public spaces to predict the level of PM2.5 pollution.

A wide variety of applications are now available as a place on the Internet of Things (IoT), and it is are classified as Low-power and Lossy Networks (LLN), and their devices have significant energy, memory, processor capabilities, and radio range constraints. The Low power and Lossy Networks (RPL) have permitted proficient end-to-end and bi-directional communication among hundreds of sensors, intelligent devices, and actuators, which helps connect resource-constrained gadgets in multi-hop IoT environments. RPL is built to address the primary issues that LLNs face, namely energy holes and huge energy utilization problems. However, RPL is experiencing significant load balancing and congestion issues, resulting in a poor Packet Delivery Ratio (PDR) network. This study proposes grid-based clustering for RPL, a lightweight scheme that organizes nodes into consistent clusters and transmits packets over a unique grid environment to tackle this challenge. Furthermore, compared to the existing data transmission technique, GC-RPL has reduced the quantity of energy used in the nodes, owing to the network enforcing fewer control packets.

In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location information of the node instead of global topology to transmit the data. The WSN routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirement an effective change is made in the data transmission environment and network topology. A cluster-based routing mechanism is initiated, and it utilize the optimization-based cluster mechanism which in turn reduces the consumption of energy and maximizes the throughput. The process of cluster head selection is attained using Artificial Fish Swarm Optimization and the routing is done using bacterial foraging algorithm. The objective function of the proposed approach provides an effective routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range. The proposed technique is compared with the existing approach and from the results, the energy consumption is minimum for diverse nodes.

The system of delay differential equations which has its delay in highest derivative are known as neutral delay differential equations (NDDEs) which is found in many applied sciences branches which plays a crucial part in the modelling of real-world occurrences mathematically. Some methods were complicated to find an analytical solution approximately. In this study, using the generalized Lambert W function to construct the characteristic roots of the neutral delay differential equation with random delay of first order type and examples were derived to support the outcome numerically.

Alzheimer’s disease is an advanced varied neurodegenerative illness; it is the foremost reason of dementia in upcoming adult life. Detection of AD is significantly and rapidly growing in investigate field of Medical and Computer Science domain. Early prognosis of AD theatres a vital part in refining handling potentials and upsurges the existence frequency of the patients. Since, detection of AD in early stage is tough task and hence this paper is helpful to find approaches for initial finding of AD through segmentation and quantification of different brain tissues in Magnetic Resonance Imaging (MRI). This paper describes an exploratory architecture for VGG-16 Net CNN Model for Automatic Alzheimer’s illness finding and quantification using MR brain images. The proposed model is attaining the higher accuracy compared with outdated machine learning algorithms.

In today's world, technology is rapidly advancing. To stay up with this rapid technological advancement, graduates must have a solid skill set, including problem solving, effective communication, and the ability to quickly adapt to new technology, among other things. These talents will be impossible to teach using traditional classroom teaching methods. As a result, a successful education system must be built to suit these needs."Outcome-Based Education" is one such educational approach that has received a lot of attention from professors and scholars all around the world in recent years [1]. Students' self-learning takes precedence in the Outcome Based Education System above learning from what teachers have considered in the classroom. In outcome-based education, students are given goals to achieve by the end of each course, as well as goals to achieve by the time they graduate. In outcome-based education, lecturers serve as knowledge facilitators for students. In this paper, random forest based ranking algorithm is used to design the framework for classification of student performance based on OBE Framework. In outcome-based education, one of the most important inputs for determining the efficacy of a university's or college's teaching and learning activities is student feedback of direct and indirect method. The proposed system results are compared with traditional machine learning classification algorithms like LDA, K-nearest neighbor, CART, Naïve bayes and Support vector Machine. The proposed approach gets optimum accuracy compared with existing approach.

Conventional heat transfer fluid Therm500 has had its thermal conductivity improved with the use of BaO nanoparticles. Different concentrations of BaO:Therm500 nanofluids (from 0.001 to 0.006 g) are generated and characterised across a temperature range of 300 K, 303 K, 313 K, and 323 K. Studies using FT-IR spectroscopy on BaO:Therm500 nanofluids showed that there was no particle-fluid interaction. According to research on heat conductivity, this improvement may have occurred because of a combination of factors. As the temperature rises, the viscosity of the BaO:Therm500 nanofluids lowers, leading to an increase in the Brownian motion of nanoparticles and set convection-like effects, which in turn leads to improved thermal conductivity. Then, acoustic properties of Barium Oxide:Therm500 nanofluids are reported at four distinct temperatures, such as 300K, 303K, 313K, and 323K. For six different molar concentrations (0.001g, 0.002g, 0.003g, 0.004g, 0.005g, and 0.006g) of BaO nanofluids, density, refractive index and ultrasonic velocity values are measured and the acoustical parameters such as adiabatic compressibility (β), intermolecular free length (Lf), specific acoustic impedance (Z), Rao’s constant (R), molar compressibility (W), viscous relaxation time (τ), free volume (Vf), Gibbs free energy(ΔG) and internal pressure (πi) are calculated. Acoustical characteristics are used to investigate molecular interactions in nanofluid systems. The non-linearity of ultrasonic velocity with particle concentration has been observed due to weak particle – fluid interaction.

Urbanization is nothing but rapid evolution of metropolitan cities in terms of its growth, facility to the live hood etc. this urbanization is more benefitted to the country in growth of revenue. Generally, the natural water bodies like lakes, water streams and river cover the maximum portion of the earth surface. It not only provides the sufficient amount of water and it reduces the temperature effect on the environment. The physical as well as greenery cover of earth surface such as forests and Parks is also giving the maximum contribution in order to reduce the effect of temperature. Increasing in infrastructure buildings like residential, commercial any other types and increasing in number of vehicles, automobiles lead to more accumulation of temperature in urban areas. The natural water bodies, agricultural lands, buried land, forests and other green areas converted into pavement surfaces. This morphological change in urban land of natural land leads to more and more concentration of temperature. These impermeable lands turn s to absorbent of solar radiations and sunrays. It also further reflects to the environment. Because of these activities can cause Urban Heat Island (UHI).

Resources are an integral part of cloud. Resources are required to be allocated and deallocated for the tasks in an optimized manner such that the resources available are utilized to the maximum extent. In addition to that, the resources are to be allocated in such a way that the best resource is allocated to the task with higher complexity in order to speed up the execution time, thereby reducing the waiting time for the tasks in the queue. This paper discusses resource allocation with reference to CPU cycles and memory across multiple data center uniformly, by taking into account the task complexity and to achieve effective utilization of resources.

Wireless communication technologies have easily imprinted their significance into humans' daily lives through Internet of Things (IoT). Recent growth and application of IoT in the smart transportation system, smart cities and so on, possess thriving surface for various types of security attacks with societal, environmental and economic impacts. PUFs (Physically Unclonable Functions) are newly emerging primitive for security consisting of a simple architecture for smart IoT devices including constraints on power. Recently, PUF technology has been widely used for the encryption and authentication of IoT devices. Although, the generation process of the secret key by prior proposed research work possesses challenges of reliability with relation to PUF that affect the security concerns of the applications regarding IoT. It is required to resolve the issues relating to reliability and cost efficiency for smart and IoT devices. For this, the nodes used for communication have to be secure over the network, this causes the reduction in security attacks on the PUF. The proposed work uses a lightweight PUF for enhancing the security issues for IoT devices that have constrained resources. This research work proposes DO-PUF (Dynamic obfuscation - PUF) to secure the IoT devices. Dynamic Obfuscation (DO) integrates the previous input along with generated keys and random numbers, which creates a novel key. DO-PUF is evaluated through designing the instance on machine learning model and it is observed that DO is not only secure but efficient as well.

