Gongcheng Kexue Yu Jishu/Advanced Engineering Science

In this paper, a mathematical model for Tomato Yellow Curl Virus (TYCLV) with control parameters is established. By using next generation matrix, the basic reproduction number is calculated. The local and the global stabilities are analysed associated with the disease free and endemic equilibriums. The bifurcation of the model is studied. The sensitive indices for the TYCLV of spread and eliminate are determined. The numerical simulation is carried out using MATLAB.

Incorporating Machine Learning (ML) methods into flight instruction is now seen as a game-changing strategy to update aviation curriculum and raise safety benchmarks. In this work, many ramifications of using ML in pilot training, including the opportunities, threats, and ethical issues that may arise has been investigated. The research technique is methodical, consisting of steps like gathering data, cleaning it up, choosing an algorithm, building a model, and testing it. The results were shown using fictitious datasets whose parameters were based on those utilized in aviation. Predicting critical flying circumstances using the Isolation Forest method and evaluating pilot stress using neural network-based stress level prediction were the two key goals pursued. The results demonstrate ML's usefulness in spotting outliers and tailoring approaches to stress management. The ways in which these discoveries might improve aircraft safety, pilot happiness, and realism in training are emphasized in the discussion. The results of the studies are strengthened by the use of theoretical models like the Yerkes-Dodson law. In the publication, the author stress the importance of the research for future pilots, schools, authorities, and business executives. With its potential to reinvent aviation education, redefine operating standards, and eventually contribute to safer skies, machine learning has a place in the training of pilots. This study paves the way for more investigation into such emerging topics as real-time adaptability, extensive biometric integration, simulated reality experiences, and moral concerns.

In recent years, IoT (Internet of Things) devices and their attendant solutions have become more prevalent, particularly in manufacturing, supply chain management, healthcare, transportation, and numerous other "smart" settings. Thanks to IP (Internet Protocol) addresses designated to IoT devices, cyber-physical systems are now able to communicate with one another automatically. Inadequate protection on these ultimate platforms has resulted in attacks such as denial-of-service, Botnets, identity theft, and information theft. Mirai, Torii, Emotet, Dridex, Trickbot, Gluteba, and QBots are examples of Internet of Things-related threats. With the aid of AI, we can at last create cyber-physical systems that are both trustworthy and secure. Machine learning and deep learning approaches combat cyberattacks by identifying and blocking Botnets. This paper investigates Botnet attacks, which are prevalent in IoT devices due to a lack of security requirements during manufacturing or a lack of user security awareness. Anomaly detection is a potential weapon in the hands of machine learning or Deep Learning for identifying and preventing cyberattacks on Internet of Things devices. In this study, we present a Botnet detection system capable of detecting an attack on live traffic. Next, we employ the Aposemat IoT-23 dataset to evaluate and compare a variety of machine learning as well as deep learning techniques for detecting Botnets based on their conventional characteristics. Gated Recurrent Unit (GRU) is a deep learning model with a detection accuracy of 99.87% for identifying Botnets. Thirdly, we use the Wireshark program to investigate the Aposemat IoT-23 dataset's raw packet captured (pcap) files for attacks. Then, we employ GRU, a deep learning model, to identify malware infections with a 99.89% success rate and reduced time complexities.

Video streaming is based on video applications such as videoconferencing, video on demand, Netflix, Amazon Prime, Youtube, Hotstar, etc. In this paper, we are reducing video delay by improving the quality of experience and quality of service by using real-time protocols RTP and HTTP with the help of cognitive network and RLNC algorithm, Without compromising high-quality video. Here we are comparing both the protocol, which is best for video streaming using 3 different videos (MP4, HD720, and HD1080 pixels) with four-parameter matrices (packet delivery ratio, end-to-end delay, throughput, and energy). Simulation results verify the significant improvement achieved by the proposed protocol compared to other existing RTP and HTTP protocols under different network conditions.

This paper propose an eXtreme Gradient Boosted Multifactor Ensemble Relevance Vector Node Classification based Attack Detection (XGBMERVNC-AD) Model with the goal of enhancing the performance of attack detection in larger wireless network with lesser communication overhead,. Boosting is a machine learning ensemble meta-algorithm. Boosting is employed in XGBMERVNC-AD Model in order to further increase the stability and accuracy of node classification during the attack identification process. The XGBMERVNC-AD Model considers multifactor such as energy, loss rate and trust value, and cooperativeness of each nodes in network for accurate attack detection. The XGBMERVNC-AD model generates ‘n’ number of weak Relevance Vector Node Classifier (RVNC) results for each an input sensor node based on observed energy, loss rate and trust, cooperativeness value. After that, the weak classifier’s output is combined into strong classifier by considering their error value. This helps for XGBMERVNC-AD model to boost the accuracy of abnormal behavior identification in large wireless IoT network with a lower time usage. Experimental of XGBMERVNC-AD model is accomplished by taking metrics such as attack detection accuracy, communication overhead and packet delivery ratio with respect to various numbers of input sensor nodes.

Excessive treatment of cardiovascular disease-related health information has led to an increase in a specific order that limits independent analysis for variable estimation in decision-making. A major illness that can be fatal to a person is coronary disease, which is characterized by a heart condition that tends to malfunction. Proper identification during the first phases of the illness can avert cardiac failure, therefore sparing a person's life. Many studies are being conducted to identify heart problems. Improvements are obtained by accurate diagnosis and early identification of health problems through patient information analysis. The proposed structure offers a unique method depending on the blockages of the heart's major blood vessels—the hybrid Improved Multilayer Perceptron approach with Weighed Quantum Dragonfly Algorithms (IMLP-WQDA) to assess the degree of severity of heart disorders. To comprehend the direction of the qualities influencing the outcomes, the study uses principal characteristic assessment. The study employs a method to identify and extract the affecting variables, depending on the UCI information. The ultimate goal of this effort is to identify the criteria that have the most influence on the detection of heart illness and to acquire 36 traits that are primarily grouped. To do this, the IMLP-WQDA model—which uses a variety of characteristics to determine the severity of cardiac disease—is presented. For the best learning environment, the implemented IMLP-WQDA makes use of back-propagation. To comprehend the direction of the qualities influencing the outcomes, the study additionally uses a principal attribute assessment. The analysis of cardiovascular disease severity using the proposed IMLP-WQDA framework is the work's ultimate product. It has been discovered that, in comparison to conventional methods, the evaluation of the proposed approach offers somewhat greater precision and predictive indicators.

Removing the oily effluent from the wastewater is the project's goal. In the industrial sector, pollution has led to numerous issues. Wastewater is made clean by eliminating oil contamination. There are three types of oil skimmers that are commonly found: weir, oleophilic, and non-oleophilic. The feature that separates oleophilic skimmers is the part that collects the oil (rope, disk, belt, or drum), not how they work. Even a thin layer of oil floating on the water can be eliminated by it. The belt's "oleophilic material" is mostly to blame for this. To recover spilled oil from surface water, an endless belt oil skimmer that floats freely was created. The skimmer makes use of a special, highly efficient belt that is motor-driven. We can prepare water for use in other processes by removing oil. This can reduce pollution from oil spills and prevent water waste. Currently, most of the oil produced by industries ends up in rivers, ponds, and the ocean. National and international environmental regulations are therefore becoming more stringent every day. To comply with these standards, it is cost-effective to build a cheap machine.

We study the oscillatory properties of the second order half-linear difference equation Δ(g(t)Δ|u(t)|^(τ-2) Δu(t))+s(t)|Δu(t)|^(τ-2) Δu(t)-h(t)|u(t+1)|^(τ-2) u(t+1)=0, τ>1 (HL) The core ideas of oscillation theory for this equation will be shown to be quite similar to those for the linear equation. Δ(g(t)Δu(t))+s(t)(Δu(t))-h(t)u(t+1)=0. We establish some sufficient conditions related to the oscillatory behavior of the equation(HL). Examples are provided to illustrate the importance of the main results.

In this paper, a mathematical model of Cervical Cancer is formulated. The boundedness and the positiveness of state variables are verified. The equilibrium points are found. The local and global stability of the model is analyzed. Numerical simulations are performed to show the flow the variables.

This paper discuss the implementation of fractional order controllers to large inter connectedpower systems with HVDC link. In recent years researchers paid much attention on application of fractional order controllers to power system stability issues. The enhanced performance of FOPI controllers oner Integer order controllers can further be improved by tuning the controller parameters. In this work improved version of DE algorithm is used to tune the FOPI controller. The effectiveness of the proposed training algorithm is tested on IEEE 9-Bus test power system using MATLAB/SIMULINK under different fault conditions.

