Gongcheng Kexue Yu Jishu/Advanced Engineering Science

This study proposes a novel approach to power flow control in on-grid hybrid microgrids by combining AC-DC microgrids with a modified unified phase-to-phase power controller (UIPC). The AC microgrid and the DC microgrid represent a hybrid system that is normally connected to the grid. Instead of power converters connected in parallel, these microgrids are connected to one another via a modified UIPC. The first contribution of this thesis is a modification of the UIPC standard, which makes it possible to control the energy exchange in AC-DC microgrids with fewer power converters than three are required in each phase of the conventional UIPC structure. LPC control methods use an artificial neural network (ANN) controller. Using the H∞ filter method, the ANN tool is optimized to eliminate format errors in the membership functions. The DC microgrid is used to supply the LPC with the required DC voltage via the BPC. After successfully powering the DC microgrid with a photovoltaic instrument, LPC #039; The intermediate circuit voltage changes. For the DC- -BPC, the NDO-MS-SMC (Strate Strong Absolute Multi-Mass Slip Mode Control) strategy is available to stabilize the DC link jitter, which is based on the observation of non-linear disturbances. The simulation results demonstrate the effectiveness of the proposed energy management strategy in combination with the flow control technique for smart grids in Advanced UIPC. An artificial neural network (ANN) is used to adapt UIPC converters; this is proposed in this study as a comparable extension.

The most vulnerable groups to falling asleep behind the wheel include shift workers, business car drivers, and truck drivers. The majority of collisions are caused by intoxicated drivers. Since the business owner is held accountable, the burden falls on them. It can result in financial loss. We demonstrate an adaptive driver and business owner alert system in this presentation, together with an application that informs the business owner about their driving habits. The two main factors that cause serious accidents that result in injuries, fatalities, and property damage are alcohol use and drowsiness. We recommend a system that makes use of a variety of sensors to address this issue. These sensors are utilised for seat belt monitoring, drowsy driver detection, and alcohol detection. The buzzer is used to warn the driver if they feel sleepy, their seatbelt isn't fastened, or alcohol is detected. The motor stops whenever the sensor values fall outside of the threshold value range. In an emergency, the GPS module pinpoints the location and sends this information to the designated person or person in charge of the ward through GSM. Cortex M3 will have complete control over the entire system. By warning the motorist, this technology can help to reduce serious traffic accidents.

Flyash blocks made of autoclaved aerated concrete (AAC) are lightweight employed quickly in high-rise structures. One of the sustainable materials is the AAC Block, which is made from fly ash from industrial waste. AAC block masonry in fills contribute to a structure's low weight, thermal conductivity, and speed of construction. To increase the effectiveness of the AAC block masonry, material level research is required. The literatures lack a awareness of the material level of behaviour of AAC block construction. In order to comprehend the compressive stress-strain behaviour of AAC Block masonry and its components, experimental study is described in this work. Compression testing is used to determine how well AAC block masonry responds to weak and strong mortar.

This article covers a batch arrival finite Markovian queueing system with multi-server support in phase-wise service and consumer impatience. We considered two types of customers with different arrival rates in terms of batches and state-dependent service. Poisson and Exponential distributions are assumed for arrival and departure times under FCFS mode. We model the system as a Markovian process and generated transition probabilities for different number of customers based on Runge-Kutta of order four. We also determined system’s performance indices. We have demonstrated the sensitivity analysis results and examined the effect of various input parameters on the system's constants.

A Stock Market is a place where company shares are traded to stockbrokers. Stock price prediction is one of the most challenging problems as a high level of accuracy is the key factor in predicting a stock market. Many methods are used to predict the price in the stock market but none of those methods are proven as a consistently acceptable prediction tool due to their volatile nature. The stock market prediction has been one of the popular topics in the financial domain. Prior to the advancement in Machine Learning and Artificial Intelligence, many Statistical models were employed. Even though these models gave almost accurate results but using these models was not efficient and time-consuming because of the market’s rapidly changing behavior. Due to the advancement in Machine Learning and Artificial Intelligence, it has become possible to employ different models like Neural Networks, Regression, Decision Trees, etc. which give us results as accurate results as Statistical Models if not better in a very short amount of time. This paper, going to make use of an artificial neural network (ANN) model with backpropagation to forecast stock prices. ANN can generalize and predict data after learning and analyzing the initial inputs and their relationships. A feed-forward network and backward propagation algorithm is used to predict stock prices. This research work introduced a method that can find out the future value of stock prices on a particular day and also predict a bullish or bearish market based on the current price using the ANN backpropagation algorithm.

Steganography is one of the essential techniques in cryptography to transfer data securely over internet. In this technique, the digital images are used as a cover to relay sensitive information. The Least Significant Bit (LSB) method is one of the simplest ways to insert secret data into a cover image. The goal of this research is to offer a new image steganography approach based on chaotic cryptography. The selection of pixel position in the cover image is the most important security feature of the proposed approach. This chaotic based steganography method uses pseudo-random numbers generated through logistic map to select pixels in different size of images. The data has been embedded in the least significant bits of Red (3), Green (2) and Blue (3) positions of random pixels of image. This proposed adaptive stegano technique is validated for the quality of algorithms using the Peak Signal Noise Ratio (PSNR) and Mean Square Error (MSE) measurements.

DDoS attacks, which target a network as a whole, are the most common name for these kinds of attacks. The attacks make use of weak spots in the authorised group's website framework, for example, to get unauthorised access to sensitive information. The author of the current study used a legacy KDD dataset in his or her research. If you want to know where DDoS attacks stand right now, you need to use the most recent available dataset. In this research, we employ a machine learning technique to classify and forecast DDoS assault types. The classification methods Random Forest and XGBoost were utilised for this purpose. A comprehensive paradigm for DDoS attack prediction was provided in this study. The suggested work makes use of a Python simulator built on top of the UNWS-np-15 dataset, which was obtained from the GitHub source. Following the implementation of the machine learning models, a confusion matrix was created to evaluate the accuracy of the predictions. For the initial categorization, the Random Forest method achieved an impressive 89% in both Precision (PR) and Recall (RE). Our proposed model has an Accuracy (AC) of 89%, which is excellent and more than sufficient. Both Precision (PR) and Recall (RE) for the XGBoost algorithm hover around 90% in the second classification. Our proposed model has an Accuracy (AC) of 90% on average. We found that the accuracy of defect assessment was significantly increased, from around 85% to 79%, when compared to prior study.

There has been a significant paradigm change caused by artificial intelligence in healthcare, and it is having an effect on diagnostic methods, drug discovery, health analytics, interventions, and much more. In this work, we explore the use of chatbots powered by artificial intelligence to better meet the requirements of patients and their loved ones. These systems depend mainly on machine learning techniques and Natural Language Processing. In this paper, we detail a potential use case for an AI-chatbot that would provide help to pregnant women, new parents, and families with young children by providing appropriate knowledge and advice when required.

As a new subfield of neural networks, deep learning has proven to be highly effective at tackling difficult learning challenges. It is becoming increasingly difficult to build high-performance solutions of neural networks with deep learning as the dimension of the nodes grows in response to the needs of actual applications. In this study, we propose a deep neural accelerator unit (DLAU), a scalable accelerating architecture for large-scale neural networks using deep learning that uses field-programmable gate matrix (FPGA) as the computer chip prototype, to achieve both performance gains and reduced power consumption. The DLAU accelerator uses tiling approaches to investigate locality in deep learning applications and makes use of three pipelined computing units to increase throughput. The DLAU accelerated is capable of speeds comparable to those of Intel Core2 CPUs, as shown by experiments conducted on a cutting-edge Xilinx FPGA board. Index Terms-Deep learning, field-programmable gate array (FPGA), hardware accelerator, neural network.