The IOT framework develop for forecasting and prediction model for agriculture mo9nitoring system using python code. this paper represents the IOT based hybrid model and working system for monitoring agriculture along with crops production system. the various IOT devices. raspberry PI controller works with various IOT devices using satellite system for monitoring crops production system using sensor and diode devices. This hybrid system is known as embedded system. the python code performs the working environment for all embedded devices with connected controller, sensor mode representation. This paper presents various embedded system working using with satellite monitoring for agriculture crops production endowment. Index Terms: embedded system, furcating system, raspberry PI controller, python, prediction model, methodology, etc.

Reducing power losses and regulating voltage stability within the limits of Radial Distribution Systems (RDS) are essential processes to provide quality power to consumers. The power loss minimization and voltage profile improvement are effectively done by optimum network reconfiguration and the placement of the capacitors in RDS. This paper presents the combined methodology of Capacitor placement and Network reconfiguration is properly applied to maximize the net saving cost, minimize the power loss and improve the voltage profile. The size and location of capacitors and tie-line switches of nodes are optimally allocated by the effectual Moth-Flame Optimization (MFO) algorithm. The MFO is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. This algorithm provides a better solution with less computational time by two searching operators of Moth and Flame. The Performance of the MOF is analyzed by a standard test system of 33 and 69-node RDS. The best simulation results of loss reduction, voltage enhancement, and cost-saving are numerically and graphically reported. The dominance of the obtained results is compared with other soft computing methods available in the literature.

In this article, develop image encryption techniquerelated on anElliptic Curve DiffieHellman(ECDH)and optimization algorithm of aPelican Optimization Algorithm (POA). The proposed model is utilized a secure ECFH key exchange towards calculate a communal session key with the enhanced EI-Gamal encoding technique. 3D and 4D Arnold cat maps can be utilized towards efficient transform and scramble the parameters of plain image pixels. An efficient digital signature can be utilized for authenticated the encrypted image preceding to decryption. The projected system is split into three stages such as ECDH key exchange, Decryption with decryption and signing of encrypted data. The encryption can be achieved on pixel blocks of specific pixels to empower speed of encryption and efficiency. In the projected technique, the efficient key selection can be achieved with the consideration of POA algorithm. The projectedtechnique is applied in MATLAB in additionpresentationis analyzed with performance metriceslikePeak Signal to Noise Ratio (PSNR), correlation coefficient (CC), Error, encryption time, in addition decryption time. The projectedtechniqueiscontrasted with the conventional techniques such as Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman encryption (RES) respectively.

Lung cancer can be a type of malignant growth in addition challenge towards identify. This as a rule causes orientation for both men and women, so it is imperative to immediately look at the handles accurately. Similarly, some methods have been executed towards identify cellular dysfunction in the lungs in the early phases. This study introduces a study of various strategies in the light of machine learning to detect cellular dysfunction in the lungs. Numerous techniques have recently been developed to analyse cellular dysfunction in the lungs, most of which use CT scans and some x-ray images. Furthermore, different classification techniques for applying image recognition to detect cellular dysfunction in pulmonary arteries are compatible with different section calculations. Based on the study it was computed which CT scan images can be high reasonable to achieve accurate outcomes. However, CT scan images can be usually utilized aimed at the location of the malignant growth. Similarly, the marker-controlled watershed segment yields more precise effects than remaining segmentation strategies. Additionally, the outcomes achieved from the in-depth training practices based on the strategies were more accurate than the techniques implemented using the old-fashioned machine learning methods.

In this study, we present a comprehensive survey of new contracts with in deep learning for dermatological outcomes. First, we provide a detail outline to skin infections. Finished written investigate, we provide general data acquisition techniques in addition solutions to some of the most commonly used and freely accessible dermatological databases for the preparation and testing of in-depth learning models. From that point on, we present the popular in-deep learning models, depicting the basic origins of in-depth learning. Appropriately, common deep learning systems are depicted and thought out. To clarify how to evaluate an in-deep learning technique, we know assessment measurements that are compatible with various errors. Then, during that time, we write about the benefits of in-deep learning in diagnosing dermatitis and learn about the material that is compatible with the various endeavours.

"Lung Cancer" is the clearest cause of malignancy-related death worldwide. Then in advance location, the expectation and determination of lung cancer in the lung became fundamental because it accelerates and consequently works in the clinical team. As a result of its accurate results, AI procedures were used to improve the progress of dangerous situations and medicine. Different types of Machine Learning (ML) such as Artificial Neural Network (ANN), Logistic Regression, Support Vector Machine (SVM) and Naïve Bayes have been used in the clinical field to examine and visualize cancer in the lung. Also, various types of deep learning and maximum relevance–minimum redundancy (mRMR) based lung prediction are audited. In this audit, the factors that cause cellular dysfunction in the lung and the use of ML calculations are exceptional, and extraordinary to observe their overall assets and shortcomings. This paper will enable researchers to quickly navigate the attached writing as opposed to referencing multiple papers. Also, future implications of lung cancer prediction are introduced using deep learning.

This article offerings an overview of deep learning for lung infection location in images specially in medical domain. Disease is the main source of death for all kinds of people. The initial discovery of malignant development is valuable in dismissing the illness completely. The image processing events are generally utilized for forecast of lung cancer and furthermore for early identification and therapy to forestall the lung cancer. To anticipate the lung cancer in the lungs different features are separated from the images thusly, Neural network-based methodologies are valuable to foresee the lung cancer. The essential point of this work is to foster a high-level Computer Aided Diagnosis (CAD) framework utilizing deep learning technique that will effectively separate information from Computer Tomography (CT) scan images and give exact and ideal finding of cellular breakdown in the lungs. The study persuades the author to think that deep learning could be a useful asset in diagnosing tiny and exceptionally difficult to decide nodules to help clinical dynamic cycle.

The results of a research study on the mechanical features of concrete involving zeolite and polyvinyl alcohol fibres (PVA) are presented in this paper. Zeolite has been used to partially replace ordinary Portland cement. Various percentages of PVA fibre have been tried along with zeolite. Desirable results (workability and strength) were obtained with 10% Zeolite and 0.3% PVA fibre. The water content was kept constant for all concrete mixtures. SP (conplast) was used to attain the target workability and strength. Mechanical features such as compressive strength, flexural strength and elasticity modulus were examined. PVA fibre volume fraction was treated as the primary study variable. Cubes of size 150 mm X 150 mm X 150 mm, Cylinders of size 150mm X 300mm and prisms of size 100 mm X 100 mm X 500 mm were cast and tested as per standard code practices to realize the effect of PVA fibres on the mechanical features of concrete containing Zeolite. The cube compressive strength increased by a maximum of 36.07%, the cylinder compressive increased by a maximum of 37.78%, the flexural strength increased by a maximum of 81.81% and the elasticity modulus increased by a maximum of 26.52%.