As there are number of investment alternates available in the market gold is also one of them. We are testing the risk return relationship of gold in comparison with risk free securities. For this the data of daily gold prices is taken from MCX for the period 2005 till 2018. To analyze the data descriptive statistics is used to check the normality of the data. The risk return comparison is dome with the help of Sharpe ratio analysis. The results concluded that the gold gives higher returns over the period of time but sometimes it becomes risky to invest in such volatile investment alternate.

The emergence of cloud computing has completely reshaped the way data is being stored and processed, providing scalable and cost-effective responses to the needs of data storage and processing. Nevertheless, there have been serious privacy and security issues that arose through the migration of sensitive data into a cloud setting. Privacy-preserving cryptographic methods came to the fore as a great remedy, allowing computations without revealing the underlying data and thus contributing to maintaining privacy. This article delves into the realm of privacy-preserving cryptography for cloud computing, exploring three key techniques: two of which are homomorphic encryption and secure multi-party computation (SMC), and the latter, which is called differential privacy. Homomorphic encryption allows calculations to be done on the encrypted data, thus preserving the secrecy of the data despite the involvement of the cloud in the process. SMC is such a phenomenon that enables the parties to compute a given function collectively without disclosing the private inputs, thus making it possible to encrypt data transmission from the parties. Differential privacy offers a rigorous basis of mathematics for maintaining the privacy of individuals in the patterns of a statistical database in the process of privacy-preserving analytics. The article provides a clear explanation of the fundamentals, practical examples, and technical issues of these privacy- preserving methods. It underlines the essence of finding the right compromise between personal data protection and good computing speed, and it also tells us about the necessity of the widespread implementation of these tools for a more transparent and safer cyber world.

Clustering is an extremely efficient technique for saving energy in the Wireless Sensor Networks (WSNs). WSN is based on the hierarchical cluster, and hence the Cluster Heads (CHs) will consume some more energy owing to the additional overload for the receiving and the aggregating of data from that of their member sensor nodes, and this transmits all the aggregated data to its Base Station (BS). So the proper selection of the CH has a vital role to play in conserving the energy of the sensor nodes for the purpose of prolonging the WSNs and their lifetime. Here in this work, a proposed Low Energy Adaptive Clustering Hierarchy (LEACH), the Genetic Algorithm (GA) and finally the Chemical Reaction Optimization (CRO) algorithm is considered. The LEACH is a popular clustering algorithm wherein the sensor nodes will elect themselves to be the CH having a certain probability. But the main disadvantage of this algorithm will be that it may choose a CH having low energy that will be able to die quickly and thereby degrades the network performance. So there is a large number of algorithms that are developed for improving the LEACH. The selection of the CH will be a non-deterministic Polynomial (NP)-hard problem. The work has proposed one more energy efficient CH algorithm of selection that has been based on the CRO and the GA algorithms. This type of a CRO algorithm has been developed using some efficient schemes of the encoding of molecular structure that is for the energy efficiency considering several parameters like the intra-cluster based distance, the residual energy and sink distance. In case of the formation of the cluster and its phase several distance and energy parameters have been considered. This algorithm has been extensively tested based on different scenarios of that of the WSNs by means of varying the other sensor nodes and the CHs. These results have been compared with certain currently existing algorithms for being able to demonstrate the proposed algorithm and its superiority.

Recent global events have highlighted the need for autonomous crowd analysis. This technology is gaining interest in computer vision and cognitive science, as well as in the real world, where crowded scenes and their behavior are being increasingly studied. With the rise of crowd phenomena in the real world, crowded scene and their behaviour analysis has gotten a lot of attention recently. When overcrowded towns are subjected to regular crowded events such as strikes, protests, parades, any religious gathering or other types of public gatherings, they face a slew of security challenges. To address these concerns, human security personnel are often deployed to supervise meetings and protect the safety of attendees. However, in crowded spaces such as COVID-19 breakout sessions and public events, it can be challenging to manually manage, count, secure, and track everyone present. An automated system could help to improve safety and efficiency in these situations. Due to significant occlusion, complicated actions, and posture changes, assessing crowd situations is difficult. Crowd analysis is divided into two main categories: crowd statistics and crowd behavior analysis. Crowd statistics determines the level of service (LoS) of a crowded environment, while crowd behavior analysis identifies the mobility patterns and activities of people in a scene. Crowd behaviour analysis has become an essential tool for assuring peaceful event organizing and minimal casualties in public and religious venues across the world. Traditionally, handmade characteristics were used to compute crowd analysis. This paper provides an overview of contemporary exploration study of various crowd behaviour analysis approaches and address new unexplored problem with the eye of deep learning.

Chronic diseases have emerged as a major global public health concern, with their incidence increasing at an alarming rate. Early detection and prevention of these conditions not only improves patient outcomes but also reduces healthcare costs. Leveraging the synergy between Machine Learning (ML) and the Internet of Things (IoT) has enormous potential to revolutionize healthcare, notably through predictive modelling for chronic illness risk assessment. This analysis digs into the challenges and potential involved in developing a prediction model for chronic disease using ML and IoT. It provides a complete overview of current research status and outlines critical routes for future inquiry, addressing topics such as data protection, algorithm refining, and seamless interaction with electronic health records. Furthermore, the significance of wearable gadgets and remote monitoring in forecasting chronic diseases is investigated, as is the possibility of precision medicine for tailored risk assessment. This paper emphasizes the significance of interdisciplinary collaboration and standardization in enabling the effective deployment of ML and IoT-based solutions for chronic disease prediction.

Self-Compacting Concrete (SCC) is known for its higher paste volume, which can lead to increased cracking and a higher creep coefficient, making it brittle. The construction industry must reject such fragility in concrete. An effective method to improve the concrete's flexural and ductility properties is by incorporating fibres. These fibres significantly enhance its capacity for absorption energy and reduce the occurrence of cracking. Three specimens were tested for each case. Nylon fiber has shown better acceptance test results followed by Synthetic fiber when results are compared. To accomplish the study's goals, every fibre was added to concrete mixtures in the following proportions: 0.5 per cent, 1 per cent, 1.5 per cent, and 2% by the weight of cement.

Steganography and irrigation are key areas of the indirect areas of information. Combining the secret messages in digital audio is more complex than the other standard media, such as digital images, the Human Audit System (HAS), multimedia data hiding techniques, digital rights management, secret communications, access phase for controlling the masking process.All the above applications and multimedia steganography techniques should meet two essential requirements such as cognitive transparency and stego subject. The secondary control rate is the high data rate of embedded data. All stego-applications require a higher bit rate of embedded data, but there is a need for diagnostic and encoding algorithms without access to the original multimedia display. The LSB coding is an early technique examined in the area where the digital audio information is hidden and in the drowning area. The main advantage of the LSB encoding system is the overwhelming majority of the Watermark Channel Bit Ratio and Algorithm is a low computational problem that has substantially less strength against major defective signal processing changes. The work mainly focuses on a method that hides the way the viewer suspects it. If anyone knows that there is still data in the audio, it is very difficult to extract the host audio data. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.

Despite the growing reliance on Healthcare 5.0 due to the COVID-19 epidemic, current solutions are not immune to cyberattacks. This research work proposes a community cloud-based Secure Healthcare 5.0 (CCSH) system that ensures security at the user level, mobile application integrity, hardware or device integrity, and communication security. Utilizing Risk Impact Parameters (RIP) and Threat Events for risk calculation, this study presents a Healthcare 5.0 system that is both safe and robust. For security, we employ UICC (Universal Integrated Circuit Card) and TPM (Trusted Platform Module), and for quantitative and qualitative risk assessments, we use the PILAR Risk Management Tool. In addition to these, our proposed CCSH system ensures security on the cloud side and ensures forensic readiness. The proposed protocol has lower computational and energy costs. The CCSH protocol is verified using Burrows-Abadi-Needham (BAN) logic.

The gudgeon pin is one of the engine's most stressed components. Gudgeon pins are used in automotive engines to link the piston and connecting rod. This study looks at the detailed design and analysis of existing Gudgeon pins in order to detect the pin's stress concentration and then minimise it by making necessary changes to the pin's design and mounting. The current work involved constructing a Gudgeon pin and then using Finite Element Analysis (FEA) software to examine and minimise stresses on the pin. They chose silicon nitride as the best material after comparing titanium alloy, nickel alloy, and silicon nitride.