The increasing complexity of the DSP systems demanding higher computational performance in its architecture. But the traditional DSP arithmetic has limits in terms of speed of calculations. More over in some applications speed is more important than accuracy. In order to further enhance performance, approximate arithmetic circuits are designed with some loss of accuracy to reduce energy consumption and increase the speed. These approximate circuits have been considered for error-tolerant applications. In this paper, we propose an FIR filter based on Rounding Based Approximate (ROBA) Multiplier. In this multiplier the operands are rounded to the nearest exponent of two. This approximation will lead to simplification of multiplication operation thus reducing area and increasing speed. As the multiplier is the slowest element in the system, it will affect the performance of the overall FIR filter. The proposed ROBA multiplier based FIR filter was compared with HAAM and DRUM multiplier based FIR filters. The results shown significant reduction in the power and area of the FIR filter with proportional improvement in the multiplier speed. The Filter was implemented and tested using Virtex 5 FPGA at an operating voltage of 1.0V using Xilinx ISE environment.

One of the most often used mathematical procedures is multiplication. usage of mathematics in fields as disparate as computational brain networks and audio processing. One of the main forces at work in such contexts is the multiplier. energy consumption, critical path delay, and resource use. In FPGA-based systems, the intensity of these effects tends to grow. However, most state-of-the-art ideas have been previously made for ASIC-based systems. A few FPGA-based systems that can only handle undocumented integers, requiring extra circuitry for verified operations. In order to overcome these restrictions for FPGA-based applications, This research supplies an increase that balances low latency or energy efficiency using a signed-number-based exact signing scheme that achieves optimal area utilization. Vivado's area-optimized multiplier is used as a comparison. Our IP deployments may deliver 40%, forty per cent, and 70% savings, respectively.

Hardware security is crucial for the vast majority most uses including banking via the internet, internet shopping, army satellite, wireless technology, and gadgets that use electricity. imaging and other media digitalization. The practice of cryptography is linked to the transformation of plain text into gibberish and vice versa. Parallel key the use of encryption, the use of hash and public-key encryption are the three main types of cryptographic methods. The Automatic Encryption Standard (AES) with algorithms with symmetrical keys are two examples of this. It is common practice to use one identical key for cryptography and decompression. It's a lot quicker, simpler, and requires less computing power to accomplish. The proposed 256-bit AES solution is highly optimized. key plan and smaller byte units with power as well as area. Efficiency was possible because to the re-use of the S-box structure.

Complex 2.5D/3D SOC design architectures with high-performance computing beyond sub-10 nm advanced node technology may be investigated and implemented. Thanks to node scalability, heterogeneous integration, and advanced design, we can think beyond the bounds of Moore's Law. limit the reach of the legislation and account for excessive power loss In the context of nanotechnology and low-power VLSI circuit designs, the science of quantum computation has seen extensive study of reversible logic functions. Digital circuits that are capable of reversible computation have significantly lower power consumption. In this work, we propose a ground-breaking new layout. Making use of logic gates that may be used in reverse, an ANN is developed. A After searching the relevant literature extensively, just a few pieces with similar content were located. To the best of our abilities, our proposed method is correct.

Forecasting the amount and pattern of precipitation (rainfall) in a certain geographic area for a set time period, usually in the near future, is known as rainfall prediction. For many purposes, including farming, managing water resources, flood control, and disaster management, accurate rainfall projections are crucial. Establishing models of prediction that can predict future precipitation requires the use of past meteorological data and machine learning techniques. Weather patterns are often non-stationary, meaning they change over time. Machine learning models assume stationarity, which can be a limitation when dealing with climate and weather data that exhibit trends, seasonality, and long-term shifts. The proposed model uses Blended stacking model, is a technique in machine learning where multiple models are combined to improve predictive accuracy. Stacking can make rainfall prediction models more robust to changes in data distribution and weather patterns. It can adapt to varying conditions and provide consistent performance over time.Blended stacking can provide not only point predictions but also measures of uncertainty. By aggregating predictions from multiple models, it can offer insights into the variability and confidence associated with rainfall forecasts.

Safety of data will be one of the main concerns our planet will face in the near future, and cyber security has a crucial function to play within the world of technology. As soon as there is talk about cybercrimes, the government and many corporations take various actions to stop them. Cybercrime has been spurred on by a variety of actions and it is constantly growing daily. Along with its primary focus on new technical techniques, trends, and ethical issues originating from cyber security, the study also provides an overview of the challenges encountered by cyber security as a result of technology breakthroughs and innovation.

Objectives: India has experienced a rapid escalation of overweight and obesity rates, surpassing the global average. Obesity has emerged as a significant health concern with multiple contributing factors, including biological, genetic, social, environmental and behavioral influences. Among adolescents worldwide, obesity is a prevalent issue and poor dietary habits are considered to be the fundamental risk factors for many chronic diseases such as type II diabetes, cardiovascular disease, cancer and obesity. To address these complex problems, the use of Machine Learning (ML) techniques can facilitate the identification of patterns and factors of obesity by analyzing extensive nutritional epidemiology databases. The main aim of this study is to perform multi-class classification of obesity of adolescent girls using an ensemble of ML classifiers. Methods: A primary dataset of 2000 adolescent girls, gathered from schools and colleges in Chennai, India are input for this study. Our study investigates the obesity using ML classifier algorithms by employing 75% of the dataset for training and the remaining for testing. We have applied eleven well-known algorithms to compare and evaluate their prediction accuracies using the training and testing datasets. To assess the performance of the classifiers, the metrics such as precision, recall /sensitivity and F1-Score have been calculated and analyzed for predicting the actual outcomes. Findings: Based on our computer experimental results, the CatBoost algorithm has achieved the highest accuracy of 90.6%, when we compare with that of other classifiers. Novelty: The uniqueness of our study is collecting and analysing the primary dataset which is focused on adolescent girls, age ranging from 16 to19 and classifying the dataset using various ML algorithms. But, the most of the other studies are not focusing on this specific age group for predicting the obesity. By understanding the factors contributing to obesity in adolescent girls, the researchers can study the long-term health impacts and help the society to prepare better for future healthcare challenges.

Nowadays people are using Electronic Systems widely in their daily life. Without any Electronic product, there is no life for human beings on Earth now. The requirements of the present generation of people are high Speed, low Power and low Area Electronic Systems. A number of arithmetic circuits are used in Digital Systems. Different arithmetic circuits are Adder, Multiplier, Divider etc. There are different multipliers with different techniques to obtain Products from Multiplier and Multiplicand.Radix-4 Multiplier is one of the Multiplier. In the Radix-4 Multiplier number of Partial products is n/2, where n is the number of bits of Multiplier. Speed of operation, Power dissipation and area of this Multiplier are high. By decreasing the number of Partial products of n-bit Multiplier, Area, Power Dissipation and Propagation Dely can be decreased. In Radix-8 n-bit Multiplier number of Partial Products are n/3.This decreases the Area, Delay and Power Dissipation. Radix-4 & Radix-8 Booth 8-bit Booth Multipliers are designed and Implemented on FPGA. Delay, Power Dissipation and Area are compared for both Multipliers. The comparison indicates that Radix-8 Booth Multiplier is best Compared to Radix-4 Booth Multiplier with respect to Delay, Power Dissipation and Area. So, we can replace Radix-4 Booth Multiplier with Radix-8 Booth Multiplier. Problem definition: Nowadays, there are many Electronic Systems used by people for different purposes. They may have different Subsystems in them which may have low Speed, high Area and high-power Dissipation. Multipliers are used in a number of applications like Digital Filters, Multimedia etc. Radix-4 is such a multiplier. The drawback of Radix-4 Booth Multiplier is number of Partial Products is n/2 where n is the number of bits of Multiplier. This leads to high Delay, Power Dissipation and Area. They can be decreased by decreasing the number of Partial Products. In the Radix-8 Booth Multiplier, the number of Partial Products is n/3. the complexity of radix-4 multipliers can lead to challenging signal routing on integrated circuits,

The study of the stress concentration factor for rectangular composite plates with geometrical discontinuities become essential due to the their extensive practical applications in different fields of engineering. The aim of this work is to contribute in the understanding of the behavior of stress concentration factor around hole in orthtropic plate while considering the effect of some paramters on the SCF. A numerical investigation based on a two-dimensional finite element approach is carried out using Abaqus. Indeed, the effect of the fiber orientation, stacking sequence and the hole size on the SCF is determined for the plate with circular hole subjected to unidirectional loading. As well as, the influence of the axis ratio of the elliptical hole with the loading's direction on the SCF and the stress distribution are shown.