Remote data collection from a sensitive area is required for a tedious process of communication. The research examines the deployment of wireless sensor networks on the battlefield in terms of certain parameters such as node energy, delay, and survivability of the network. The simulation environment is analyzed using a network simulator in accordance with three levels. Wireless sensor networks require continuous data collection and high energy consumption during their initial set-up phase; secondly energy consumption is concentrated in the routing phase; and finally delay estimation is done in the transmission phase.

The renewable energy sources have gained their importance from last few decades. The enhanced environmental concerns & exhaustion of fossil fuels reserves has inspired the researcher to find alternative energy sources. The biodiesel obtained from Vegetable oil & animal fat is recommended for replacement of conventional fuels. The use of Waste Cooking Oil is economical and makes the biodiesel competitive in price with petroleum based fuels. The current study is aimed to study the influence of Aluminium oxide nano additives on the performance and emissions characteristics of compression ignition engine fueled with waste cooking oil bio-diesel. In the experimentation, 50ppm & 100ppm of Aluminium oxide nano-particles were mixed in waste Cooking Oil biodiesel. Experiments were carried out with B20 WCO fuel and adding Aluminium oxide nano particles in the proportion of 50ppm & 100ppm respectively to B20WCO biodiesel blend. The results revealed that, the brake thermal efficiency increased and specific fuel consumption has reduced for B20WCO & B20WCO nano additive blends. When compared with diesel, there is a significant reductions in the parameters like UBHC, NOx and CO emissions at B20WCO in conjunction with 50ppm &100ppm Aluminium oxide nano-additive blends. However, there is a slight increase in NOx emissions for B20WCO & B20WCO nano additive blends.

Cognitive psychology is characterized by being goal-oriented, problem-focused, and problem-solving from the very beginning. In some people's minds, failure is a sign of worthlessness or inferiority. This branch of psychology is concerned with analyzing how mental processes like perception, attention, thought, language, and memory operate, mainly through inferences from behavior, in addition to studying the phenomena themselves. In the 1940s and 1950s, cognitive approaches developed in response to contemporary behaviorism in the emphasis on unseen knowledge processes rather than observable behaviors directly observed, and the belief that stimulus and response are not simple and direct, but rather complex and mediated relationships. A result of its concentration on the higher mental processes contrasts with the typical approach of psychoanalysis, which emphasizes instincts, unconscious forces, and other unconscious mental processes. Many methods have recently influenced the relationship between cognitive psychology and computer science and artificial intelligence to information processing and information theory developed in computer science. In this paper, different approaches of Cognitive Psychology, concepts of behaviourism, perception and attention are illustrated. Different Object properties are identified between entities of psychology. Ontologies are used to build the relations between different relations identified to build a knowledge base. Protégé editor is used to implement Web ontology language for fundamentals of psychology in aspects of developmental, clinical, abnormal and social psychology to understand these concepts optimally.

Multiple mobile sinks have been included in wireless sensor networks (WSNs) to improve the EE of data collection and transfer in large-scale applications. Energy consumption (EC) is now an important study subject for sensor nodes to increase the network's lifespan. Many cluster-based and mobile sink scheduling strategies have been devised to improve energy efficiency (EE) in a WSN. Despite the many benefits of applying approaches, there are still several problems in various areas, such as data buffering and data queue, while delivering data to the base station. This research examines the efficacy of several scheduling-based energy-efficient routing methods for WSNs. These protocols are analyzed selectively based on the design, implementation, and deployment of the many mobile sinks in the WSN. In particular, the hierarchies of the mobile sink-based routing protocols Low Energy Adaptive Clustering hierarchy (LEACH), Ring Routing, and Spatial-Temporal Defined Multiple Mobile Sink Scheduling Framework towards Throughput Maximization and EC in WSN have been thoroughly analyzed. The simulation findings of these protocols validate the efficacy and efficiency of the examined EC strategy. Spatial-Temporal Defined Multiple Mobile Sink Scheduling Framework outperforms protocols; it has been determined.

In recent years, the use of digital images has increased drastically in various domains in life such as medical, military, scientific and other. Images are often degraded by noises. During image capture, transmission noise can obtained or occurred. This noise affects the quality of image and loss of important information, so it is very important to remove these noises with preserving the image as much as possible. Noise removal technique play important role in the field of image processing. In image processing noise removal is an important task. By using noise removal techniques that is image filtering techniques, the quality of the image remains as it is. In this paper four types of noise such as Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise are used to remove the noise from an image. Different filters such as Mean filter, Median filter and Wiener filter are used to remove noise.

Mobile Ad Hoc Network (MANET) is one of the world's most important new research areas. The MANET node is a small device with computer power that protects the network from limited power, memory, transfer range and various attacks. The security of wireless ad hoc networks plays an important role and is becoming more and more important in the current generation. Cluster head selection and Routing security Mobile Ad-Hoc Networks (MANETs) are major challenges in wireless sensor networks. Previous method problems are limited security of nodes, lack of certification authority, and lack of centralized monitoring points. To overcome the problems, this work proposed the method Enhanced Spider Monkey Optimization Cluster-based Secure Routing Protocol (ESMOC-SRP) for Some nodes used to select the cluster head in the model population and the cluster head is selected from the nodes. This solves the problem of different node levels and cluster head positions. This will extend the life and stability of the Wireless Sensor Network. The Trusted Node Evaluation (TNE) method uses a secure data transfer technique through the exchange of keys between the cluster structure and the CA. The proposed method is ESMOC-SRP, which is used to improve the efficiency of node reliability assessment. Reliability measurements reflect the quality of packets, the number of packets, and the reliability of measurements maintained by reliability management nodes. Can improve the data transfer integrity by exchanging keys with a Certificate Authority (CA) between nodes. The proposed trust-based Node Evaluation model secure Routing is confirmed by ESMOC-SRP performance analysis tests based on the proposed simulation parameters and performance metrics, improving the security and energy efficiency better than previous methods.

Cloud computing is one of the important business models of modern information technology. It provides users with a variety of services (hardware, software) with minimal interaction and low cost. Security and privacy are the primary concerns of utilizing on cloud environment. The data storage and infrastructure authority in the cloud are offered by the third party. Sometimes sensitive data has the risk of being handed over to cloud service provider. Hence the user always expects useful secure data storage. Overcome those issues to propose a Lightweight Bcrypt Symmetric Key (LBSK) data encryption and decryption algorithm with key rotation technique. Different algorithms and blockchain methods are there in use for protecting the data. Encryption is one of the fascinating parts of data securing technology. For Secured cloud data audit, introduced an Aggregated authority certificate provider (AACP) with blockchain authentication, which reduces the burden of the data owner. In this proposed AACP which run on cloud server continuously and monitor the cloud user request. The Hyperledger blockchain proxy re-encryption is used to achieve higher security and privacy for the non-trusted provider. The Data encryption has been carried out all the way through the Blockchain algorithm and stored in the cloud. Security level is increased with public verification and performance aspects, for remote data verification without keeping the data locally. In this proposed method provides a better result compared to existing methods.