Pioneering the realm of furniture e-commerce, this platform seamlessly intertwines cutting-edge web and AI technologies, ushering in a new era of retail sophistication. At its core lies a dynamic database meticulously curated to offer a diverse array of products, complemented by intricate user profiles. These profiles not only capture individual preferences but also fuel the platform's AI algorithms, enabling it to craft personalized shopping journeys. The amalgamation of web and AI technologies transcends conventional retail boundaries, redefining the shopping experience. Users navigate through a digital landscape tailored to their tastes, with AI-driven recommendations refining their choices. Beyond the front-end, the platform revolutionizes supply chain management, optimizing efficiency and ensuring swift, reliable deliveries. The commitment to a seamless fusion of technology and customer-centricity goes beyond transactions; it cultivates an environment where customer happiness takes precedence. This comprehensive approach reflects a visionary stance in the competitive e-commerce arena, where innovation converges with convenience, enriching the customer's journey and setting new standards in the dynamic world of online furniture retail.

As energy and environmental crises increase, existing refrigeration technologies continue producing greenhouse gases while incurring substantial energy expenses, resulting in the depletion of the ozone layer. However, these disadvantages can be overcome using other gases in the Thermo-acoustic Refrigeration (TAR) system. TAR has no reciprocating parts and has no adverse effects on the environment. The present work describes the numerical simulation of a simple thermocoustic refrigerator (TAR) using Fluidyn software. Fluidyn software is used to predict the effect of the different gases as a working fluid on the performance of the (TAR). The various gases used in simulation software are air, argon, Helium, hydrogen, and xeon. The hot heat exchanger temperature is 300k, and the cold heat exchanger temperature is 297k, so the temperature difference is less; hence, the refrigeration effect is minimal in the thermo-acoustic refrigerator. It is noticed that as the temperature difference increases, the performance of the thermoacoustic refrigerator changes drastically. Although the model did not achieve the original goal of refrigeration, the experiment suggests that thermo-acoustic refrigerators could one day be viable replacements for conventional refrigerators.

Worldwide managing thrash is a serious problem. Industrial waste and construction waste are a major threat since these wastes are randomly disposed of in landfills. Granite waste is generally produced in the form of sludge when the granite rocks are cut and polished to the required sizes. This granite sludge thrash is a significant contributor to the thrash generated by industrial activities. These tiny particles of granite waste close the tiny spaces in the soil, which stops water from seeping through and renders the land useless. Also, mortar prepared with ordinary Portland cement is probably the most largely used man-made material in the world. But mortar production is of concern worldwide because impacts the environment's CO2 emission. Hence, an attempt is made to reduce the use of cement content by using granite waste for cement paste in mortar mixes. Locally available Granite waste (GW) is used in mortar mixes as a filler material. Replacement of cement paste is made by levels of 0, 5%, 10%, 15%, 20%, 25%, 30%, and 40% by weight of cement paste. For each replacement level and for a higher water-cement ratio of 0.7, 0.8, and 0.9 the mortar’s workability and compressive strength are checked. The curing period of 7,14 and 28 days is taken for testing of specimens. Mortar mixes with 40% replacement of cement paste with granite powder have good workability and compressive strength at 7, 14, and 28 days of curing.

The investigation addresses the requirements for energy sharing and operational dependability of a mixed solar pico network. The dynamic integration of clean energy sources is made possible by mixed mini lines, which get around the efficacy or converter cost constraints of AC and DC mini grids. A connecting convert (IC) is required for the smooth transfer of electricity across the circuits with a constant bus voltage of DC and suitable AC sub-grid speed in order to guarantee optimum system performance. This paper introduces a Qasi Z-source converter (qZSC) with integral boost capacity as an IC that is used in an integrated mini grid powered by PV and wind. Additionally, a proposal for dynamic twin loop regulate is made, which allows for optimum power point monitoring and keeps the top DC-link voltage steady. Additionally, a flexible dual loop regulation is suggested, which enhances the general stability of the whole thing and ensures a steady maximum voltage for the DC-link in addition to supporting the highest possible power point monitoring. Simulink or is used to evaluate the suggested network and management strategy and outcomes show effective it is in different situations.

Communication is vital for sharing emotions and thoughts, yet deaf and mute individuals face challenges in expressing themselves. Sign language serves as their primary mode of communication, but its limited understanding among the general population creates barriers. This paper presents a sign language recognition model utilizing convolutional neural networks to interpret hand and facial gestures. Data collection is facilitated through OpenCV and Mediapipe, enabling accurate prediction of words. The model acts as a bridge between individuals with physical disabilities and the general populace, facilitating effective communication and fostering inclusivity. [[11][12][13][14][15][16][17][18]

Communication is vital for sharing emotions and thoughts, yet deaf and mute individuals face challenges in expressing themselves. Sign language serves as their primary mode of communication, but its limited understanding among the general population creates barriers. This paper presents a sign language recognition model utilizing convolutional neural networks to interpret hand and facial gestures. Data collection is facilitated through OpenCV and Mediapipe, enabling accurate prediction of words. The model acts as a bridge between individuals with physical disabilities and the general populace, facilitating effective communication and fostering inclusivity. [[11][12][13][14][15][16][17][18]

The article discusses desertification in Kalyan Karnataka (Previously known as Hyderabad-Karnataka Region) Region, highlighting the need for viable land and soil management in land use planning. Factors contributing to desertification include overuse of resources, improper soil use, and inadequate water supplies. Measurement and monitoring systems using satellite data are crucial for sustainable regional economic development. Geographers use observation, drawing, analysis, and interpretation to create effective land use plans for farming. They focus on land management, protecting it from erosion, salinization, water logging, and nutrient growth. They examine spatial variations in land usage in Bidar, India, analyzing trends over the past 13 years.

A Wireless Sensor Networks (WSNs) serves the purpose of sensing and gathering critical information from its environment. The pivotal consideration in this framework is energy consumption, a factor predominantly influenced by the chosen clustering approach. Although numerous clustering techniques have been devised to sustain stable energy levels for sensor nodes in WSNs, their performance has proven to be less than optimal. This work introduces an innovative method, termed Fuzzy Gaussian Adaptive Clustering Algorithm (FGAC), to elevate clustering performance within WSNs. FGAC initiates with a meticulous data preprocessing phase wherein sensor nodes are systematically organized into clusters, and a designated Cluster Lead (CL) is assigned for efficient data collection and transmission to the BS. Especially, FGAC prioritizes the quality and standardization of sensor data. The outcomes yield a comprehensive representation of the inherent structure of sensor networks, adeptly addressing challenges associated with uncertainty. The proposed method is evaluated using WSN-specific metrics, factors such as energy efficiency, throughput, latency and clustering accuracy. The experimental results demonstrate the effectiveness of the proposed approach, revealing best clustering performance contrasted with other methodologies.

The idea here is to set up automated reminders for the patient at certain times to take their medication. Most people with chronic illnesses have to take many medications at different times, and it's not easy for them to keep track of all the pills and when to take them. This automated announcement system is meant to broadcast certain medicines at specified times determined via its WiFi interface, avoiding confusion and alerting the patient at the appropriate times. A voice alert system for medication leftovers is the focus of this project's design and development efforts. It is common for elderly persons to forget to take their medication at the recommended intervals. Keeping track of all the medications and their names is a huge challenge for healthcare providers. So, by making an announcement, this system will prompt the patient to take their medication at the designated time. Particularly useful for people who need to take several medicines at regular times. In order to remind patients to take their medications at the times prescribed by their doctors, a voice guider for medication remaining systems is developed utilizing RTC.

The wood manufacturing or wood product based industries need to check the wood quality defects to improve the quality of wood products. The traditional wood quality testing techniques has a many problems such as extravagant, low accuracy rate for wood defects and awkward routine. The traditional wood quality testing methods didn’t accurately show the correct location and wood size of internal defects. In this research, deep learning based some effective wood defect detective methods are presented with its summarized details to show which method is invoke better. The performance analysis shows which metric evaluation is produce relevant accuracy results in wood quality defect detection.

Manganese-nickel ferrites MnxNi1-xFe2O4 for x=0.25 were effectively made by using the auto combustion process, nitrates, and citric acid (C6H8O7) as a combustion agent. The morphology and structure of ferrites were examined using the X-Ray Diffractometer (XRD), thermal gravity (TG) for the temperature range of 30°C to 600°C, and scanning electronic microscopy (SEM). The saturation magnetization was determined and the hysteresis curves were shown using a vibrating sample magnetometer (VSM). Fourier transform infrared (FTIR) spectroscopy was used to validate the presence of metal oxide bands and the removal of organic components.