World is changing swiftly due to the advancements of the modern era. Every industry uses the Web, and having access to it has become a bare minimum for everyone. Because of the social network industry's phenomenal growth, people are using social networking sites to share their perspectives on current events. Having observed the competition, the product and service feedback and its analysis has become essential. Sentiment analysis commonly referred to as opinion mining, is a typical activity for starting conversations that seeks to uncover the feelings that underlie opinions in texts on a number of subjects. Subjects, such as cinema, consumer goods, and societal issues have been the recent area of study among the researchers to analyze opinion of general public. Twitter has grown in popularity over the past few decades. Today, it is used by millions of people to communicate details about their daily lives and feelings. These data can be processed and analysed automatically by applications using techniques like sentiment analysis and topic modelling. Keeping the popularity of twitter and the sentiments that are shared on this micro-blog in mind, the present study examines the results of various sentiment analyses performed on Twitter data.

Radiotherapy planning is a critical aspect of cancer treatment, where the optimal selection of beam directions and dose distributions significantly impacts treatment efficacy and patient outcomes. Traditionally, this process involves time-consuming manual trial-and-error methods, leading to suboptimal treatment plans. To address this challenge, optimization strategies based on advanced artificial intelligence (AI) techniques have been explored. This paper presents an investigation into the application of AI-driven optimization methods for beam direction and dose distribution selection in radiotherapy planning. The study proposes an approach utilizing Convolutional Neural Networks (CNN) to learn the relationship between patient anatomy and optimal beam orientations. The CNN model is trained on a dataset comprising anatomical features and corresponding beam orientations, derived from a column generation (CG) algorithm. Additionally, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms are employed to optimize the CNN's weights and biases to attain the Fluence Map Optimization (FMO) objective function. Experiments are conducted using data from 70 clinical prostate cancer patients. The results demonstrate the effectiveness of the CNN-PSO and CNN-GWO approaches in generating beam orientations that yield treatment plans with dose distributions comparable to those obtained through traditional CG. DVH analysis of the resulting plans for different anatomical structures validates the accuracy and feasibility of the CNN-GWO model in radiotherapy planning. The findings of this study highlight the potential of AI-driven optimization strategies to revolutionize radiotherapy planning by significantly reducing planning time and enhancing treatment plan quality.

The initial estimate of the critical voltage leading to the initiation of the electric arc carries great economic importance, because it positively affects the technical approach adopted in the analysis of electrical connections. This is especially true in many countries that tend towards a preventive approach by using of a large number of insulators. It has been found that empirical mathematical equations and models somewhat facilitate the estimation of the critical flashover voltage. However, these estimates can vary because of distinct constants associated with each type of insulator. The novelty of the proposed work is the development of new method by hybridization of two artificial intelligent techniques to make easy the estimation of critical flashover voltage value of insulators. The hybrid principle has been chosen, between the Particle swarm optimization (PSO) method, with a similar principle based approach, the Whale optimization Algorithm(WOA). The goal shifted towards employing artificial intelligence to find uniform arc constants that are compatible with all available insulators, and approach the experimental values achieved for each insulator. We randomly select initial values for the arc constants. Using the first method, we reach optimal values for these constants, and then we repeat the process several times to create a database of good solutions. We use this database as initial values for the second method to guide the solution, and we repeat the process each time. Taking into account the objective function on one hand and achieving the closest possible convergence with the experimental values on the other hand, we ultimately obtain two unified values for the arc constants that can be adopted in calculating the critical voltage for each type of insulator. After multiple iterations, we have achieved favorable results. The result obtained proved to be better than what was reached previously, and this confirms that the combination between the theories is a good solution that can be relied upon. The obtained values demonstrate good alignment with the experimental data, indicating their practical importance, especially in the context of technical study and economic considerations.

In this study, copper metal matrix composites (CMCs) loaded with cerium oxide nanoparticles were subjected to the friction stir method (FSP). The CMCs have several desirable qualities, such as their portability, power, growth potential, and innate intelligence. Friction stirs processed surface composites (FSP-ed) parameter optimization using the Taguchi method is also the subject of this investigation. In terms of hardness and tensile strength, this method analyzed the optimal combination of the process parameters and delivered the optimal solution. The optimal tensile strength and hardness of the material, respectively, have been discovered to be 104.043 VHN and 440 MPa. The best settings were 1200 rpm for the rotation, 35 mm/min for the transverse, and 3 passes for each direction.

This study looks into the integration of combining hybrid cars with solar and wind power in order to advance sustainable transportation. The report provides a complete examination of the combinatorial potential combining hybrid transmissions and green energy integration, concentrating on technology improvements, customer habits, market dynamics, and governmental frameworks. The evolution of the hybrid engine is reviewed, with advances in propulsion systems, storing energy, as well as control algorithms highlighted. To understand the elements influencing electric car adoption, consumer tastes and financial factors are investigated. Developments in the market as well as regulatory policies are examined in order to shed light on the bigger picture of sustainable transportation. The policy ecology is deconstructed, with an emphasis on the importance of government enticements, infrastructure for chargers, and grid integration. By combining these factors, this study provides concrete recommendations to encourage the broad adoption use hybrid vehicles powered by energy from renewable sources, opening the path for an environmentally friendly and resource-efficient transportation ecology. This research contributes to a more comprehensive understanding of both the technology and policy context required to propel the shift to greener commuting solutions.

Attendance monitoring is very important at schools, colleges, offices and many more places. Conventional attendance system takes more time to record attendance and sometimes there is a chance of proxy attendance. Even in biometric attendance system also students need to give thumb impression, which consumes time. To overcome the problems of conventional and biometric attendance systems smart attendance systems came in to picture. This paper proposes a smart contactless attendance recording system for classroom students using face recognition technique. Initially students need to register with name, roll number and set of images and this information will be saved as database. Next an image of each student is captured and image processing is done to extract facial traits, and then compares those features to a database of people who have registered with the system. The attendance is automatically marked in excel sheet if a match is discovered. Face detection and recognition are done with the help of Haar cascade classifier, the Local Binary Pattern Histogram methods and K nearest neighbour algorithms. From simulations it is observed that K nearest neighbours algorithm is more accurate but requires more space and time compared to Haar cascade classifier.

In recent decades, the world of telecommunications has experienced significant development, thanks to the success of 2G mobile communications systems and the widespread deployment of the Internet. The transition to 4G enables the integration of multiple services such as multimedia, packet switching and broadband radio access. In this context, MIMO (Multiple-Input Multiple-Output) technology is recognized as a good solution in the development of the next generations of broadband wireless networks. This technique is very attractive for digital communications, allowing increased data rates and/or improved system performance. The aim of this work is to highlight different aspects that concern the simulation and modeling of the propagation channel, which are particularly critical in the design of MIMO systems.