As cyberspace is evolving, new threats in the realm of computer and internet safety are surfacing too. As a result, traditional Intrusion Detection Systems (IDS) are gradually becoming obsolete. Earlier IDS-based security systems focused on predefined signature standards, which made it impossible to detect newly discovered abnormalities and attack variations. Its main issue was the slow rate at which the signature database was refreshed and scaled to keep up with the increasing rate of threat evolution. Researchers continue to employ state-of-the-art methodologies to assure effective threat detection and protection using anomaly-based detection as the task of combating today's cyber threats becomes more difficult. To equip systems with future-proof intrusion detection measures, researchers are adopting sophisticated techniques based on machine learning and deep learning. Because of the variety of modern, complex threats, developing an effective intrusion detection system in a multi-layer attack classification environment is a challenge. Intrusion Detection Systems require high-performance classification algorithms as attackers can readily create intrusive techniques and avoid detection instruments deployed in a computing environment. Furthermore, using a single classifier to effectively detect all types of attacks is complicated. As a consequence, a hybrid approach provides better performance and accuracy. The proposed approach is based on developing a Manifold Approach that is specifically designed to address the aforementioned issue and identify different types of attacks. Each layer of attacks has its own classifier. The proposed approach will be evaluated on the CIC IDS 2018 data set. With the proposed method, the final accuracy obtained is 95.68%, a recall rate of 99.99%, and a better attack detection rate than the baseline classifiers and other existing approaches for different attack categories.

This article presents a state-of-the-art design for a limitless impulse response (IIR) filter that can be easily reconfigured for use in real-time software. Using a vedic multiplication strategy, this work demonstrates the excellent general performance of a recursive or Infinite Impulse Response (IIR) filter. The number of repeats may be kept to a minimum and the processor's overall performance is improved by the reduction of computational delay and hardware. This research investigates the impact of different filter architectures on the development process and overall performance. To enforce and evaluate the planned filter's functionality, simulation is used across three independent platforms. The first system is MATLAB/ SIMULINK, the software package utilised to implement the IIR filter. The second strategy is called "HDL - Cosimulation," and it involves using SIMULINK's already-present tools in order to translate the formulated filter-out method into VHDL, the language used to describe very fast integrated circuits. The third approach uses Xilinx System Generator's pre-existing building components to physically realise the filter design. The method shown here allows the proposed filter to be implemented locally inside the FPGA device of interest.

As a result of some recent sudden building collapses, the use of concrete with improved properties has become necessary. This study produced concrete using plastic box waste (PBW) from boxes used to store and transport vegetables and fruits. The particles resulting from these boxes were used to partially replace 5%, 10%, 15%, 20%, 25%, and 30% of the sand. The produced concrete was subjected to tests of compression, density, modulus of elasticity, and impact resistance. The impact resistance significantly improved from 18% to 155% in the first cracking and from 14% to 67% in failure cracking as the replacement of sand with PBW increased from 5% to 30%. The density was also reduced by 3.43% to 10.33% for the same range of replacement. Decreased compressive strength and elastic modulus were observed, but both values remained acceptable for structural concrete.

Rescue efforts during natural disasters, military applications on battlefields, and other crucial situations when the current infrastructure is insufficient are good candidates for IEEE 802.11-based wireless ad hoc networks fails. Since the battery life of nodes in these networks is limited, the Reduced packet collision is crucial; otherwise, nodes would suffer energy required for packet transmission must be increased. Performance in these wireless networks is greatly influenced by the packet transmission process. The authors have proposed several packet transmission techniques depicted with Markov chain models to enhance network performance. However, because of the high collision rate, these methods have not proved effective for dense networks. In this paper Comparison of several Back off Algorithms are discussed and Analysed for the need to improve the network performance.

Graphene nanoflakes (GNFs) were chosen due to their great potential for employment in electronic nanoscale applications. The most promising implementation of this paradigm is to be used as a recharging battery or as a nanosensor for various particles. The interaction between graphene nanoflakes (GNFs) and selenium or sulfur has been investigated by considering it as add atom impurity and a passivating element. The adsorption of both Se, and S at five different positions viz. hole (H), passivation (P), top (T), replace (R), and bridge (B) is studied to define the most stable structure. The electronic properties (such as band gaps, Fermi level, HOMO, and LUMO energies) and the global properties (ionization potential, electron affinity, electronegativity, hardness, softness, and electrophilicity) are calculated for GNFs with and without the Se and S atoms. All these systems were investigated by method at the B3LYP/3–21 G level in light of the density functional theory (DFT) with the Gaussian 09 program. It was detected that all the systems impinged with Selenium and Sulfur particles caused the bandgap shrinking with different values at different impurity locations. The bandgap was also affected and significantly reduced in value by the interactions of pure GNFs with impurity particles (Selenium and sulfur) with bridge denaturation. At the same time, there were slight changes in both the gap position of selenium and the passivation position of selenium and sulfur compared to the original state of GNFs.

The non-stationary impulses of an electrocardiogram (ECG) are widely utilized to assess the pace and frequency of heartbeats. An electrocardiogram (ECG) is a procedure that measures the heart's electrical activity to detect irregularities. Medical experts more widely use automatic ECG categorization in medical diagnostics and therapeutics. This work presents practical strategies for automatically classifying ECG data into two categories: standard and affected (abnormal) patients. To represent the ECG signal, morphological characteristics are retrieved for these classes. In this work, the signals are prepared with the normalization and filtering approaches, which minimizes the noise incidence. The preprocessed image is segmented, and feature vectors are selected using Ant Colony Optimization (ACO). With the assistance of feature vectors, abnormalities in the ECG signals are classified with the Bi-Long Term Short-Term approach (Bi-LSTM). The performance of the proposed approach is investigated using the performance metrics and acquired accuracy of 90%, which outperforms the existing ECG classification techniques.

Data security is one of the most critical parts of considerable data investigation. Most cloud systems deal with sensitive information, such as personal, corporate, or medical records. Threats to this type of data might affect the growth of the cloud systems that store it. On the other hand, traditional security solutions are ineffective in safeguarding substantial data transfers. To handle the vast generation of data and their security aspects necessitated an efficient privacy-preserving mechanism. Initially, cloud-based big data is clustered with the K-Modes-based Fast Mutation Artificial Bee Colony clustering (K-mode FMABC) technique, and clustered information is balanced with the support of the Hadoop-based map-reduce mechanism. Further, the information is encrypted using an advanced super encryption technique, and the convolution process is accomplished over the method of estimation that encrypt data. The estimation process is completed by the deep neural network (DNN). If the encrypted data are not properly encrypted, the data will be sent through the convolution process again. The proposed framework's experimental findings are compared with the current methodologies; the big data-based privacy-preserving scheme outperforms existing techniques.