Planning for renewable energy systems and running the grid depend a lot on how well predictions of wind energy production work. Using normal methods, it's not always easy to record the complicated and unpredictable events that happen when wind energy is produced. This is a new way to make density forecasts more accurate and reliable during actual measurements of wind energy output. It uses deep learning models. Its goal is to make a deep learning system that can handle the spatiotemporal aspects of wind data well. Neural networks that are both recurrent and convolutional are used. The suggested method might be able to make probabilistic density predictions for wind energy generation by finding complex patterns and correlations in data on wind direction and speed. The usefulness of our model is checked by comparing it to baseline methods and using real data from wind power production. The results of our deep learning process show that it is more accurate and reliable than other methods, especially when it comes to finding strange events and uncertainty. There are also sensitivity tests that are done to see how stable the proposed framework is in different situations. The research results show that the use of deep learning models can greatly enhance the precision and dependability of estimates of wind power output. In turn, this could make it easier for green energy sources to be added to the power grid.

Public Sectors are under quagmire of political restructuring mayhem subsequent to pro market reforms. Presently, they are prone to highly competitive Global environment posing threat on their sustainability. Under such circumstances a sense of insecurity has prevailed amongst the vast majority of employees who are skeptic with fear of job loss, which is higher than their counterparts elsewhere, either due to complete closure or proposed disinvestment. Behavioral scientists are inquisitive to understand obliteration of leadership, in general, and motivational schemes, in particular, from various perspectives as many theories of leadership that were earlier found suitable to motivate the employees of PSUs are under question and call for revisit. Even though the Indian Public sector attracted the best human resource in brains, talents and skills, the problem of poor performance, lack of competitiveness and low productivity are entirely due to management control structure characterized by multiple principles and multiple goals which oriented PSUs towards a bureaucratic model rather than commercial model of operation and behaviour characteristics which lacks autonomy and accountability. The paper attempts to highlight an overview of leadership challenges prevailing in PSUs pari passu their sustainability post disinvestment policy.

Wireless sensor networks are becoming more and more capable of transporting information as computer and communication technology continue to advance. Wireless sensor networks may not be able to handle a high number of data transfer requests as the demand for information transmission rises. Finding solutions to lower energy consumption during wireless sensor network information transmission is critical for improving the operational efficacy of wireless sensors. Energy carried by the sensor takes precedence in this process. In order to emphasize the significance of the predicted model, security among the sensor nodes is also crucial. The preliminary study examines wireless sensor network clustered routing and begins by looking at cluster creation using the LEACH protocol and analyzing its benefits and drawbacks. The modified bat algorithm is used in this article to assess the nodes' operational status in light of network node usage. The work improves how the localization accuracy between anchor and neighbor nodes is evaluated. Once the modified bat algorithm has determined the node optimization for the best localization accuracy, the Q-learning algorithm is used to locate nodes using its state-action-reward mechanism. For the coverage issue of WSNs, an modified bat algorithm with Q-learning is suggested. This technique can speed up the process of obtaining the ideal solution point set and efficiently extend the life of the system. The simulation revealed some useful benefits of the Modified BAT-Q-learning technique (MBQ), which can be used for later wireless sensor network coverage optimization. The experimental evaluation indicates the proposed MBQ outperforms the existing state-of-art techniques.

This study explores the integration of Non-Orthogonal Multiple Access (NOMA) and Multiple Input Multiple Output (MIMO) technologies to enhance communication systems' performance metrics in both downlink (DL) and uplink (UL) scenarios. Specifically, the research focuses on improving bit error rates, spectrum efficiency, and average capacity rates, while also examining the reduction in outage probability. By leveraging the inherent advantages of NOMA in increasing user capacity and spectrum efficiency, and combining these with the spatial multiplexing and diversity gains provided by MIMO technology, this paper proposes a novel framework that optimally utilizes the spectral resources and enhances the reliability of wireless networks. Extensive simulations and analytical evaluations demonstrate significant improvements in system throughput and robustness, providing a comprehensive solution that could be pivotal for future wireless communication systems, particularly in the context of 5G and beyond. The results highlight not only the theoretical advancements but also practical considerations for implementation, offering valuable insights for both academia and industry in the field of wireless communications. Methods: The transmission subcarriers for each user are allocated according to their optimal power in NOMA-MIMO. Superposition coding is used at the transmitter and successive interference cancellation at the receiver. It includes uplinks, downlinks, and MIMO extensions for NOMA. With QPSK modulation, four users with varying power coefficients, a SNR and transmit power, and two contrasting bandwidths 90 and 210 MHz, the model uses selective frequency Rayleigh fading. Findings: The DL results found that the BER and SE against transmitted power showed the MIMO-NOMA enhanced the BER performance for the best user U4 from 10−1.7 to 10−5.2 at 90 MHz bandwidth , and from 10−1.5 to 10−5 at 210 MHz for transmitting power of 40 dBm. In contrast, the SE performance for the best user U4 is enhanced from 24 × 10−3 to 25 × 10−3 bits/second/Hz at 90 MHz BW and from 19.8 × 10−3 to 20 × 10−3 bps/Hz at 210 MHz BW. Although the outcomes for the UL were obtained in terms of average capacity rate and OP versus SNR at 90, and 210 MHz BW, the MIMO-NOMA result showed that the average capacity rate for the best user U4 performance improves by 12 bps/Hz for 1 dB SNR and the OP is reduced by 15 × 10−3 for 90 MHz BW and by 12 × 10−3 for 210 MHz BW at an SNR of 0.17 dB. Novelty: Two different bandwidths were investigated for the system over a Rayleigh fading channel; and a new power domain scheme was proposed for NOMA-MIMO to increase data rate, throughput, and capacity.

Periyar River basin is the second largest river basin in Kerala, which is spread over three districts in Kerala. This basin consists of several reservoirs such as Mullaperiyar, Idukki, Idamalayar, Lower Periyar etc. Morphometric analysis for the Periyar river basin was worked out using Remote Sensing and Geographical Information System (GIS). Nine subwatersheds were delineated within the basin to calculate morphometric parameters. In this study, the morphometric parameters such as Stream Order (U), Stream Length (Lu), Mean Stream length (Lsm), Stream Frequency (Fs), Drainage Density (Dd), Texture Ratio (T), Length of Overland Flow (Lof), Elongation Ratio (Re), Circularity Ratio (Rc), Form Factor (Rf), Bifurcation Ratio (Rb) and Relief Ratio (Rh) were calculated for the Periyar river basin. Using these tabulated morphometric parameters of subwatersheds, prioritization ranks for subwatersheds were allocated. According to the analysis, Periyar is categorized as an 8th order stream, with the upstream area being identified as the highest priority. This morphometric study provides a better understanding of the Periyar river basin which helps the future development of the basin. GIS based river basin analysis gives accurate data to create a digital drainage network of the basin, watershed management plan and hydrological inference.

Since cloud computing uses distributed and parallel computing to supply its users with several services, including physical resources and platforms, it has emerged as the most important technology of the modern period. Users of technology have stored and retrieved vast amounts of data as a result of their use of it. Therefore, handling massive amounts of data has become increasingly necessary to properly serve their business's and other private consumers' needs. Map Diminish is the most widely used tool for processing this kind of huge data. Still, the hardest part of the job is making sure users' confidential data is secure. Even with the use of many security measures, including authentication, guaranteeing total protection for confidential information is nearly essential because security policies could be compromised. Data protection from leaks and other security breaches is therefore urgently needed. In order to improve data safety, this study proposes the Octagon-CryptoDataMR paradigm, which uses the Map Diminish programming style and cryptographic hash and encryption/decryption methods. To protect data privacy, the suggested model employs a position-based swing hill cipher at the diminish stratum and a sequence rolling twofold hash technique at the map stratum. This architecture professionally secures the data using straightforward cryptographic techniques that provide complex outcomes. To confirm the efficiency of the recommended representation in improving data security, an experimental investigation has been conducted. The findings demonstrate that the suggested model guarantees data privacy while offering improved speed with the least amount of execution time.

Improved Unified power flow controller (IUPFC) is a unique type which is able to regulate power system parameters such as active and reactive power along with voltage magnitude and phase angle is presented based on Flexible AC transmission (FACTS).By Injecting controllable voltage in series to transmission lines, reactive and active power can be controlled. The IUPFC is a combination of a static synchronous compensator (STATCOM) and a static synchronous series compensator (SSSC) coupled via a common DC voltage link. IUPFC helps the transmission lines to obtain their maximum power capacity by avoiding the redundant reactive power which overloads the transmission line. This paper investigates three different modes of IUPFC using a 48-pulse controller i.e., static synchronous series compensator (SSSC),Improved unified power flow controller (IUPFC), and static synchronous compensator (STATCOM). STATCOM, SSSC, and IUPFC are designed to stabilize voltage and provide compensation to reactive power of the power grid system, by using a 48-pulse converter. This paper envelops a innovative approach based on voltage source converter is used for the designing of detailed model of GTO based UPFC is implemented in Mat lab/SIMULINK.

