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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

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Comparative Analysis of Power Distribution in Conventional Grids and Deep Learning-Enhanced Smart Grid 3

Comparative Analysis of Power Distribution in Conventional G...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Blessy, J. Janish Vinoth Raj, R. Nareshkumar, M. Daniel Kamalam, T. Infanta, J.Binu Allen Velammal Institute of Technology Chennai India Saveetha Engineering College Chennai India Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331509675

This study explores the potential of deep learning methodologies to improve the efficiency and performance of power grids compared to conventional systems. The methodologies employed by machine learning algorithms in processing data sourced from smart meters, weather sensors, and energy markets are examined. The main focus of this study is on applications of energy sectors like demand forecasting, optimisation of generation and distribution, and real-time energy consumption management. The advantages of AI powered grids encompass refined demand forecasting, better integration of renewable energy sources, and optimised management of storage systems. This study illustrates the ability of intelligent grids to effectively manage supply and demand, leading to decreased costs and a minimised environmental footprint. The integration of learning-based smart grids in the context of demand response, emphasising the adaptation of energy supply to align with consumer behaviour is observed. Recurrent Neural Network algorithm is proposed to enhance the framework through the integration of Bagging and Boosting techniques in AI driven distribution systems. The results demonstrate that neural networks possess considerable capability to improve grid stability, decrease operational expenses, and alleviate the effects of climate change through more efficient energy systems. This study explores the comparative potentials of artificial intelligence in optimising smart grid energy systems alongside traditional grid frameworks. The findings indicate that the smart grid, enhanced by deep learning technologies, demonstrates significantly greater efficiency compared to conventional grids. © 2025 IEEE.

关键词： Demand response

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Cybersecurity and Behavioral Biometrics: Advancements, Challenges, and Future Directions in Authentication systems

Cybersecurity and Behavioral Biometrics: Advancements, Chall...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kumar, N. Venkatesh Bonagiri, Krishna Thilakavathi, B. Banumathi, S. Indumathi, G.S. Pandikumar, K. VSB Engineering College Department of Chemistry Tamil Nadu Karur India Quadrant Technologies Ave NE RedmondWA98052 United States Rajalakshmi Engineering College Department of ECE Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science Tamil Nadu Chennai India St Joseph's College of Engineering Department of MBA Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of CSE Tamil Nadu Chennai India

ISBN: (纸本)9798331512965

In today's digital landscape, the security of personal and organizational data faces increasing threats from sophisticated cyberattacks. Traditional authentication methods, such as passwords and tokens, are proving insufficient in defending against these evolving threats to improve authentication systems in response, the use of behavioral biometrics has become a viable strategy. Behavioral biometrics leverages unique patterns in human behavior, such as typing rhythm and touchscreen gestures, to continuously verify user identities with high accuracy and minimal user friction. This paper explores the advancements in behavioral biometrics, including the application of machine learning algorithms to analyze and interpret behavioral data. It also examines the challenges, such as privacy concerns and algorithmic reliability, that hinder widespread adoption. Furthermore, the paper discusses future directions for authentication systems, envisioning the integration of behavioral biometrics with technologies like artificial intelligence and blockchain to strengthen security and privacy protections. In order to shed illumination for the potential and challenges of using behavioral biometrics to improve cybersecurity measures for people and organizations in the digital age, this paper will analyze these topics in-depth. © 2025 IEEE.