Alzheimer's disease (AD), a complex, incurable, and terrible illness, has a positive worldwide influence on human survival. It had no vaccinations, making it the sixth most significant cause of mortality in the United States. The most challenging aspect of biological discovery. The identification of AD-related proteins and genes will aid in the understanding of the disease's aetiology and the identification of vaccine and treatment targets. They look into combining genes/proteins with Alzheimer's, necessitating using functional instruments and experience. It was used to build a machine-learning algorithm for predicting the connection of proteins with Alzheimer's disease using current data from all known AD proteins/genes. We proposed the EADD (Enhanced Alzheimer's Disease Detection) technique for MR scan of the brain is often used to diagnose Alzheimer's. The MRI dataset noises have been removed using Multilayer perception(MLP). The image enhancement has been done with Histogram equalization in this proposed work, the image segmentation has been done using Edge-based Robert operator, Training has been done using CNN with RESNet150, and classification has been done with CNN Algorithm. The experimental results indicate that the classification accuracy of the approach proposed in this research can reach 98%.

Falls at home is one of the most common health issues facing elderly people who live alone.Fall detection is an active research area in the smart home and smart healthcare system field. The sensor-based devices that the senior can wear or keep inside the pocket are available in the market.While they are easy to use, people forget to wear them due to their age factor. Senior people are now being monitored for their safety at home using a video surveillance system. But the lacking of the system isthat it doesonly monitors and not detect the fall.Due to this, the system does not help people to get help on time.Otherwise, a person should monitor the videoscontinuously, but it is a very tedious task. This work proposes a new vision-based real-time fall detection algorithm to address this issue. It is cost-effective and ensures the safety of senior citizens who live alone.In this instance, the fall is detected and an alert is made based on sudden changes in the human object model and the idle state continuing for more than 180 frames.The publicly available video datasets are utilized for implementation. The simulation result shows that the new vision-based approach achieves a high detection rate of 96.77%within less processing time.

In the digital world, images are extremely important. The forging of an image is becoming more common in the digital age. Because of the latest image alteration tools and technologies, identifying a forged image has become a difficult undertaking. The suggested method employs a hybrid approach to identify image forgery. The proposed approach is broken down into three stages. In the first phase, the R, G, and B channels of an input image are clustered employing K-Means, and then the Gaussian filter is utilized to blur the image. The entropy feature is then taken from an input image's colour and grey scale and stored in matrix form. The retrieved characteristics are matched in the second phase, and the matched blocks are coloured blue and red. In the third phase, count the continuous locations of matched blocks; if it is larger than 10 pixels, the image is forged; else, it is not forged. According to the experimental results, the hybrid technique detects forgery in an image with great accuracy.

Introduction: NHS Digital stated that in 2020, one in six school children will be affected by mental illness. Emotional disorders such as anxiety and depression are most common among young people. Increased responsibility can be stressful and the thought of fulfilling society’s expectations can be stressful and emotional. Objective: the major focus of the present research was to assess and compare the mental health and decision-making styles of tribal and non-tribal adolescents. Methods: The sample consists of 101 adolescents where 47 are from tribal and 54 from the non-tribal community with a purposive sampling method. Statistical techniques such as mean, standard deviation, Pearson’s correlation and t-test were used to analyze the data. Results: The vigilance style of decision-making is negatively associated with mental health. Procrastination, buck-passing and rationalization are positively associated with mental health. Conclusions: Mental health is partially significant with the decision-making styles. The increase in mental dysfunction decreases the ability to decide in vigilantly and in turn rises stress-associated decision-making styles such as procrastination, buck-passing and rationalization. The lower socio-economic status and family history of addiction impact the mental health condition of tribal and non-tribal adolescents.
Keywords: Mental health, Decision-making style, Vigilance, Addiction, Adolescents

Chennai is a rapidly growing metropolitan city in India both spatially and economically. The increased economic viability of the city has resulted in massive appreciation of land prices in the sub-urban areas and urban fringes. This study aims to understand the impact of physical, locational, neighbourhood and transportation factors on land pricing in the urban fringes. A total of 236 questionnaires were collected from three study groups - real estate developers, brokers & valuers, and potential buyers. The responses were presented and analyzed, and the relative importance index was calculated. It was found that the transportation factors and neighbourhoods affect residential property value to a greater extent. The distance from the central business district and the distance to the highways followed by the proximity to various urban facilities were the essential factors affecting the land price in the suburban regions of Chennai. The study recommends that property investors should consider these attributes in the investment decision making process and the authorities should consider these attributes for development planning.

The machine learning algorithm's productivity relies on the features selected for the analysis. Feature selection is the important pre-processing step in the data analysis, especially for the data sets which consist of more variables. This step helps in getting better accuracy by eliminating the unwanted variables in the dataset. This research work includes Optimal feature selection for crop prediction, and improving crop yield prediction by considering suitable and important descriptors. Feature shuffling, Single feature performance, and Target mean performance are used for feature selection based on feature importance score. The selected variables are given as input to the Gradient boost regressor, Random forest regressor, SVM, KNeighbor regressor, and Decision tree Regressor for better accuracy. The features selected using the hybrid technique give 90.92% of accuracy for the prediction of yield.

The primary purpose of employing hand gesture detection to control computer applications is to improve user-computer interaction by making computers responsive to users needs. Hand gesture recognition is achieved using non-vision and vision-based techniques. Vision based technique is more natural in general because these do not involve the use of any hand gadgets. Application of our work is to access an PPT(Power Point) without using a keyboard or a mouse, hand motion detection and sign language understanding. This results in enhanced human computer interaction, ease of accessing a computer application and getting rid of difficulties that exist between the user and computer caused due to any hardware damage or failure such as wear and tear of keyboard, mouse or any I/O device. Gaps that have been identified in gesture recognition were limited only to detection of hand gestures, understanding of the sign language, and decreased efficiency under low lighting conditions. However we have aimed to overcome the stated gaps and introduce a novel way of accessing a power point presentation by leveraging concepts and working principle of gesture recognition using machine learning..

The recent increase in technological changes to, Smartphone's have changed the lives of human beings. Whenever customers are purchasing smart phones, they examine a variety of factors such as the screen, processors, storage, camera, width, battery, accessibility, and so on. The most essential consideration that individuals overlook is if the product is worth the price. People fail to make the proper decision because there are no tools in place to cross-check the pricing. To solve this problem, many companies are at the moment are taking the help of machine learning strategies to take the correct decision. In this paper,We are using historical information on Smartphone essential features and costs to construct a model that will estimate the approximate price of the next Smartphone with decent accuracy. Different algorithms such as Linear Regression, KNN, LDA, Logistic Regression, and other different models are used to compute the price of smart phones. Optimized versions of Linear Regression and KNN algorithms are passed into a stacking classifier to generate a Hybrid Model which provided an accuracy of 94.8%. Bagging and Boosting Ensemble methods are also used to predict the accuracy and price of smart phones.