With so much conjecture regarding the performance of a new wireless communication standard (5G), further study was required to take into consideration the difficulties it poses. 5G is regarded as a multi-system support because of its capacity to supply the benefits of vertical industries and the huge interconnection of devices. We must provide the infrastructure to link the wide range of devices and applications we have today. Because of this, network slicing has become a vital technology for the communications network to meet its requirements. Of better understand the many approaches to cutting the next-generation mobile network (NGMN) into smaller, more manageable pieces, this article looks at the use of technologies like "Network Function Virtualization (NFV)" and "Software Defined Networking (SDN)." ML methods to network slicing for networks beyond 5G and the benefits of resource management and mobility prediction by applying ML techniques are also discussed and addressed in detail. ML approaches are also studied and explored.

In this study, Spark Plasma Sintering (SPS) was used to create Al2O3 nano-material (NM) reinforced by adding Cr, Si-C, and ZrO2. All the alloying elements are added to the base material with a constant volume of 3% to the Base material (Al2O3). The strength and toughness facture of the all the Al2O3 NM composites were investigated and results were compared. Throughout the research work 80 µ grams of particle size of Al2O3 powder is considered. The additive particle size 40 nm of Cr, Si-C, and ZrO2 is taken for experimentation. MNC Al2O3 with 3% Cr has shown highest TS as 798 mPa. NNC Al2O3 with 3% ZrO2 and has shown the highest FT as 4.6 mPa.

Cancer is the most serious illness to human beings. Previous attempts to evaluate cancer illnesses assisted in establishing which progressions should be conducted on high-risk patients, lowering their risks. These cells may eventually cause cervical cancer. Cervical cancer is the top cause of death among women, and early identification is essential for successful treatment. Recent research has looked into the use of ML for early diagnosis of cervical cancer, but problems remain. This study compares the performance of various MLtechniques, like XGBoost, DT, AdaBoost, CatBoost, and a Stacking model, in forecasting cervical cancer. A dataset for cervical cancer prediction was used from the UCI ML Repository in the proposed project. Data preprocessing, which involves fixing data imbalances and extracting features, begins with splitting the dataset into a training set and a testing set. The predictive system's performance is assessed using classification accuracy, F1-measure, recall, precision, confusion matrix, and classification report. The suggested model detects cancer among patients using multiple ML classifiers, with a Stacking model having a greatest accuracy of 0.9687%. A results demonstrate that asuggested method achieves better results than standalone ML algorithms on many measures, including projected accuracy. The report emphasises the need of further research in this field, as well as the potential of ML to improve cervical cancer diagnosis. The research intends to contribute to efforts to enhance cervical cancer diagnosis and treatment by offering a novel strategy that solves the problems faced by existing approaches.

Among the most vital components of existence is healthcare. Heart disease (HD) is a major killer in the modern world. The sickness claims the lives of countless people annually. Worldwide, 17.9 million people succumb to heart disease every year, according to the WHO. The application of image classification, in conjunction with the many HD detecting technologies and methods, might further enhance the outcomes. Thanks to developments in deep learning and machine learning, complex models for automatic early diagnosis of cardiac illnesses have been developed. This is all made feasible by the availability of large-scale data utilised for medical diagnostics. Heart disease identification using a DL method based on image classification is the goal of this research. At the moment, the method of choice for image recognition classification is a deep LSTM. The proposed model is tested using the publicly available Kaggle UCI heart-disease dataset, which includes 14 characteristics and 1050 cases. The SMOTE oversampling approach was used to address the issue of this dataset's extreme imbalance. One drawback of classical methods is their inability to generalise well to data that is different from the training set. Observable differences between training and test accuracies corroborate this. Our model attained a validation accuracy of 97% when several performance measures were utilized to evaluate a proposed algorithm. These metrics included accuracy, precision, recall, and the F1 measure. A proposed architecture is shown successful by a detailed comparison of the model's accuracy with different classification algorithms utilising multiple performance metrics. The work's implementation findings propose that the proposed technique outperforms prior efforts in early HD prediction.

The demand for lightweight concrete in various construction applications has grown, aiming to reduce overall structural weight. However, achieving adequate strength while maintaining low density is a challenge. This study evaluates lightweight concrete by incorporating expanded polystyrene (EPS) as a partial replacement for coarse aggregate. Different mix proportions of EPS and coarse aggregate were tested, along with the addition of steel fibers and silica fume to enhance bonding and durability. Physical and mechanical properties of the lightweight concrete were examined and compared to standard concrete. The results indicate that EPS-based concrete offers controllable low density and reinforcing materials like steel fibers improve its overall strength.

In this paper, a creative strategy is advanced for assessing the unique mechanical ways of behaving of supported concrete (RC) section individuals by applying the irregular woodland calculation. First, the proposed method's implementation and the creation of dynamic modified coefficient (DMC) predictive models were elaborated. Then, because of the absence of dynamic stacking tests on RC segment individuals, a mathematical model of RC sections considering the unique change on flexural, shear and bond-slip ways of behaving was created on the OpenSees stage, and the model exactness and the viability were confirmed with the accessible experimental outcomes. In addition, the effects of dynamic modification and the deformation sub-element were investigated by comparing the simulated results of the hysteretic curve using numerical models of varying complexity. Moreover, a mathematical trial information base was laid out to get the preparation information for fostering the DMC prescient models of basic mechanical conduct boundaries, including the yielding bearing limit, extreme bearing limit and uprooting pliability. At last, the consequences of component significance for various info boundaries were contemplated, and the model exactness was assessed utilizing the test set and accessible exploratory information. It was uncovered that the prescient models created utilizing the irregular woods calculation can be utilized to appraise the powerful mechanical ways of behaving of RC section individuals dependably.

The automobile has evolved a lot in the past decade with a lot of advancement in technology and the invention of new technologies that increase haptics, comfort, and safety for the occupant. In the past, if a part was to be covered, a large radius was required for covering to avoid wrinkles and for production with less rejection. As a trend, everyone is expecting crisp designs with the smallest possible radius. The main advancement in technology enhances critical profile wrapping by either manual or automatic edge folding, where style intent is nearly possible to achieve with additional improvements in skin properties. The wrapping of a part in the instrument panel improves haptics and comfort for the occupant. Console Side Decos are one of the options to enhance the aesthetic appearance and soft touch feeling by wrapping it. This paper describes the different technologies used in covering Side Deco and step by step process involved in designing Side Deco of Console. The designed part is validated by Proto Built and again revalidated with the new loop of Proto with product improvement from the feedback of Proto Loop 1

Automated and Autonomous vehicle has attracted considered interest in recent decade by leveraging the current telecommunication networks in order to reduce the traffic in both air and road network and to reduce the cost of fuel cost communication and reduce emissions and finally mitigates the parking related issues in the smart cities. However current telecommunication networks suffer from simultaneously handling the heterogeneous vehicle data traffic and network component failure due to natural disaster. In order to manage those challenges, a new distributed failure tolerant path planning framework has been modelled to generate the virtual path for automated vehicles like drone and grounded vehicles. Due to advent of artificial intelligence and deep learning technologies in the software defined networks, efficient management of the automated vehicle is feasible using existing telecommunication network like 3G/4G and LTE networks. Proposed model integrates architecture with the Amazon or IBM webserver to gather and manage the network data and its communications. Web Server has been deployed with developed virtual path planning algorithm to control the autonomous vehicles on extracting the various information from the sensor component in the ecosystem using telecommunication network. Base station also categories and schedules the data efficiently to reduce the computation time and cost. Especially each node which considered as sensor or transceiver is capable of the operating in fixed speed for specific data size and it is configured in transformers in electric grids, street lights, and overhead electric lines in railways and medians in the road. In order to increase the limit, data compression and signal amplification is enabled through amplifier connected with sensor or transceiver component. Further optical fibre channel is employed for data communication between transceiver and base station in the web server whereas two transceiver can be connected in wireless manner to reduce the dependence of the optical fibre. Controlled signal is transferred to transceiver built-in in the autonomous devices through trajectory of the intermediate transceivers. Control signal carries the information of the virtual path plan as route map containing the route number of the optical fiber channel and specific identification number of transceiver representing start and end point destinations for movement. Finally transceiver communicates the information like speed limit, signal indication like one ways and closed roads etc. Experimental analysis of the proposed model is evaluated on various aspects. In that proposed architecture capable of controlling various devices in high heterogeneous traffics without loss of control signal. Proposed framework can be transformed to concept of intelligent transportation system for eco friendly smart cities.