Three-operand binary adder is the basic functional unit to perform the modular arithmetic in various cryptography and pseudorandom bit generator (PRBG) algorithms. Square root carry select adder used for three-operand addition that significantly reduces the critical path delay at the cost of additional hardware. Hence, a new high-speed and area-efficient adder architecture is proposed RCA logics to perform the three-operand binary addition that consumes substantially less area, low power and drastically reduces the adder delay. The proposed architecture is implemented on the FPGA device for functional validation and also synthesized with the commercially available 32nm CMOS technology library. Moreover, it has a lesser area and lower power dissipation Also, the proposed adder achieves less area than the existing three- operand adder techniques.

The proposed research work specifies the modified version of binary Vedic multiplier using Vedic sutras of ancient Vedic mathematics. It provides modification in preliminarily implemented Vedic multiplier. The modified binary Vedic multiplier is preferable has shown improvement in the terms of the time delay and also device utilization. The proposed technique was designed and implemented in Verilog HDL. For HDL simulation, Xilinx tool is used and for circuit synthesis, Xilinx is used. The simulation has been done for 4-bit, 8-bit, 16-bit, 32-bit multiplication operation. Only for 32-bit binary Vedic multiplier technique the simulation results are shown. This modified multiplication technique is extended for larger sizes.

This Project describes a one-sided Schmitt-triggered with data independent read and write operation from a 9T Static Random-Access Memory (SRAM) cell that consume less power and has excellent read and write stability. The existing approaches are performed with data leakage problem, huge area, expensive energy per access read data bits. To solve this problem, the proposed work will introduce three duplications of Static RAM cells with read ports to arise ST 12T SRAM, with the goal of greatly reducing data based read port leakage to improve the read quality and minimize area and power. The proposed methodology of Schmitt trigger based 12T SRAM memory cell achieves excellent read robustness in a one-sided Schmitt-triggered inverter with a three different single bit arrangement, while the write ability improves by power gating in Schmitt-trigger-inverter with support of control and trip voltage. This article’s proposed approach 12T SRAM memory cell will be designed at the single bit level, utilizing 16 nm CMOS Technology, and demonstrate the area, latency and power consumption using Mentor Tanner EDA Tool.

With the advancement of technology, the size of transistors and the distance between them are reducing rapidly. Therefore, the critical charge of sensitive nodes is reducing, making SRAM cells, used for aerospace applications, more vulnerable to soft-error. If a radiation particle strikes a sensitive node of the standard 6T SRAM cell, the stored data in the cell are flipped, causing a single-event upset (SEU). Therefore, in this project, a Soft-Error-Aware Read-Stability-Enhanced Low Power 12T (SARP12T) SRAM cell is proposed to mitigate SEUs. To analyze the relative performance of SARP12T, it is compared with other recently published soft-error-aware SRAM cells, QUCCE12T, 10T SRAM12T, RHD12T, RHPD12T and RSP14T. All the sensitive nodes of SARP12T can regain their data even if the node values are flipped due to a radiation strike. Furthermore, SARP12T can recover from the effect of single event multi-node upsets (SEMNUs) induced at its storage node pair. Along with these advantages, the proposed cell exhibits the highest read stability, as the ‘0’-storing storage node, which is directly accessed by the bit line during read operation, can recover from any upset. Furthermore, SARP12T consumes the least hold power. SARP12T also exhibits higher write ability and shorter write delay than most of the comparison cells. All these improvements in the proposed cell are obtained by exhibiting only a slightly longer read delay and consuming slightly higher read and write energy.

History and Objectives: The thyroid gland, which is one of the major endocrine organs in the human body, plays a crucial role in regulating daily metabolism. Death rates from thyroid disorders are decreased by early identification. Radiologists and pathologists typically diagnose thyroid illness, and this process strongly relies on their training and knowledge. This study reveals that deep learning-powered algorithms successfully identify thyroid problems automatically, supporting doctors' diagnostic choices and lowering the incidence of human false-positive diagnoses. The present study is a pioneering effort in the field, as it is the first of its kind to employ two preoperative medical imaging modalities for the purpose of multi-classifying thyroid disease categories. The mentioned elements encompass adenoma, a benign condition characterized by the presence of cystic structures and many nodules, as well as thyroiditis, a condition involving inflammation of the thyroid gland. Additionally, the term "normal" is included. This paper presents a diagnostic model for thyroid disease utilizing a cutting-edge deep convolutional neural network (CNN) architecture. The model aims to distinguish between different types of thyroid illnesses. The findings of the study are as follows: The model demonstrates high performance in both categories of medical images, achieving accuracy scores of 0.972 for computed tomography (CT) scans and 0.942 for ultrasound images. The experimental findings underscore the appropriateness of the chosen convolutional neural network (CNN) for both visual modalities, hence emphasizing the potential clinical uses of the deep learning model

The identification of eye disorders using fundus images poses a substantial challenge to the medical field. In fact, distinct phases of severity are shown by each pathology, and these stages may be deduced by confirming presence of lesions and defining their morphological characteristics. Moreover, several lesions with distinct diseases share traits with one another. Several techniques have been put out for using fundus images to identify eye conditions. Because deep learning (DL) based techniques may tailor the network to the desired detection outcome, they outperform other methods in terms of detection. This paper identifies ocular diseases using DL based techniques. Adaptive Wiener filter is first used to resize, crop, reflect, and eliminate noise from the image. Subsequently, SMOTE is used to address data imbalances and perform data augmentations. Lastly, a method for illness identification called Federated Deep Learning (FDL) is suggested. We used FDL to four pre-trained models, including convolutional neural network (CNN), VGG16, VGG19, and ResNetV2. DL models taught centrally are contrasted with those learnt within the federated framework.

In this Modern era Wireless network technology has rapid development and it is also important in this modern era. It has become popular and it used wide range of application. There has various problem, they are data transmission delay, power consumption, packet losses, communication link losses, Collision Storage and secure data transmission. The main objectives of this research work is to avoid packet losses, energy wastage and find out the collision when the process of data transmission from sender to receiver. However, clarified that the usage of packet transmission strategies. Not only that we survey that, what are the steps are to handle to reduce the power wastage, when the time of increasing the sensor nodes. In this survey, also clarified how the routing protocol and optimization algorithm works using with the clustering technique.

Lightning is a major disruptive phenomenon in the operation of all electrical installations. Lightning has always been a cause of disruption in the use of electricity. However, it is important to note the fairly recent and growing requirement for the quality of electrical systems (reliability, availability, continuity of service, etc.) as well as the constant concern to minimize the costs of using and producing electricity. This leads us to see that lightning becomes a hard point in improving all these factors. In this article, we present the ATP software that we will use in the simulation. After that, we discussed the description of lightning strikes and how to protect facilities from them. The next important step, which represents the novelty of this work, was to create a lightning strike model using the MATLAB/SIMULINK program, based on the mathematical equation of a lightning strike. After that, by using the model created, we simulated a lightning strike that attacked two different nodes of an electrical network, generators, and nine nodes, using the two simulation software's ATP/EMTP and MATLAB/SIMULINK to study the effect of the lightning strike on the voltages and currents of our network system. At the end of the work, we compared the results obtained by the two simulation software, ATP/EMTP and MATLAB/SIMULINK, and we discussed and explained the results obtained by the two software.