Automatic Number plate / License plate recognition (ANPR/ALPR) is identified as emerging technology which has been developed based on image processing techniques. It has been used in various fields like identifying vehicles in applications like over speeding, red-light enforcement, parking control and toll collection. ANPR is employed to detect the license plates then make the recognition of the plate that's to extract the text from the plate using location algorithms, segmentation plate and character recognition. The major issue that plays here is that the accuracy. Recent developments in deep learning have improved the capability to solve multifaceted visual recognition task. Hence using deep Convolutional Neural Networks will increase the processing speed and prediction accuracy of solving the ANPR process

Mobile Ad Hoc Networks display a light weight element channel assignment component & a helpful burden adjusting methodology that is material to group-based MANETS to address. Its performance increasing common & typical network loads considered for MANETS are increasing as application evolve mechanisms to improve performance in terms of throughput, energy consumption and inter packet delay variation. For the performance of wireless mobile computing systems, it is critical to efficiently allocate communication channels. The centralized channel allocating algorithms proposed in the literature are neither scalable, nor robust past are complicated & require active participation of the mobile nodes. The protocol used at the Medium Access Control (MAC) layer ensures the success of a Mobile Ad Hoc Network (MANET). The protocol parameters can be decided to make the optimum utilization of the channel resources at the MAC layer based on the requirements and the specific network under concern.

Agriculture and health are the two interconnected fields as they are linked with the well being of the people which is of primary concern in today’s world. They serve as backbone for human life and economic system of the country. Agriculture is crucial for maintaining good health. In contrast, health also affects agriculture by demanding good agricultural products. We can see that these days health is affected by consuming adulterated food. There is a high need of improving the quality of agricultural products we use, which helps in maintaining good health. Good health creates healthy people and communities which in turn aids in achieving ecological and economic balance in the world. Thinking bigger, we need to apply the technologies to improve both agriculture and health to monitor different issues in both the sectors. Nowadays, Artificial Intelligence (AI) is playing a vital role in almost all fields. Machine Learning (ML) and Deep Learning (DL) being the part of Artificial Intelligence are gaining more and more importance and benefits in different sectors. In this paper, we see how Artificial Intelligence, Machine Learning and Deep Learning have their influence in the field of agriculture and health in particular. The paper contains the major algorithms and tools used in Artificial Intelligence to benefit in these two sectors. General Terms: Applications, Algorithms, Tools.

Remotely sensed images, their classification and accuracy play a vital role in measuring a country’s scientific growth and technological development. Remote Sensing (RS) can be interpreted as a way of assessing the characteristics of a surface or an entity from a distance. This task of identifying and classifying datasets of RS images can be done using Convolutional Neural Network (CNN). For classifying images of large-scale areas, the traditional CNN approach produces coarse maps. For addressing this issue, Object based CNN method can be used. Classifying images with high spatial resolution can be done effectively using Object based image analysis. Deep learningmethods offer the strength of auto learning the spatial features of an image. Object scale based adaptive CNN is a novel technique that can improve the accuracy of imageclassification of high spatial resolution images. For efficientRS image classification, a novel Deep learning approach called distributed CNN can be used which leads to enhanced accuracy of RS image classification. In this paper, three CNN models have been compared while considering the training time and efficiency to classify RS images as parameters of measure to assess the CNN models.

Artificial Intelligence and Cloud Computing are having mutual associations with each other in order to provide effective services. Edge computing is an extended version of cloud computing that is applied for enhancing the cloud environment’s data computing and storage. Edge computing based environment will reduce latency and improve the performance of applications. For sensitive data exchange, edge computing is more appropriate than traditional cloud computing techniques. While working with big data like a large scaled data environment there are several challenges regarding latency, security, connectivity and privacy. While integrating the services of AI with edge computing will ensure secure and optimal threat intelligence architecture. This research work utilizes the salient features of AI and Edge computing in a combined manner.

By offering several benefits such low starting cost, significant flexibility and scalability, and easy maintenance, cloud computing (CC) had facilitated amazing advancement. The popularity of CC has sparked interest in serverless applications among programmers who desire to create a variety of micro-services. However, despite the fact that there have been numerous studies on the creation of server-less applications based on CC over the last few years, the protection of the CC infrastructure has received the majority of attention, and as a result, serverless security architecture has received comparatively little investigations. In this article, we examine the secure programming practices for serverless services on CC and discuss potential risks. The Graph Ontological Framework technique is used to design the infrastructure for AWS based serverless systems, and major security concerns are found. Additionally, we offer a solution that is function and language-neutral, simple to incorporate into already-existing server-less infrastructures, and will aid in the development of more secured serverless models. According to our knowledge, the proposed ontology model is the very first monitoring framework especially designed to meet the needs of serverless systems.

The increased utilization Internet of Things (IoT) equipment in homes, workplaces, transportation, hospitals, and other venues has led to an increase in malware activity. Since the detachment between IoT and fog devices is closer than the range among IoT equipment’s and the cloud, attacks can be quickly detected by integrating fog computing into IoT. Owing to the vast amount of records generated by IoT devices, Machine Learning (ML) is commonly employed for attack detection. The issue is that fog strategies could not have the facilities, such as information processing capacity and memory, to quickly identify threats.This research suggests a method for transferring the live prediction responsibility to the fog nodules and the ML model selection function to the cloud. An ensemble ML model fused with Genetic Algorithm (GA) is developed in the cloud using the anticipatedmethod, based on historical information and then real-time attack prediction on fog nodes is performed. The NSL-KDD datasets are used to evaluate the suggested strategy. In terms of a number of performance indicators, including processing time, precision, accuracy, recall and Receivers OperationCharacteristics (ROC) curve, the results demonstrate the efficacy of the suggested approach.

Integrating the Internet of Things (IoT) with unmanned aerial vehicles (UAV) system enables various value-added services from sky to ground. IoT devices are connected to communicate or exchange information in wireless sensor networks (WSN). Moment of the sensor in UAV, data transmission and data sensing processes are consuming more energy in WSN. This data transmission process takes a higher amount of energy than other processes of IoT-based UAV devices. It is an open challenge for researchers to optimize energy consumption during data transmission whenever an emergency occurs. So, this research focuses on developing an energy-efficient emergency response Routing Protocol for Low-power and Lossy networks (EEER-RPL) to prolong the network lifetime of the IoT network. It is achieved by electing the Super Nose (S.N.) during the emergency message transmission. The S.N. election process is performed by calculating the distance among all neighboring nodes. A node with optimum distance and higher energy is considered as S.N. to transmit data among its neighboring nodes. In this, the S.N. acts as a normal node in the standard data transmission process. Whenever the network gets the emergency signal, it selects S.N. to perform energy efficient Routing. The Simulation results show that the proposed EEER-RPL outperforms the existing schemes regarding network lifetime, average packet delivery time, and average hop selection.

MATLAB's Simulink and the Antenna Toolboxes are used in this research to build and simulate a 2.45 GHz square patch microstrip antenna. Microstrip patch antennas' advantages and disadvantages will be discussed in detail in the introductory portion of this paper's brief history of the development of antennas for wireless communication systems. The rectangular microstrip patch antenna, coding, and outcomes of the developed microstrip patch antenna are all covered in this work. Antennas are critical components of every wireless communication system. Reduced antenna size, weight, and cost with good performance and minimal return loss are the requirements for this technique (RL). The MPA (microstrip patch antenna) may be utilized to achieve these specifications. A flare retardant substance is used as a substrate for the proposed MPA. Using an RL of -38.86 decibels at a frequency of 2.3933 GHz, the MPA was found to be capable of operating with a BW (bandwidth) of 57 MHz and a VSWR of 1.002. The antenna's volume is 75.805 x 57.223 x 1.6.