In e-commerce applications, user reviews considered as significant information to increase the customer and revenue of the product items. Product or item recommendation to user relies on analysis of the online review and clustering of the user similarity. Conventional model towards recommendation on analysis of the review is carried out using the machine learning model. However machine learning model suffers from analysing the fake online review due to curse of dimensionality and data sparsity issues. In this paper, an improved convolution Neural Network is designed to analyse and detect the fake review to the products. Improved convolution Neural network is established on incorporation of the sentiment analysis process in the fully connected layer of the convolution neural network. Initially preprocessing is carried out to remove the stop words and to carry out the tokenization and stemming process. Preprocessed data is employed to the latent Dirichlet allocation mechanism to capture of the intention of the user on the review and latent features of the user is gathered on user profiling. Feature Embedding is carried out and resultant feature is applied to the improved convolution neural network. Improved convolution neural network process the embedded feature in convolution layer, max pooling layer and fully connected layer to identify the fake reviews. Experimental analysis is carried out on the Amazon review dataset. Performance analysis of the proposed model is evaluated against the conventional model on the measures of the accuracy. It proves that proposed model achieves the 99.25 percent accuracy compared to state of ar approaches.

Diabetes can be considered as life threatening diseases to human beings as it is not treated at an early stage. Due to this, diabetes as well as other diseases due to their tensions and regular life chores has to be detected and prevented at early stages. In order to predict the disease at earlier stages, boosting based approach is processed in the existing literature as it is an iterative process which improves the predictive accuracy for various disease predictions. Especially boosting model operates by learning multiple functions with subsequent functions focusing on predicting the instances of dataset but the features for training and computational time is very high. Despite considerable success, boosting still has difficulty on data sets with certain types of problematic training data (e.g., label noise) and when complex functions over fit the training data. In this paper, cluster based boosting (CBB) approach is presented as optimization technique using particle swarm optimization for feature selection. In this proposed approach, partitions of the training data into clusters containing highly similar member data and integrates these clusters directly into the boosting process is carried out after feature extraction and feature selection. CBB boosts selectively (using a high learning rate, low learning rate, or not boosting) on each cluster based on both the additional structure provided by the cluster and previous function accuracy on the member data using feature selection technique termed as particle swarm optimization. Optimized cluster boosting allows CBB to improve predictive accuracy on training data after feature selection. In addition, boosting separately on clusters reduces function complexity to mitigate over fitting. This concept provides comprehensive experimental results on PIMA Indians diabetes (UCI benchmark) data set with Decision tree using K-Means algorithm. Proposed results demonstrate the effectiveness of the CBB approach that uses clusters to improve boosting in terms of accuracy.

Secure data access model on Mobile cloud data has received a lot of attention recently and is viewed as a promising trait. Biometric based authentication to secure access of the mobile cloud data have undergone fast development towards data protection and preventing malicious data threats. In this work, multimodal biometric based authentication technique as fusion model has been projected for data security. The key characteristics of finger print and face patterns include its uniqueness to each individual, unforgettable, non-intrusive and cannot be taken by an unauthorized person. In related methods, feature selection methods explore intrinsic finger print and face on single feature method, but their performance remains undesirable in terms of computational cost. However the extracted features from the both finger print and face pattern are huge with high redundancy. On employment of fusion concept on feature extraction techniques through weighted average strategies, equal error rate is minimized to obtain the optimum weight. In this paper, we propose a combinational model composed differential evolution technique to enhance the recognition of finger print and face patterns to authenticate the user towards data access. The system has been trained in selecting high relevant features on using the extraction techniques such as principle component analysis and linear discriminant analysis. Feature fusion carried out as concatenation on PCA and LDA technique using discriminant correlation analysis feeds the proposed feature selection models by optimal subset of features. The proposed system uses differential model to determine the less no of optimal features for multimodal authentication. Multimodal authentication performed using Multivariate linear regression on less no of optimal features. The analysis of empirical results shows that proposed system produces the best accuracy reflected by 100 percent accuracy on comparing with existing single model biometric authentication models. Further Proposed model has been evaluated and the results shows remarkable efficiency with existing state of art approaches.

Animals play significant and enormous role in the enhancing environment stability, ecosystems and human life. Hence it is considered to be important to protect the health of animals from various disease and to increase the security of animal habitat from forest fire and other natural calamities. In order to accomplish proper life cycle of the animal, effective monitoring becomes mandatory. Department of animal husbandry establishes various manual scheme to preserve and protect the animals. However manual monitoring of animals is highly challenging. On advancement of various sensor technologies, it is made simple but still to enhance the efficiency of the automated monitoring system, internet of things enabled distributed system provides efficient solution. In this paper, smart distributed Internet of things enabled animal health and location tracking system using intelligent wireless sensor and Node Micro Controller Unit is designed and experimented using tiny high calibrated sensors. It is considered as distributed wireless model incorporating monitoring sensor, data collection units, transceiver and base stations. Proposed wireless sensor network composed of temperature sensor, GPS module and PIR sensor acquires the sensed data and transform to the IOT server for further analysis. Temperature sensor senses the temperature of the animal, PIR sensor senses the human movement in the animal boundaries and GPS module transmit the location information to the data collection unit or controller as aggregate information containing sensing data. Further controller transmit those aggregated information to base station for data server for data analytics operations. In this paradigm, base station is employed to control and manage the health and security of the animals on any abnormality detection. On identification of the abnormality, IoT enabled module generates the warning messages as prerecorded voice through speaker or voice processor to alert the people. Further warning message is transmitted to forest officers or police station through GPS module regarding unauthorized access of human beings in the animal boundaries. Experimental analysis of the proposed modules is evaluated on its effectiveness and efficiency in resisting the unauthorized access of people in the animal boundaries and preventing the animal health by timely treatment procedure. Proposed model can be employed to live stock monitoring and pet animal monitoring in residential environments.

Purpose: Transcriptomics has been revolutionized by the development of microarray technology, which makes it possible to simultaneously measure thousands of genes' levels of gene expression. This innovation holds an immense potential in understanding cardiovascular diseases such as Ischemic Cardiomyopathy (ICM) and Non-Ischemic Cardiomyopathy (NICM), which present substantial health concerns on a global scale implying the need for studying ICM and NICM exhaustively. The primary objective of this proof-of-concept paper aims at uncovering potential biomarkers and learn using data-driven method to identify important genes that are differentially expressed. Methods: Microarray data from Gene Expression Omnibus (GEO) repository provided the dataset, which includes expression data from peripheral blood mononuclear cells (PBMC) of patients with ischemic and non-ischemic cardiomyopathy as well as a control group that was age and gender-matched. This research paper endeavours to conduct comprehensive microarray data analysis for transcriptomic profiling aimed at the identification of differentially expressed genes (DEG) associated with cardiomyopathy. Leveraging a data science process model, this study delves into the exploration and interpretation of a specific dataset, GDS3115, curated for its relevance to cardiomyopathy. Results: In total, five DEG showing significant differences in their Gene Expression Profiles to make diagnostic / prognostic analysis were identified. The inferences are tabulated and plotted the DEG in volcano plot as an interpretation of result obtained. Conclusion: Candidate biomarker genes such as CX3CR1C, HSPA1L///HSPA1B///HSPA1A, JUN, ZNF331, RORA are ICM’s therapeutic targets. This study identified several DEG that may be involved in the pathogenesis of ICM/NICM. This abstract synthesizes the research idea, workflow, methodologies employed, and the potential implications of the study in identifying cardiomyopathy related genes via Biological analysis using the GDS3115 dataset.

Pedestrian safety is a matter of significant global concern, with more than 1.3 million pedestrians (ap-proximately 23%) losing their lives in road traffic accidents each year. In Indian cities, the challenges surrounding pedestrian safety are particularly acute, with some cities reporting over 50% of road traf-fic fatalities involving pedestrians. Pedestrians often suffer severe injuries, and in some cases, even fatalities, as a result of head impacts with vehicle components such as the hood, bumper, fenders, and grills during frontal collisions. This research aims to investigate the severity rate of pedestrian injury during accident and evaluate factors like neck force, head injury criteria, head G-force, femur load (upper leg) and tibia load for ascertaining the optimal design and materials to reduce pedestrian inju-ries with a particular emphasis on the primary impact areas such as hood, bumper and fender. To achieve these objectives, the research employed Hyper Mesh and LS-Dyna Simulation tools to evalu-ate pedestrian injury criteria. Simulations covered various materials, including different steel grades, plastic and composites with a specific focus on energy absorption and deformation characteristics. The paper presents valuable findings that encompass design enhancements and alternative lightweight materials for car hood, bumper and fender.