Since Hans Berger's 1920s introduction of EEG, its signals have been vital for diagnosing neurological issues and other applications. Brain-computer interfaces (BCIs) have transformed mind-controlled robotics, with Deep Learning enhancing EEG signal decoding. However, factors like substance abuse and Parkinson's disease pose classification challenges. To address these complexities, our research designed and validated a dual-model architecture. The aim was to generalize motor imagery classification for mind-controlled robots, encompassing both healthy individuals and those affected by substance addiction. This extended to assessing the real-world applications of these models in piloting advanced robotic platforms, including quad-rotors and wheeled mobile robots. Our empirical evaluation highlighted clear distinctions between the SVM and attention-based Bi-LSTM models; With the SVM, precision values fluctuated within a range of 0.702 to 0.782, while recall metrics varied between 0.705 and 0.790. On the other hand, the attention-based Bi-LSTM model demonstrated a broader precision range of 0.744 to 0.864 and recall values spanning from 0.752 to 0.856. Impressively, the average precision and recall for the Bi-LSTM model stood at 0.807 and 0.809 respectively, indicating its robust and consistent performance. Additionally, the presented confusion matrix further substantiates the heightened efficiency of the attention-based Bi-LSTM model in EEG signal classification. The evident proficiency of this model in classifying EEG signals ushers in an innovative and inclusive approach to mind-controlled tasks. This not only advances the BCI field but also signals promising avenues for therapeutic and rehabilitative measures tailored for those with neurological discrepancies.

Concrete is a porous material, and the pores are crucial for its strength and durability. In this way, low porosity is the best way to protect concrete against the most aggressive attacks. This study aims to investigate the effect of different additives on the concrete's capillary absorption and breakdown in harsh environments (hydrochloric acid (HCl) and sulfuric acid (H2SO4)) after 30, 90, and 180 days of being completely submerged. To find out how long sand concrete (SC) can stand up to chemical attack, three different mixtures were tested: 10SF5BFS (10% silica fume and 5% blast furnace slag, replaced by the weight of cement), 10SF (10% silica fume), 5BFS (5% blast furnace slag), and SC0 (no substitution as control). These mixtures were selected to investigate the effect of mineral additives SF and BFS on the chemical behavior of SC in a harsh environment. Moreover, the capillary absorption and mass variation of SC were studied. According to the durability results, replacing the cement with 10% SF reduced the capillary absorption by 40%. After 180 days of storage in HCl and H2SO4 acids, 10SF5BFS and 10SF types of concrete offer perfect resistance to chemical attack compared to other concretes.

Concrete is a porous material, and the pores are crucial for its strength and durability. In this way, low porosity is the best way to protect concrete against the most aggressive attacks. This study aims to investigate the effect of different additives on the concrete's capillary absorption and breakdown in harsh environments (hydrochloric acid (HCl) and sulfuric acid (H2SO4)) after 30, 90, and 180 days of being completely submerged. To find out how long sand concrete (SC) can stand up to chemical attack, three different mixtures were tested: 10SF5BFS (10% silica fume and 5% blast furnace slag, replaced by the weight of cement), 10SF (10% silica fume), 5BFS (5% blast furnace slag), and SC0 (no substitution as control). These mixtures were selected to investigate the effect of mineral additives SF and BFS on the chemical behavior of SC in a harsh environment. Moreover, the capillary absorption and mass variation of SC were studied. According to the durability results, replacing the cement with 10% SF reduced the capillary absorption by 40%. After 180 days of storage in HCl and H2SO4 acids, 10SF5BFS and 10SF types of concrete offer perfect resistance to chemical attack compared to other concretes.

This project discusses a comprehensive control of a wind turbine system linked to a pid-enabled industrial plant; an algorithm was developed to enable a control structure that employs a four-leg inverter linked to the grid side; this inverter injects the available energy and acts as an active power filter; this reduces load current disturbances and improves power quality. Nonlinear and linear loads in three-phase and single-phase configurations are addressed in a four-wire system. During the connection of the wind turbine, the utility side controller is designed to adjust the disturbances created in presence of reactive, non-linear and/or unbalanced single- and intra-phase loads, in addition to supplying active and reactive power as required. The controller's DC-link capacitor and grid-connected power converter are designed to enhance power quality when wind power is unavailable. The key difference of the suggested methodology with respect to others in the literature is that the proposed control structure is based on the Conservative Power Theory decompositions. This option gives power and current references for the inverter control that are decoupled, allowing for a wide range of customization, granular control, and robust performance. Software benchmarking in real time has been used to assess how well the suggested control algorithm would do in a production setting. The control strategy is developed in Opal-RT and proven in hardware-in-the-loop (HIL) using a TI DSP. MATLAB/SIMULINK is used to realize the control strategy and verify its accuracy. The findings validated our power quality enhancement control and enabled us to forego the use of passive filters, paving the way for a smaller, more versatile, and more reliable electronic implementation of a control system that is based on a smart grid.

In recent years, the integration of renewable energy sources and their unpredictable nature has posed significant challenges to power grid stability and voltage regulation. To address these issues, the Modular Multilevel Converter (MMC) based Static Synchronous Compensator (Statcom) has emerged as a promising solution for reactive power compensation and voltage improvement. However, one critical concern in MMC-Statcom operation is the voltage balancing of DC capacitors, which directly affects system performance and efficiency. In this research, a novel DC capacitor voltage balancing strategy is proposed for MMC-Statcom to ensure optimal operation and enhanced performance. The proposed strategy employs advanced control algorithms and innovative switching techniques to maintain balanced DC capacitor voltages under varying operating conditions. By achieving balanced capacitor voltages, the MMC-Statcom can effectively compensate reactive power and regulate the grid voltage with improved efficiency and stability. The effectiveness of the proposed DC capacitor voltage balancing strategy is extensively evaluated through simulation studies and experimental validations. Comparative analyses are performed with existing voltage balancing methods, demonstrating superior performance and robustness of the novel strategy. The results showcase its potential for practical implementation in real-world power systems. Overall, this study presents a significant advancement in MMC-Statcom technology, providing an effective solution for reactive power compensation and voltage improvement while ensuring reliable and stable grid operation. The proposed novel DC capacitor voltage balancing strategy holds the promise of contributing to the enhancement of power system stability and facilitating the integration of renewable energy sources in modern electrical grids.

Radar detection and vehicle-to-everything (V2X) communication are two advanced driver assistance system (ADAS) functions used to improve the safety of the road users and their driving experience. However, the proliferation of radars on vehicles and the functionalities expected for V2X because several issues like interference and insufficient communication flow. To address these challenges and optimize the use of the electromagnetic spectrum, the mutualisation of communication and radar detection functions in the same component using millimetre-wave (mm Wave) is proposed. As orthogonal frequency-division multiplexing (OFDM) waveform seems to be the most suitable waveform to enable this cooperation, we propose to investigate the performance of OFDM radar compared to that of current frequency-modulated continuous wave (FMCW) radar. We simulate and analyse the signal-to-noise ratio (SNR) and the probability of detection (PD) of OFDM radar based on the parameters of a current mid-range automotive FMCW corner radar. We observe that FMCW radar has better SNR and PD than OFDM radar under these conditions. However, by applying a coherent integration scheme on receive, the results obtained show that good performance of OFDM radar can be achieved. To check the effect of the parameters used on the communication performance, we provide graphs of the Bit Error Rate (BER). These graphs show that the BER do not suffer from these parameters but rather that they can be beneficial to the communication.