This paper examines a comprehensive review of factors influencing the purchase of eco-friendly automobile i.e. electric vehicles aiming to offer a conceptual framework of factors and deliver the path to additional research. This paper is a scientific evaluation of 578 peer review magazine articles to become aware of the purpose for and in opposition to the aim to purchase an electric vehicle. In this study, a total of 578 papers were reviewed followed by two-step verification and subsequently, 53 papers have been recognized and analyzed paper provides important information for eco-friendly product manufacturers. This paper pursuit to identify the factors that affect customers buying electric vehicles and accordingly present useful guidelines for the manufacturers in developing and imposing electric vehicles.

In recent years, friction welding (FW) has become increasingly widespread as a result of significant progress made in the area of industrial applications (IA). When joining two different types of metals, FW is one of the most efficient and cost-effective methods available. Within the scope of this investigation, the 2 metals that were subjected to FW were stainless steel (SS) & EN3B. The processing parameters that were applied included rotating speed, frictional time or pressure, as well as forging time or pressure. By adjusting the FW variables, this work aims to combine 2 distinct metals, namely SS & EN3B, and to increase their tensile strength (TS). The TS of the material was studied by conducting a series of experiments with different combinations of the process parameters. It was discovered that the process worked, and that the strength of the welded joints changed depending on how the process variables were set up. TS was used to test the welds, and the results were recorded in a table along with the input variables. The hardness distribution at the joint interface was noted and plotted a hardness peak was observed at interface. Micro structural observation was also done characterize the joint.

In the digitized world era, everything is made available to all people thus it leads to data dissemination and allows for sharing of unauthenticated information. There are several sources such as social media platforms, Twitter and web blogs that are sharing information. Here the need arises to predict whether the news is fake or real news. Fake news is essentially modeled as a distortion bias where true information is distorted by a degree to fit any biased view. The reason for the success and rapid spread of fake news is that everyone has inherent biases and looks for confirmation of their preexisting notions. It is used for furthering propaganda and sowing hate in society. It is therefore very important to develop systems to automatically detect fake news. In this paper, the Hard Voting Ensemble classifier is proposed for fake news detection. The use of Passive Aggressive Classifier, Decision Tree Classifier, Naive Bayes classifier, Support Vector Machine (SVM) and Voting Classifier on LIAR Dataset were studied in this paper. Feature engineering approach, TF-IDF vectorizer is used to transform the text into vector form. The performance of the proposed classifier is tested with various data sets.

The Internet of Things (IoT) is a method of providing a significant technique to use the Human lifestyle in a more advanced way. For obtaining a smart way of lifestyle, electricity supply should be processed through cutting-edge technology. These strategies can be used to monitor and control solar photovoltaic systems for electrical energy generation. Solar cities, Smart villages, Microgrids, and Solar Street lights are a few of the applications for this technology. This is the most successful period in history for renewable energy, with a much faster growth rate than previously. The generation system has acquired internal and external barriers, making it difficult to provide a continuous supply of energy sources these days. The approach described here shows how much energy solar panels use as a renewable source of power on the internet. The performance conditions like operating conditions, temperature effects, solar irradiation incident on photovoltaic panel output, and energy import and export through a grid-connected system can all be monitored using IoT devices in this study.

The work is a continuation of the work done by the author in the field of Medical Science. In the current scenario we are suffering from the problem of COVID. The pandemic is having a deep impact on various countries of the word. The pandemic has influence all across the world. There are various other diseases like cancer which is affecting a lot of population across the globe. In the current work the author is engaged in the survey and exploration of ways to face a disease called Thyroid. In the current work the author made survey of the Machine Learning approaches to make a detection of the Thyroid. In the current work the author made a utilization of the Tool called Python. The author made good utilization of the libraries present in Python. In the current work the author surveyed and used the algorithms namely Gradient Boosting Classifier, ADA Boost Classifier, Light Gradient Boosting Machine, Decision Tree Classifier, Extra Tree Classifier, Logistic Regression, K-Neighbors Classifier, SVM-Linear Kernel, Linear Discriminant Analysis, Ridge Classifier, Dummy Classifier, Naïve Bayes and Quadratic Discriminant Analysis. The work done by the author is an approach that still is having some limitations like the output user interface is not prepared which would have made the project more user interactive. The dataset is taken from Kaggle. The number of rows and columns present in the dataset of Kaggle is not that rich however the accuracies that are obtained after running of the code is extremely acceptable. But it would be more efficient if the data set would have considered more parameters and the number of tuples in the dataset would be more.

Renewable energy generation has become the choice of many countries, including Indonesia. New and Renewable Energy in Indonesia is a Solar Power Plant . Not only targeting people who are not reached by alternative distribution networks, but the system is a need for public and communal facilities in Indonesia. Gorontalo City is part of Gorontalo Province as a province that has an area that occupies part of the northern part of Sulawesi Island with various regional characteristics, ranging from coastal areas to mountainous areas, where several areas in this province have an attraction for investors to invest their capital. in the industrial and commercial sectors. commercial. Besides that, The uneven distribution of energy infrastructure is an energy problem faced by Gorontalo City. The research and modeling method in this study uses the Photovoltaic System Software (Pvsyst) application by conducting a survey of the research location planning the installation of a Rooftop Solar Power Plant to determine the condition of the building, the geographical location of the research location. From the simulation results using the PVsyst Ichsan Gorontalo University Building using a stand alone system with a PV array of 1261 units with a total Pnom of 328 kWp, for battery technology, namely lithium-ion, 925 LCO units produce a voltage of 241 V with a current capacity of 9990 Ah. Produces energy per year of 513619 kWh/year with a ratio of 70.36% where the active load is 13 hours/day. The research and modeling method in this study uses the Photovoltaic System Software (Pvsyst) application by conducting a survey of the research location planning the installation of a Rooftop Solar Power Plant to determine the condition of the building, the geographical location of the research location. From the simulation results using the PVsyst Ichsan Gorontalo University Building using a stand alone system with a PV array of 1261 units with a total Pnom of 328 kWp, for battery technology, namely lithiumion, 925 LCO units produce a voltage of 241 V with a current capacity per year of 513619 kWh/year with a ratio of 70.36% where the active load is 13 hours/day. The research and modeling method in this study uses the Photovoltaic System Software (Pvsyst) application by conducting a survey of the research location planning the installation of a Rooftop Solar Power Plant to determine the condition of the building, the geographical location of the research location. From the simulation results using the PVsyst Ichsan Gorontalo University Building using a stand alone system with a PV array of 1261 units with a total Pnom of 328 kWp, for battery technology, namely lithium-ion, 925 LCO units produce a voltage of 241 V with a current capacity of 9990 Ah. Produces energy per year of 513619 kWh/year with a ratio of 70.36% where the active load is 13 hours/day.