Nowadays, Event based Social Network has become popular flexible platform to share knowledge of various social and technical aspect among the interesting participants in the social network. Due to wide number of the advantages in event based social network , many event organizer generating multiple events across social network which increase the propagation of multiple similar events and leads to user interaction issues in predicting the successive event among competing events. Most traditional approach employed to event popularity analysis utilizes machine learning and deep learning models only on intrinsic and extrinsic properties of the user attention on various context of the event instead of focusing on evolving factors of the user. However dual attention on event and user has not be focused at same time in predicting the successive events among the competitive events to the selective group of the participants. In this paper, Online deep evolving dual attention network for successive event prediction and recommendation to target evolving participant deep is proposed to exploit the popular event to the evolving user group. Initially, dataset containing attribute with missing value has been filled using imputation method and singular value decomposition method is employed to irrelevant attribute reduction. Pre-processed dataset is employed to Latent Dirichlet Allocation towards user and event profiling to obtain the latent information of the user and event in form of feature vector. Next, Feature extraction technique considered as linear discriminant analysis is employed to extract the evolving user feature and event features containing attributes with respect to the scatter matrix. Evolving event features and user features is projected to the online deep dual attention network to compute the successive event to the user. It is carried out on processing the user attention layer and event attention layers and concatenation of the layers to yield a representation learning. It represent the mapping of the user to the event on basis of drift in both user and event feature vector on multifaceted attribute information. Finally event recommendation is provided to evolving user and participant recommendation is provided to the successive event on competition events. Evaluation of the proposed model through various case studies has been implemented and validated across various measures such as accuracy on precision, Recall and f measure along scalability and Execution time.

Heterogeneity of the sensor nodes with limited energy sources will lead to uneven energy consumption and traffic imbalanced across the densely deployed network. On employment of the energy efficient algorithm could try to achieve energy efficiency among the heterogeneous sensor nodes to prolong the network lifetime using mobile sink. However despite of many advantageous of routing algorithm, it introduces hotspot problem on deployment of the multiple mobile sink for data gathering by set of data collection points as it frequently updates sink location information to all the sensor nodes in multi hop manner. In order to mitigate the hotspot problem, a new source traffic defined multiple mobile sink routing protocol has been employed to mitigate the hotspot issue towards improving the energy efficiency and network lifetime on extraction of multiple parameter including energy, coverage, data collection points, data fusion degree, schedule patterns, data redundancy transmission success ratio in the trace file of the particular topology. Particular network topology achieves good scalability, long network lifetime and low data collection latency. In addition, Source traffic defined Clustering techniques projected in this work will self organize the sensor nodes into effective clusters on generation of multiple cluster head to facilitate the data transmission. Cluster head along with information of data collection points plans the trajectory of the multiple mobile sinks for effective data collection from the sensor nodes. Trajectory of the multiple mobile sinks can be enabled using particle swarm optimization to reduce the energy depletion and moving distance of the sink nodes for data collection. Extensive simulations are conducted using NS2 simulator to evaluate the effectiveness of the proposed scheme. The performance results show that proposed model achieves more energy saving per node and energy saving on cluster heads on large traffic while comparing with data collection through multi-hop relay to the single mobile data sinks using existing state of art approaches.

In the present study, silver (Ag) nanoparticles (NPs) were synthesized from aqueous bark extracts of Ficus religiosa and investigated for antibacterial potential. The spectrum of UV–Visible of the prepared Ag-NPs showed an absorption peak at 430 nm due to the excitation of surface plasmon resonance (SPR). The MIC of biosynthesized AgNPs against Xanthomonas campestris malvacearum, the plant pathogen bacteria was determined and was found to be effective at 0.060 mg/ml. The result of antibacterial activities showed that biosynthesized Ag-NPs had an inhibiting activity against Xanthomonas campestris malvacearum bacteria, where the inhibition zone was 25 mm in diameter.

This research presents a novel approach to face recognition by integrating Local Binary Pattern (LBP) feature extraction with Galois/Counter Mode (GCM) cryptographic techniques. Face recognition plays a significant role in various domains, including security and biometrics. The LBP algorithm is employed to capture local texture information from facial images, enhancing robustness against lighting variations and facial expressions. On the other hand, GCM is utilized to provide secure data encryption during transmission, ensuring both confidentiality and authenticity. The proposed system demonstrates promising results in face recognition accuracy while maintaining the utmost data security, making it suitable for real-world applications where robust and secure identity verification is crucial. This fusion of LBP and GCM presents a reliable and efficient solution to address the challenges faced in modern face recognition systems.

Researchers are interested in bioapplication nanoparticle production due to nanotechnology. Recently, metal oxides like ZnO have gained popularity as antibacterial materials due to their durability under rigorous processing conditions and safety for humans and animals. Zinc activates 300 enzymes and affects growth, membrane stability, bone mineralization, tissue growth, repair, wound healing, and cell signaling.Numerous studies have indicated that ZnO nanoparticles boost antibacterial activity. Zinc nanoparticle production using plant resources is a new and promising research area. Using green synthesis, nanoparticles were made from several plants. The plant's stem, flower, leaf, latex, root, peel, stem bark, and fruits were used to create nanoparticles. Zinc oxide nanoparticles were characterized using XRD, FTIR, UV-VIS, EDAX, Particle size analyzer, TGA, and SEM. This review covered zinc oxide nanoparticle production utilizing plant extracts and its applications.

Benzothiazole (BTA) and its derivatives are among the most important heterocyclic compounds, widely found in natural commodities and pharmaceutical drugs. It possesses a large number of pharmacological properties, and many of its analogues have structural diversity, to contribute to the production of new medicinal drugs. BTA derivatives possess a broad spectrum of pharmacological activity. The development of medicinal chemistry containing BTA has been rapid and highly active. BTA chemicals are frequently used in medical care to address a wide variety of illnesses with good results. Current advancements in BTA-based compounds such as anticancer, antibacterial, antifungal, anti-inflammatory, analgesic, antioxidant, anticonvulsant, anti-tuberculosis, antidiabetic, antimalarial, and other therapeutic agents are the focus of this review. New ideas are spurring the development of BTA-containing drugs that are more active, less toxic, and more effective for diagnosing disease

Uran is located near to the Arabian Sea and known for coastal geography. Uran has industrial development, particularly in sectors related to the port and associated logistics. While undergoing changes due to industrialization, Uran tehsil faces challenges related to environmental conservation and sustainable development. Moth plays several essential roles in the environment. They serve as pollinators for various plants, aiding in the reproduction of many flowering plants play crucial role in food web serving food for various predator and also work as environmental bio indicator. The present study was conducted to prepare a checklist of moth from the campus of Veer Wajekar College Uran. The study was carried out from January2023 – August 2023. In this study, a total of 23 species were recorded from 8 different families, Erebidae, Noctuidae, Uraniidae, Sphinigidae, Pterophoridae, Geometridae, Crambidae, Saturniidae, from this Erebidae was the most dominating Family.

People working together to make the world a better place are essential to the well-being of our society, the world economy, and the planet itself. All sustainable measures are necessary for a safe, secure, and prosperous future. Twelve principles, which include ensuring the stability of the chemical process, conserving energy, lowering the toxicity of reagents and end products, and minimising harm to the environment and public health, are the foundation of green chemistry. These principles guide the synthesis process. The green synthesis of diverse chemicals involves a number of physical techniques, including ball milling, hydrothermal processes, ultrasonic assistance, and microwave heating. These techniques are frequently coupled with the utilisation of natural materials. It has been demonstrated that green synthetic technologies are more dependable than conventional chemical processes. The purpose of this paper is to illustrate the benefits of several green synthetic approaches.

In the backdrop of extensive industrial, automotive, and infrastructural growth, urban development has become synonymous with key factors contributing to pollution. Air pollution, being the major cause of global warming, includes numerous harmful substances such as greenhouse gases and carbon dioxide. These gases accelerate global warming as they keep the heat from the sun and block it from entering the earth’s atmosphere. The only solution is the reduction or stabilization of harmful factors in the environment. And here comes the major role of mangroves, which are able to store three to four times more carbon than forests or trees on land. The carbon sequestrated can be calculated through eco-friendly, non-destructive methods like biomass assessment by measuring the dimensions of trees. Avicennia marina is one such species that helps to reduce pollution. The main objective is to study the Avicennia marina species and estimate its biomass in order to calculate carbon sequestration. A total of 100 trees were considered under current study, present at Carter road, Bandra, coordinates Lat 19.068932° & Long 72.821785°. Avicennia marina has sequestrated an average of 7.754 kg/tree of Carbon till date and an average of 28.431 tons of CO2 equivalent was obtained considering above ground biomass and below ground biomass of tree. This study shows that Girth-Breast Height, basal area, Bio-volume, AGB, BGB are directly correlated to Carbon Sequestration.