Data aggregation is a process of collecting data in an energy-efficient manner by which the life span of the network is enhanced. Aggregating data is a process of comprising the transmitted packet, in the sense the packet's only necessary information is sent to the remote sink node for further processing. While designing efficient data aggregation algorithms, a few extra requirements have to consider such as energy capabilities of sensor devices, energy resources, and computational capabilities. A novel Statistical Indexed X-means Clustering-based Divergence Butterfly Optimized Recursive Deep Learning Network (SIXC-DBORDLN) is introduced for aggregating the sensed data at the sink node with higher accuracy and minimal time. In SIXC-DBORDLN, Recursive Deep Learning Network comprises five layers, namely one input layer, three hidden layers, and one output layer for performing the data aggregation in WSN. In the SIXC-DBORDLN technique, a number of sensor nodes are considered as input in the input layer. Robin Hood indexive Hannan–Quinn informated X-means clustering algorithm is applied to generate ‘x’ number of clusters based on energy level for the first hidden layer. The residual energy value is calculated for every sensor node in the cluster. Then, assign the sensor nodes to the cluster whose residual energy value is closer to the centroid value. The cluster centroid value gets updated and the process gets repeated until all sensor nodes get reassigned. In hidden layer 2, the cluster head is selected using Jensen- Shannon divergencive butterfly optimization for every cluster based on the residual energy. The sensor node with higher fitness is taken as the cluster head. In hidden layer 3, the cluster head collects the data from all sensor nodes and sent to the sink with minimal delay. In the output layer, the cluster head transmits the data packets to the sink node. In this way, the energy-efficient data aggregation is carried out in WSN. Experimental evaluation is carried out using an agriculture dataset on the factors such as energy consumption, delay, packet delivery ratio, and data aggregation accuracy with respect to a number of sensor nodes.

A wireless sensor network (WSN) consists of spatially distributed independent devices to monitor physical or environmental conditions. Within the communication range of WSN, the sensor nodes transmit the sensed data to the base station. During the data collection, some amount of energy is dissipated. So, an energy-efficient data collection scheme is required for extending the network lifetime. A novel Energy-Aware Best First indexive Weighted Random Decision Forest Classification (EABFIWRDFC) technique is introduced for efficient data collection in WSN with higher accuracy and lesser time consumption. Initially in the ABFIWRDFC technique, a number of sensor nodes and threshold energy levels are initialized for performing the data collection. After that, the energy level of the sensor node is calculated. Camargo's indexive Instant Weighted Random Decision Forest algorithm is introduced to classify the higher energy nodes with majority votes for performing data collection. Random Decision Forest Classifier is an ensemble technique that uses the Camargo's indexive best first decision trees as weak learner to distinguish the higher energy and lesser energy sensor nodes. Then the lesser energy nodes transmit the sensed data packets to the nearest higher energy nodes by using Manhattan distance. After that, the sink node collects the data from the higher energy sensor node with higher accuracy and lesser time consumption. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate, throughput, and delay. The observed simulation results illustrate that the EABFIWRDFC technique efficiently improves the data delivery, throughput and minimizes the energy consumption, loss rate as well as delay than the conventional methods.

This thesis presents a novel cascaded multiport switching reluctance motor (SRM) drive designed for hybrid electric vehicles (HEVs) in this master thesis. The capacity of this technique to adeptly handle energy conversion between the generator/ac grid, battery reserves, and the motor is what makes it special. Aside from that, it provides a strong battery management (BM) system that successfully monitors SOC balance and orchestrates bus voltage regulation. A critical component of our design is the seamless integration of the battery packs and the AHB converter. This not only makes it easier to create cascaded BM modules, but it also prepares the stage for SRM drive-specific multilevel bus voltage and current capacity adjustments. This configuration improves the excitation and demagnetization phases of commutation, broadens the speed spectrum, reduces voltage stresses on switching components, and improves torque capability and overall efficiency. Here tailored the system to meet a variety of operational demands by including alternative driving patterns, regenerative braking systems, and charging techniques into the proposed converter. Our BM strategy's ability to manually link or unlink each battery pack from the power supply is an exciting feature. This one-of-a-kind feature considerably improves the system's fault-tolerance and easily avoids any overcharging or over draining problems during motor activity. Empirical experiments using a three-phase 12/8 SRM confirmed the feasibility and efficacy of our proposed cascaded multiport SRM drive.

The purpose of this work is to enhance the performance of solar panels by allowing them to better follow the sun's path. Currently, solar panels are being used to a far greater degree than ever before. Since this project's goal is to increase the usefulness of solar panels, we've opted to construct it. The four light- dependent resistors in the tracker actively follow the sun's path, and the panel's position is adjusted with the assistance of servo motors to ensure maximum power generation. Simulation and implementation of the most effective algorithm for the dual axis (vertical and horizontal) solar tracker are the main goals of this project. To maximise power production, this simulation rotates the panel across 180 degrees to maximise solar irradiation absorption. Compared to their stationary counterparts, trackers produce more energy because of their greater exposure to the sun's beams. In terms of land efficiency, solar trackers are superior than fixed-tilt systems since they produce more power while taking up about the same amount of area. An improved solar panel efficiency is the goal of this project.

The paper presents the results of experimental studies on wastewater treatment of light industry enterprises from surfactants. The influence of the active reaction of the medium and the processing time on the flotation device on the extraction of RIGHTS from wastewater has been studied. The optimal value of the active reaction of the medium and the duration of flotation have been established.

The typical pairing of a DC/DC boost converter and a DC/AC converter is being suggested to be replaced with a hybrid boost converter (HBC), which attempts to decrease switching phases and switching losses. Designing and testing the control system for a three-phase HBC installed in a PV charging station is the main goal of this study. The three-phase AC grid, the DC system supporting hybrid electric vehicles (HPEVs), and the PV system are all interfaced via the HBC. The control strategy is especially made to track the PV system's maximum power point (MPPT), regulate the voltage on the DC bus, and control the AC voltage or reactive power as needed. Detailed switching of power electronics during a test with the control architecture’s, for simulation, MATLAB/SimPowersystems is produced. The simulation results clearly demonstrate that the proposed control technique is practicable. To demonstrate the HBC's control performance, laboratory experiments are also run. The study makes use of a number of indicator terminology, including plug-in hybrid vehicle (PHEV), charging station, maximum power point tracking (MPPT), grid-connected photovoltaic (PV), three-phase hybrid boost converter, and vector control.

An MPPT-based hybrid solar-wind energy conversion system, as well as wind-based renewable energy production, is discussed in this paper. With the help of solar panels and wind turbines, the system will be able to produce an abundance of energy. In order to electrify rural areas, power industries, and do other tasks, this project aims to build a system that employs wind and solar electricity. Energy from the wind and the sun are examples of non-renewable resources. In addition to analyze the performance of the renewable energy system, the maximum power point tracking (MPPT) controller is used to trace the maximum outlet in both wind and solar energy conversion systems. Due to their extensive availability and relative ease of use, solar and wind power production have quickly become the most popular renewable energy sources. No amount of photovoltaic or wind power will be sufficient to meet all of our needs. This leads to the optimization of electrical phenomena and wind systems in order to meet load demand. A standalone hybrid renewable energy system using photovoltaic and wind energy conversion using a perturb and observe technique is suggested in this paper. In order to meet a variety of load scenarios, this system's application combines the benefits of all generating technologies and provides watts.

In this research, we show how a Distributed Incremental Adaptive Filter (DIAF) may be used to efficiently manage a utility-interfaced Photovoltaic (PV) - battery microgrid for high power quality. The grid needs to be turned on in case of a blackout. However, the PV-battery system is intended to keep serving critical loads even if the distribution network goes down. Unpredictable power delivery from PV arrays due to unexpected weather changes can be mitigated with the use of a bidirectional controlled converter and a battery. The Voltage Source Converter (VSC) receives the most available power and splits it between the main power source and the nonlinear loads. The VSC is controlled by the DIAF and is powered by the PV array and the battery system. When the system is coupled to nonlinear loads, the DIAF-driven control not only provides regulated power distribution, but also reduces harmonics, effectively balances loads, and boosts power factor. A PV Power Feed- Forward (PVFF) component is included to enhance the current regulation. This not only enhances the dynamic operations of a residential PV-battery microgrid, but also allows for the injection of active electricity into the main grid. In addition to enhancing fuel efficiency, the Battery Energy Storage (BES) helps maintain microgrid stability. This study proves that a PV-battery microgrid constructed in a lab can function as intended.