关键词： Cybersecurity

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The impact of feature selection models on the accuracy of tree-based classification algorithms: Heart disease case 6

The impact of feature selection models on the accuracy of tr...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Efe, Yigit Demir, Leyla Izmir BakIrçay University Department of Smart Systems Engineering Izmir35660 Turkey Izmir BakIrçay University Department of Industrial Engineering Izmir35660 Turkey

Heart disease is a very serious illness that can result with death if it not detected in time. Early diagnosis and effective treatment of heart disease can prevent instead of the progression and improve the quality of life. In recent years, machine learning techniques are widely used to accurately predict the heart disease accurately. One of these techniques is tree-based classification algorithms that construct a tree-like structure while making predictions. Feature selection models are used to improve the performance of machine learning techniques and their ability to generalize by identifying the most informative features. However, there is limited research on how different feature selection models specifically impact the performance of tree-based classification algorithms in the context of heart disease prediction. In this study, the impact of feature selection models on the classification performance of tree-based algorithms to determine the risk of heart disease is investigated. For this purpose, five different feature selection models are applied to the dataset taken from UCI Machine Learning Repository, and the classification performances of eleven different tree-based algorithms are analysed. Classification results show that the Hoeffding Tree technique achieved the highest accuracy (0.84) on the dataset where the stability selection model is applied. © 2025 The Authors.

关键词： Adversarial machine learning

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Optimizing Scalable Data-Driven Recommendations with Evolutionary algorithms for Predictive Analytics 3

Optimizing Scalable Data-Driven Recommendations with Evoluti...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Javed, Muhammad Rajalakshmi, A. Anitha, R. Gupta, Deepak Sharif, Salah Farhan A. Raj, I. Infant College of Computer Science Donghua University Department of Computer Science and Technology Shanghai China Coimbatore India Dr.SNS Rajalakshmi College of Arts and Science Coimbatore India Department of Mechanical Engineering Graphic Era Hill University Dehradun India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun India Al Kitab University Department of Medical Engineering Kirkuk Iraq K.Ramakrishnan College of Engineering Department of Cse Trichy India

ISBN: (纸本)9798331508456

Evolutionary algorithms are optimization techniques inspired by the natural selection processes, which refine candidate solutions iteratively by selection, crossover, and mutation. This kind of algorithm is particular to solving complex problems with large search spaces, and they are often used in domains like data-driven e-commerce product recommendation systems. Within this paper, an innovative evolutionary algorithm is proposed to optimize scalable, data-driven recommendations within e-commerce systems. The most important aspect is the efficient management of large datasets, which is the hallmark of contemporary e-commerce platforms. The algorithm begins with an initialization of a diverse population of candidate solutions, which refers to different product recommendations based on a user's preferences. A sound fitness function is utilized to ascertain the goodness of candidate recommendations through the evaluation criteria of engagement and accuracy founded upon historical interaction data. The proposed evolutionary algorithm performs significantly better as it achieved accuracy with a precision of 98.9% at 97%, recall at 99%, and with an F1-Score of 98.3 %. The evolutionary search is a sequence of iterated cycles applying selection, crossover, and mutation operations to progressively refine the candidate solutions during such a process. Selection has the choices with the higher fitness values as preferred candidates. © 2025 IEEE.

关键词： Marketplaces

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Integrating Federated Learning and Blockchain for Enhanced Financial Portfolio Management 3

Integrating Federated Learning and Blockchain for Enhanced F...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Murugan, V.G. Nanda, Sudhansu Sekhar Sudindra, V.R. Jatty, Gajendra Naidu Kumar, N.Rajesh Chamundeeswari, Vijaya Department of Management Studies Andhra Pradesh Madanapalle India Sri Sri University Odisha Cuttack India Icfai Business School Department of Finance Bangalore India University of Botswana Gaborone Botswana St.Michael College of Engineering and Technology Sivagangai India Saveetha Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331508456

Financial portfolio management receives secure and efficient processing through the combination of Federated Learning (FL) with blockchain technology. FL protects data privacy through its distributed training capabilities that prevent financial data exposure. Blockchain secures operations and maintains transparency through the creation of unalterable model change logs on its ledger systems. A proposed approach reaches 91.3% model accuracy level and a 1.27 Sharpe Ratio and delivers a 14.3% return on investment which surpasses conventional approaches. The training duration reaches 4.8 hours while security enhancements and increased efficiency become available in the system. The designed framework supports 200 transactions per second at 250 ms response time while maintaining complete security with 0.10 USD transaction fees. It demonstrates the ability of FL and blockchain technology to boost security measures and risk-adjusted performance metrics and computational capability. Future Work will concentrate on blockchain performance optimization while testing practical applications of algorithms and revising algorithms to optimize financial applications. © 2025 IEEE.