One possible heterocyclic aromatic chemical compound is benzomidazole. In medicinal chemistry, it is an essential pharmacophore and favored structure. With so many beneficial therapeutic properties, including antiulcer, antihypertensive, analgesic, antiviral, antifungal, anticancer, and antihistaminic properties, it plays an extremely important function in medicine. The literature review demonstrates that the Derivatives of benzimidazoles are useful compounds. A variety of evaluations published in the field of organic chemistry and medical speciality research have confirmed that the molecules in these compounds are efficient against a variety of microorganisms. Owing to their significance, artificial organic chemists started to pay attention to the synthesis techniques. As a result, we sought to gather the chemistry of several byproducts of substituted benzimidazole as well as several medical specialist activities within the gift review.

The crystal structure of N, N,4-trimethylbenzenesulfonamide has been elucidated, and its Hirshfeld surface analysis has provided valuable insights into the intermolecular interactions and packing arrangement in the crystal lattice. The study highlights the importance of hydrogen bonding, van der Waals interactions, and pi-pi stacking in determining the structural organization and stability of the compound in the solid state. Such information is crucial for understanding the properties and behaviour of N, N,4-trimethylbenzenesulfonamide in various applications, including pharmaceutical and chemical industries.

The quiet spread of plastic trash in our oceans threatens to create an unbeatable calamity. The consequences of this extensive threat may continue for millennia in the coming decade, unstable marine ecosystems for up to 10,000 years. The scope of this issue cannot be understated, with an astonishing 5.25 trillion micro and ultra-fine particles spread over our seas and an estimated 381 million tonnes of plastic polluting our waters each year, which is anticipated to quadruple by 2034. The varied impact of plastic pollution on the marine environment is extensively examined in this review. This synthetic threat covers 88 percent of our oceans, harming marine species through ingestion and habitat disruption. At the same time, the presence of the Great Pacific Garbage Patch and similar accumulations elsewhere demonstrates the terrible worldwide situation. Other contaminants, such as industrial waste, sewage, and oil spills, compound this situation, harming nearly 817 marine species, a number that has increased by 23% in recent years. Locally, the scenario in regions such as India, where big cities generate 343 tonnes of rubbish, highlights the severe threat to biodiversity and coastal ecosystems. To deal with these terrifying truths, immediate action is required. This assessment underlines the urgent need for worldwide awareness, proper waste management, and rigorous legislation to prevent this potential disaster. It stresses the vital part that each individual plays in slowing the tide of plastic waste, demanding collective action to protect our oceans, our planet's life.

The burgeoning volume of credit card transactions, coupled with the increasing sophistication of fraudulent activities, necessitates the development of robust, intelligent fraud detection systems. This paper presents the development of a machine learning-based fraud detection system aimed at identifying and mitigating fraudulent credit card transactions in real-time. Traditional rule-based systems, while effective to a degree, often fail to adapt to the dynamic and evolving nature of fraudulent behaviours. Consequently, our approach leverages advanced machine learning techniques to enhance the accuracy, speed, and adaptability of fraud detection mechanisms. Feature engineering plays a critical role in our system, transforming raw transaction data into meaningful features that enhance the model’s ability to discern patterns indicative of fraud. Features such as transaction amount, time between transactions, geographical location, and merchant category are meticulously crafted and selected based on their predictive power. Additionally, anomaly detection methods are integrated to identify deviations from typical transaction behaviors, further bolstering the system’s detection capabilities. The proposed system employs a supervised learning paradigm, utilizing historical transaction data to train various machine learning models, including Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting Machines. Each model is evaluated based on its precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC), to ensure a comprehensive assessment of performance. Special emphasis is placed on the imbalanced nature of the dataset, a common challenge in fraud detection, where fraudulent transactions constitute a minute fraction of the total transaction volume. To address this, we incorporate techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and cost-sensitive learning to balance the dataset and mitigate bias. The system is designed to operate in a real-time environment, necessitating efficient data processing pipelines and model inference mechanisms. Moreover, the interpretability of machine learning models is paramount, particularly in financial contexts where transparency and accountability are critical. We employ techniques such as SHapley Additive exPlanations (SHAP) to provide insights into model decisions, facilitating trust and acceptance among stakeholders. This interpretability also aids in the continuous monitoring and refinement of the system, as it enables the identification of model drift and the incorporation of new fraud patterns as they emerge. Extensive empirical evaluation is conducted using a publicly available dataset of credit card transactions, demonstrating the efficacy of our approach. The results indicate that the machine learning-based system significantly outperforms traditional rule-based systems, achieving higher detection rates with lower false positive rates. Furthermore, the system exhibits robust performance across varying transaction volumes and fraud types, underscoring its versatility and scalability.

The primary aim of this work is to utilize Convolutional Neural Network (CNN) models, which are based on deep learning, to analyze numerical data frequently employed in the medical field. This endeavour is crucial because liver failure ranks as the second most prevalent disease worldwide, and the number of donors is insufficient to meet the demand from recipients. It is essential to identify patients who have a high likelihood of survival and improvement following liver transplantation. To accomplish this, a dataset containing only numerical values for liver transplantation must be transformed into image data to take advantage of Convolutional Neural Network's capabilities. To achieve this, the raw data is first normalized and then subjected to a logarithmic transformation. Each normalized feature is then assigned to a specific area of the image grid. This generates images with different brightness zones based on the numerical value of each feature. The Grey Wolf Optimizer is for training the hyperparameters of the Convolutional Neural Network models. The first Convolutional Neural Network model has an accuracy rate of 91%, making it the best model. Eventually, the Ensemble Exponential Weighted Average Grey Wolf Optimization-based Convolutional Neural Network approach successfully classifies the survival rate of patients with accuracy of 93% when ensemble learning is employed.

This paper presents a comprehensive analysis of the potential implementation of IoT frameworks in Chhattisgarh’s smart cities. Despite the challenges such as infrastructure limitations, security concerns, and high costs, the study reveals significant opportunities for improved services, economic growth, sustainable development, and innovation. The paper concludes with recommendations for successful IoT implementation, including investment in infrastructure, prioritizing security, developing technical expertise, industry collaboration, and promoting innovation.

Ratnagiri district has changed a lot in the last 40 years. he. Population is increasing at a faster pace.Therefore, it is seen that different elements are affected in the population. Ratnagiri district shows a huge difference between population growth in rural areas and population growth in urban areas. The population has increased in different proportions in the taluks of Ratnagiri district. . In the last 40 years, Ratnagiri, Guhagar, Dapoli, Mandangad, Chiplun, Sangameshwar, Lanja, Rajapur and Ratnagiri taluks show a large population change and growth projections. There is an increasing proportion of the population of the past 40. Finding the present appears to me.The percentage of population growth in urban population from 1981 to 2011 is studied over the last 40 decades. In the present paper, the population change in Ratnagiri district has been reviewed.

Because of changing borrower habits, a lack of readily available credit histories, and inaccurate or nonexistent financial data, credit risk assessment is particularly difficult in developing economies. Such restrictions make it difficult for conventional statistical and machine learning models to make the kind of reliable, interpretable decisions that are required when lives are on the line. This research presents a new method for real-time credit risk assessment called Neuro-Symbolic AI. It combines the power of neural networks for pattern identification with symbolic logic's reasoning and transparency. For feature extraction from diverse datasets (such as mobile use, transaction histories, and behavioural indicators), our model incorporates deep learning. Additionally, symbolic reasoning modules are used to encapsulate domain knowledge, lending rules, and regulatory limitations. This hybrid system can reason with some degree of ambiguity, draw logical conclusions, and adjust to fresh data as it comes in. In addition, the symbolic component makes the model more explainable, which is an important quality for financial system confidence and compliance. In places where alternative sources, such as mobile money platforms and social financial behaviours, are numerous but formal banking data is sparse, our study focusses on applying this neuro-symbolic architecture to underserved financial ecosystems. The algorithm was trained and evaluated using data from South Asian and Sub-Saharan African fintech lenders and microfinance institutions. In terms of interpretability, robustness to data sparsity, and prediction accuracy, the experimental findings show that the neuro-symbolic AI model surpasses the baselines of traditional machine learning and deep learning. The symbolic layer also enables domain experts to step in and intervene with decision logic as required and efficiently manages exceptions based on rules. It indicates in a favorable direction for building more accessible real-time financial services that can fit the particular demands of developing countries.