This article presents the implementation of a three-stage Doherty power amplifier to enhance the efficiency at power back-off. In this, a three-stage power amplifier with the final stage as a Doherty amplifier optimized for efficiency peaks at 3 and 6 dB back-off. The amplifier is realized on the WIN Semiconductors 150nm Gallium Arsenide (GaAs) p-HEMT process for 5G application operating at 24.25-27.5GHz. The design achieves an output power of more than 3 watts (34.8 dBm) and power-added efficiency of 37.5 and 34.8% at 3dB and 6dB back-off respectively. With this design, a gain of 24.8dB is obtained when operating at 24.25-27.5GHz.

The phrase "social media" refers to a range of web-based platforms that enable content creation, debate, and sharing by users. The younger generation in modern India is greatly influenced by social media's ubiquitous presence in their everyday lives. Due to its widespread use, social media has become so embedded in the lives of young people that it is hard to imagine developing personally without it. This increased reliance has caused a worrisome rise in adolescent addiction. Numerous studies have examined the consequences of excessive use of social media by Indian teenagers, making it a significant topic of concern. This research explores the dual effects—positive and negative—that young people's use of social media has on their lifestyle. Benefits include easier access to health-related information, more educational possibilities, and improved communication. On the other hand, the report highlights drawbacks including cyberbullying, engaging in illegal activity, mental health conditions like anxiety and sadness, and even possible linkages to violent extremism. Most importantly, the type of results depends on how people use social media—whether in a positive or negative way. Furthermore, the study highlights the widespread problem of young people being addicted to social media and stresses how important it is in determining society dynamics.

A higher education institution's fee management system automates the collecting of student fees. The system manages tuition fees, project fees, and other payments for student services because it is an online platform. It can control budgets, payroll, other outlays, fees and offers total transparency and insight on fee-related transactions. All documentation is available online via the fee management system, making the procedure paperless. Alerts about upcoming fee payments can be sent through SMS or email to students and parents. The system has taken the place of all these time-consuming procedures. Parents may simply manage admissions for their kids with the aid of a system that includes integrated fee management system. Mobile phone fee management systems are very adaptable and it has no negative environmental effects. The system must be able to supply information to outside processes that need it for reporting. Such data paths ought to be present in an ERP system. The management of individual and general user accounts is made simple by the use of fee management system. Most importantly, the system makes sure that fees are paid on time, saving the time, money, effort, and resources on tracking fee payments.

The abstract presents an innovative design for a Cyclic Redundancy Check (CRC) with memory, featuring 8-bit and 16-bit depth options with an 8-bit width. This design operates within a 64-bit memory model and leverages Finite State Machine (FSM) control. The proposed architecture achieves enhanced performance metrics compared to existing methods, including reduced power consumption at 0.14W, minimized delay, and a more compact area footprint. The implementation is tailored to meet the demands of modern data integrity verification, offering a balance between computational efficiency, memory utilization, and power optimization. The detailed exploration of the CRC check with memory design underscores its potential for advancements in applications requiring robust error detection capabilities with improved energy efficiency and resource utilization.

This study introduces a novel approach to heart stroke prediction, leveraging the synergy of Internet of Things (IoT) and advanced cloud-based machine learning algorithms. Utilizing a comprehensive dataset from 600 individuals, the research focuses on real-time monitoring of critical vitals such as body temperature, pulse rate, blood pressure, and oxygen saturation. Innovative machine learning models including Random Forest, Artificial Neural Networks, and Gradient Boosting are employed to analyze the data, offering a predictive mechanism with enhanced accuracy and reliability. The paper underscores the transformative potential of continuous monitoring in predictive healthcare, aiming to revolutionize early heart stroke detection and prevention strategies. This approach exemplifies a significant stride in integrating technology with healthcare, paving the way for future advancements in proactive medical interventions. In this research, we present a groundbreaking method for predicting heart strokes by integrating IoT-based continuous vital sign monitoring with sophisticated cloud-based machine learning. Our approach, tested on a dataset from 600 individuals, utilizes real-time data on body temperature, pulse, blood pressure, and oxygen saturation. We applied advanced machine learning techniques, such as Random Forest and Gradient Boosting, achieving significant strides in prediction accuracy. This study not only enhances heart stroke prediction but also sets a new benchmark in the integration of technology and healthcare, opening avenues for proactive medical interventions. In this research, we introduce an innovative heart stroke prediction method that synergizes IoT-based continuous monitoring and advanced cloud-based machine learning, utilizing a custom-developed device with state-of-the-art sensors. Data from 600 individuals, featuring real-time body temperature, pulse, blood pressure, and oxygen saturation, were analyzed using sophisticated machine learning models like Random Forest and Gradient Boosting. This approach not only advances heart stroke prediction accuracy but also marks a significant leap in integrating cutting-edge technology with healthcare, heralding a new era of proactive medical interventions. This research presents a novel heart stroke prediction model using a custom-built IoT device with advanced sensors. The data, collected from 600 individuals, was analyzed using various machine learning techniques. Linear Regression showed the highest performance with an AUC of 0.992, CA of 0.96, and F1 score of 0.96, followed closely by Gradient Boosting with similarly high metrics. Random Forest, Neural Networks, and Tree models also demonstrated substantial efficacy. These results highlight the potential of integrating modern sensor technology and diverse analytical methods in predictive healthcare, setting a new standard in early heart stroke detection.

Good financial planning is essential to staying focused and on course when the company grows, new obstacles arise, and unforeseen circumstances arise. This shows the direct need for handling multiple goals and a multi-criteria decision model, which can be addressed by developing a goal-programming approach. To develop the goal programming algorithm and to compute financial planning which is based on the annual fiscal statement of a biogas generation facility that disposes of wet waste. Since the primary purpose of this research is to examine the budgeting structure of Wet Garbage Disposal and Biogas Generation Facilities. Goal programming is used across the whole budget to optimize its advantages. Following this investigation, we pinpointed the following precise goals: maximize all assets, minimize capital employed, maximize profit after tax, maximize equity, maximize gross sales, maximize EBIDTA, minimize liability, and minimize NET cash given by (used in). Goals are prioritized, and weights can be determined with the help of the AHP method in the objective function. Another part of the analysis is figuring out if all of the objectives have been achieved resulting from the study. As a result, this research will help industrial institutions meet their financial goals and implement proper measures to stay on track. Also, a goal programming algorithm is developed that can be implemented in any organization which helps to compute and assess financial performance.

The global community is presently undergoing a pandemic, specifically the COVID-19 pandemic, which has emerged as a consequence of the identification of a novel coronavirus disease in Wuhan, China in 2019. Consequently, a considerable number of over 200 countries and dependent territories have witnessed an approximate total of 768 million infections, leading to a significant loss of 69.5 million lives. As a result, the global economy, daily life, and individuals' health will experience adverse consequences. Hence, the timely detection of COVID-19 is imperative to mitigate its transmission among individuals and reduce the associated fatality rate. The efficiency and timeliness of COVID-19 case detection can be enhanced through the utilization of computer-aided diagnosis (CAD) techniques in medical imaging, particularly in the context of chest X-rays. This is due to the advantageous characteristic of low radiation exposure offered by CAD, as compared to computed tomography (CT) methods. The present study involves the development of an automated diagnostic methodology utilizing a convolutional neural network (CNN) to forecast the presence of COVID-19 based on chest X-ray images. Our contribution is comprised of three key elements: Initially, the X-ray images undergo data pre-processing techniques, such as image resizing, to adequately prepare them for further analysis. Subsequently, a CNN model is utilized to generate predictions using the aforementioned pre-processed images, striking a balance between the intricacy of the model and computational efficiency. Ultimately, the performance of the model is assessed by employing established evaluation metrics and juxtaposing them against state-of-the-art methodologies.