Different machine learning methods have been developed so far in order to identify different stages of the disease Lupus. The disease Lupus is a complicated auto immune disease and treatment was developed during last few years. So, very less research work has been done in this field and few data sets are available to perform and test research work in this field. Due to the fact this domain is devoid of efficient methods that are capable of finding out various stages of lupus. This research work has made lupus data set from individual lupus patient using survey method. The proposed model has been used for identifying stage of lupus patient. A multi class perceptron has been developed to classify the stage of lupus patient. Six features have been considered as input neurons of proposed model. The stages of lupus have been considered as Normal, Moderate and Severe. Results have been compared to other neural network models. The performance and accuracy of the proposed model have been found better compared to other models. No existing model with limited data set could ever identify the stage with better accuracy. The proposed model is helpful for medical field as an alternative support system to identify the stage of lupus patient.

User password or PIN Number has become mandatory in every secret communication, financial and monitory transactions these days. Communicating and strong one-time password or PIN number increases the potential rise of the security. Crypto Council for Innovation (CCI) is developing many PIN security requirements as encrypted symmetric keys in structures called key blocks, which contain protected keys, usage constraints. In these key blocks, there is no expectation that previously established keys can be reused. In the present paper, password or PIN encryption and migration technique is explained using Catalan number sequences. Here the encrypted password or PIN is inserted in a long random text at different positions and the text is communicated to receiver. The positions of the inserted PIN characters are secret between the sender and the receiver.

A statistical hypothesis test is an indispensable and powerful tool for making better decisions in various fields. When it comes to tasks involving large populations, it is not always possible to make accurate sample observations. That is, real-time observations may be accurate or imprecise by their nature. If the observed samples are imprecise, the corresponding samples can be manipulated with fuzzy numbers; more generally trapezoidal fuzzy numbers. Moreover, using a new ranking method derived from the lifespan of Euler centroid these fuzzy samples are fuzzy and a relevant statistical procedure was followed to test the hypothesis and obtain better results.

A technique of identical a set of speech utterances for identifying and verifying speakers using singular value decomposition (SVD) is accessible. The set of stored reference parameters yielding the largest ratio of singular values when mapped with the parameters of the utterance of an unknown speaker is considered closest to the unknown. This research examines the feasibility of integrating a speaker verification model in a voice authentication system. The speaker verification model is used to verify the identity of the speaker. Speaker voice to be extracted using familiar MFCC feature Extraction, after completing extraction process decomposition is exposed using SVD and also mapped with the parameters of the utterance of an unknown speaker. From the experiments, validation accuracy of the speaker verification model will be tested.

The three-phase injection transformer or three single-phase injection transformers comprising the primary supply will be used by the series-connected DVR to inject three-phase compensating voltages. The injection transformer raises the filtered VSI output voltage to the required value. The transformer additionally separates the DVR circuit from distribution network. The DVR's design heavily depends on the voltage source inverter's (VSI) capacity and the link filter's values, which connect the injection transformer as well as the inverter. In this study, a new Dynamic Voltage Restorer (DVR) topology has been developed. Voltage harmonic, surge, and sag mitigation can be improved due to the modest capacity of the voltage source inverter (VSI) and the values of the link filter. Using the new RLC filter, switching harmonics can be eliminated. Reduced dc supply voltage capacity is caused by low inductance. With the new DVR structure, the voltage quality can be improved and efficiency can be maximized at the same time. The model-specific RLC filter parameter architecture was described. MATLAB is used to develop and simulate the new DVR with the suggested controlled DVR topology. The control strategy offers a low transient current overshoot and good control dynamics. This is a nice outcome for transient performance simulations. The closed loop controller of the proposed circuit is emulated using fuzzy logic.

State of Charge (SOC) is essential to know the amount of energy left in a battery and to estimate the remaining range of the electric vehicle. Conventional methods for SOC calculation like the look-up table method have the drawback of low accuracy. Model-based techniques for SOC estimation consume a lot of time and need a domain expert. Data Driven techniques have proven to be more efficient in predicting the SOC along with great accuracy. Feed Forward Neural network (FNN) model and Long Short-Term Memory (LSTM) model can directly map the Voltage, Current, and Temperature to SOC removing the internal parameters of a battery in between making them simple. In this paper, FNN and LSTM models are employed to estimate the real-time SOC of the battery and their performance is compared based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and maximum error.

The present work aims at analyzing the flow, heat and mass transfer of Sakiadis and Blasius boundary layer flow in porous medium consideringCopper nanoparticles and Graphene nanoparticles. Further heat and mass transfer characteristics are examined through thermal radiation, convective heat transfer, heat generation/absorption and chemical reaction. Transformation procedure is employed. The nonlinear ODE scheme is fixed using the shooting technique based on Runge – Kutta. Characteristics The preparation of graphs and tables address the physical variables on speed, temperature & concentration and variation in skin friction, local Nusselt numbers and sherwood numbers. We have validated the current alternatives in special cases with existing literature. The copper nanoparticles have taken lesser time for execution when compared to graphene nanoparticles. It’s clear that copper particles are emerged with the base fluid faster than graphene nanoparticles. At the end of that study we found that the Sakiadis flow is greater than the Blasius stream. The mass transfer speed in Blasius and Sakiadis border layers is lower when metal nanoparticles are included in the stream compared with graphene droplets.

Using an experimental inquiry on aluminium alloys and the Taguchi methodology, this study tried to discover and tie the technical factors to the economics of the machining process. This was done by performing the study using the Taguchi technique. That was the purpose of carrying out this project. If you optimise one parameter, it will have a negative influence on the other parameters, and optimising a large number of parameters at the same time will be far more challenging. This paper investigates and reports on the impact that varying milling parameters, such as speed and feed, depth of cut, and rock dust composition, on Al7068/Rock Dust Aluminum Composite. There are several aspects to think about, such as the speed, feed, depth of cut, and the composition of the rock dust. To get commenced, the most fulfilling configuration of the 4 turning parameters was located through using the Taguchi’s Technique L16 Orthogonal array and making modifications to every of the four levels and 4 Components. This was done in order to find the optimum answer. This modification was carried out in order to get the most favourable outcome. After the machining process has been completed, the data is obtained and then compared using software designed for statistical analysis. According to the results of the research, the examination of the S/N ratio placed the feed rate at the top of the list as the most significant factor to consider.

The industries involved in manufacturing of components try to fabricate components at a lower price than the competition to be profitable. The components are made using several processes to achieve the final form. Milling is one of the machining processes highly used during the machining of the components. To achieve lowest cost of production can be achieved only through experimentation and parameter optimization. The main goal of the paper is to enhance optimization of the parameters for the milling of AMMC Al7068/Rock Dust. An array of experiments was conducted using the Taguchi technique for different percentages of rock dust in the composite. Statistical analysis was used to understand the effect of the parameters on the surface finish and strength of the components. The depth of cut rate is 120mm per min, 210mm per min and 250 mm per min. The rotation of spindle runs at 900 rotations per minute, 1100 rotations per minute and 1200 rotations per minute. The feed is zero 0.5 millimeters, 1.0millimeters and 1.5millemeters. Taguchi analysis is utilized to identify the optimum parameter values and correlate feed, spindle speed, depth of cut and rock dust composition. The agreement with the experimental facts proves that the modeled equations were valid.