User post comments on social media in the form of reviews or comments to share their opinions on any sectors. These comments are referred as sentiments/opinions and it has a huge impact in all business fields. Basically, the sentiments are of three types namely positive, negative and neutral or it may be fine grained as ratings. These sentiments should be analyzed to take decisions for further improvement in business. But, this type of data produce massive stream hence machine learning techniques are employed in finding the hidden interesting pattern behind this comments. User reviews are tagged with sentiments or scores using Natural Language Processing (NLP) and then it is classified by data mining supervised machine learning techniques. This research work focuses on hotel industry in which users post comments and views in online mode regarding the hotels they stayed. This work uses machine learning approach TF-IDF with bi-gram for word level sentiment analysis and classifies the sentiments using Naive bayes classifier combined with ensemble method. This classification model will aid in better decision support system. The research work uses Rapidminer tool for visualization, implementation, and evaluation.

The era of 1990 to 2010 saw a tremendous increase in cases of corporate frauds in India raising questions as to the efficacy of corporate governance norms in India and certainly Banking institutions has been no exception to this. The banking institutions have seen a tremendous growth and challenges since 1990 due to liberalization and privatization. Even though it is a well regulated institution with RBI being the regulator, the sector still suffers from various challenges such as ethical practices, corporate governance etc. This research article primarily examines certain aspects of corporate fraud in relation to banking institutions. First, what factors have contributed to the increasing frauds in the banking institutions. Second, what the economic effects related to these frauds. Lastly, concerns over the effectiveness of corporate governance in India in the light of available legislations and guidelines to the effect. The analysis is being carried out in the right of recent ICICI-Videocon scam which has again brought to light the loopholes in the governance of big corporate entities of which the advantage is taken by the higher ups such as CEOs of the company.

Data warehousing and data mining are crucial aspects of modern businesses. Data mining is the process of identifying patterns in data and using these patterns to derive useful information. A data warehouse is a database applications system designed to report and analyze data. Data warehousing is a vital building block in the decision-making process. The secondary aim is to analyze the major success and failure factors in data warehousing projects, as well as to study the potential challenges for the vendors, consultants, implementers, and the academic researchers to delve into it so that future strategies can be developed for the successful implementation of data warehousing in organizations. It is the recommendation of the group that changes in philosophy and policy must match changes in the environment and changes in system design and procedures. Other recommendations include the creation of a single approval plan for electronic and print resources, and establishment of a system for notification of electronic serial purchases.

The Unified Payments Interface (UPI) in India is a major platform that facilitates online money transfer between two parties using a smartphone. This innovative method enhances the accessibility and efficiency of payment systems, and holds significant potential for emerging markets and developing nations. As UPI continues to develop, more and more Indian citizens are embracing this transformative payment system, propelling India into the digital era with impressive momentum and progress. Additionally, research will demonstrate the lasting social and sociological impacts of the UPI system in India.

In this article, a solar photovoltaic (PV) array, a battery energy storage (BES), a diesel generator (DG) set, and a grid-based electric vehicle (EV) charging station (CS) is utilized to provide the incessant charging in islanded, grid-connected, and DG set connected modes. The CS is primarily designed to use the solar PV array and a BES to charge the EV battery. However, in case of exhausted storage battery and unavailable solar PV array generation, the CS intelligently takes power from the grid or DG set. However, the power from DG set is drawn in a manner that it always operates at 80%–85% loading to achieve maximum fuel efficiency under all loading conditions. Moreover, in coordination with the storage battery, the CS regulates the generator voltage and frequency without a mechanical speed governor. It also ensures that the power drawn from the grid or the DG set is at unity power factor even at nonlinear loading. Moreover, the point of common coupling voltage is synchronized to the grid/generator voltage to obtain the ceaseless charging. The CS also performs the vehicle to grid active/reactive power transfer, vehicle to home, and vehicle-to-vehicle power transfer for increasing the operational efficiency of the CS. The operation of the CS is experimentally validated using the prototype developed in the laboratory.

Integrating RESs into the distribution system threatens the reliability and security of the existing power grid. The reliability and consistency of the electricity provided by the system are significantly impacted by the unpredictability of renewable energy sources and fluctuations in demand. Microgrid power and energy storage devices are quite space intensive. Using grid connectivity and energy storage technologies like hybrid batteries and supercapacitors, this initiative aims to provide a solid foundation for solar energy management. The integration of a supercapacitor and a battery storage system allows for the rapid handling of both short-term and long-term power fluctuations, resulting in a more stable system and an easier time navigating fluctuations in PV output. How the grid and the battery are typically divided up in terms of average power is revealed by the battery's state of charge (SOC). Also provided is a method for controlling energy that is both practical and beneficial. Another benefit of using a supercapacitor is that it reduces stress on the battery system in the event of a sudden disparity between produced and required power. Simulations validate the efficacy and viability of the proposed energy management strategy.

This study demonstrates a technique for converting wind power into electrical current using a three-phase, multipurpose inverter linked to the electrical grid. The system under study is connected to the electricity grid at the shared connection point. An inverter for three-phase voltage sources, a wind turbine equipped with a permanent magnet synchronous generator, and a rectifier make it up. Using direct power control ensures it can handle multiple tasks. Uses for direct current power conversion (DPC) include increasing dynamic power, decreasing harmonics in line current, and increasing wind energy. The simulation results demonstrate the effectiveness of the proposed strategy. The system's control algorithm eliminates harmonic currents, compensates for reactive power, and transfers active power from the PMSG wind rotor to the load or the grid, according to the outcome. This allowed us to verify that our proposed solution was solid. One way to harness wind power is with a wind energy conversion system, or WECS. You might think of it as a DPC system with a permanent magnet synchronous generator (PMSG), a rectifier, a converter, and a few more components.

Numerical impulse response Wireless sensor networks (WSNs) and signal processing both frequently use FIR filters. The multipliers used in the design of FIR filter topologies are notoriously space-hogs. When putting together complicated algorithms, FIR filter design relies heavily on performance aspects like hardware cost and area. Three different kinds of adders—the Kogge stone adder, the sklansky adder, and the square root carry select adder—were chosen for this task. This paper's objectives include being familiar with the XILINX synthesizer, creating an RTL for the constructions, and confirming the structures' capacity to perform the desired functions. Different types of FIR trees are compared to one another in terms of pace. In this paper, the VERILOG programming language to create a FIR filter in XILINX ISE is being used. VERILOG is used to write the code for the FIR filter in this paper, and waveform simulation is used so that the results may be analyzed.

Numerical impulse response Wireless sensor networks (WSNs) and signal processing both frequently use FIR filters. The multipliers used in the design of FIR filter topologies are notoriously space-hogs. When putting together complicated algorithms, FIR filter design relies heavily on performance aspects like hardware cost and area. Three different kinds of adders—the Kogge stone adder, the sklansky adder, and the square root carry select adder—were chosen for this task. This paper's objectives include being familiar with the XILINX synthesizer, creating an RTL for the constructions, and confirming the structures' capacity to perform the desired functions. Different types of FIR trees are compared to one another in terms of pace. In this paper, the VERILOG programming language to create a FIR filter in XILINX ISE is being used. VERILOG is used to write the code for the FIR filter in this paper, and waveform simulation is used so that the results may be analyzed.