关键词： Investments

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Scheduling Tasks in the Cloud environment by using evolutionary optimization technique 5

Scheduling Tasks in the Cloud environment by using evolution...

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5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Mahapatra, Liza Rout, Minakhi Tripathy, Sarita Deemed to be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

Among the many software technologies around the world,there is no parallel to cloud computing(CC).Businesses use cloud computing for hosting *** cloud computing, tasks are distributed across a group of resources which when executed, help reduce time needed to finish them *** optimization refers to the process of minimizing the time needed to accomplish tasks. These group of tasks are present in a domain related to computers. In CC, the goal of this optimization is reducing the total time needed to execute the group of tasks when distributing and controlling virtual machines or resources. This paper aims to obtain optimization in scheduling tasks in CC by utilizing genetic *** methods whose basis are natural selection and genetics are called Genetic algorithms. They combine the idea of "survival of the fittest"in string-based representations with a mix of structured and random information exchange, forming a search approach that includes a level of human-like creativity..After the application of genetic algorithms to task scheduling problem, the minimum fitness per generation is seen in the graph after two generation of stability. © 2025 IEEE.

关键词： Genetic algorithms

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28th international conference on engineering of Complex Computer systems, ICECCS 2024

28th International Conference on Engineering of Complex Comp...

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28th international conference on engineering of Complex Computer systems, ICECCS 2024

ISBN: (纸本)9783031664557

The proceedings contain 22 papers. The special focus in this conference is on engineering of Complex Computer systems. The topics include: Automated Parameter Determination for Enhancing the Product Configuration System of Renault: An Experience Report;optimal Solution Guided Branching Strategy for Neural Network Branch and Bound Verification;AccMILP: An Approach for Accelerating Neural Network Verification Based on Neuron Importance;Word2Vec-BERT-bmu:Classification of RISC-V Architecture Software Package Build Failures;Test Architecture Generation by Leveraging BERT and Control and Data Flows;less is More: An Empirical Study of Undersampling Techniques for Technical Debt Prediction;modeling and Verification of Solidity Smart Contracts with the B Method;template-Based Smart Contract Verification: A Case Study on Maritime Transportation Domain;QuanSafe: A DTBN-Based Framework of Quantitative Safety Analysis for AADL Models;a Event-B-Based Approach for Schedulability Analysis For Real-Time Scheduling algorithms through Deadlock Detection;Validation of RailML Using ProB;reachability Analysis of Concurrent Self-modifying Code;an Iterative Formal Model-Driven Approach to Railway systems Validation;an Efficient Distributed Dispatching Vehicles Protocol for Intersection Traffic Control;confidentiality Management in Complex systems Design;analyzing Excessive Permission Requests in Google Workspace Add-Ons;formal Verification Techniques for Post-quantum Cryptography: A Systematic Review;autoWeb: Automatically Inferring Web Framework Semantics via Configuration Mutation;safePtrX: Research on Mitigation of Heap-Based Memory Safety Violations for Intel x86-64;towards Efficiently Parallelizing Patch-Space Exploration in Automated Program Repair.

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Next-Generation Embedded systems with Synergies VLSI with Deep Learning: Design Methodologies, Optimization Techniques, and Real-World Applications

Next-Generation Embedded Systems with Synergies VLSI with De...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kalarani, G. Mohanapriya, M. Pattunnarajam, P. Sasikala, G. Sivakumar, J. Suresh, M. Jaya Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Ece Tamil Nadu Sriperumbudur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India

ISBN: (纸本)9798331529833

New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodologies for design, optimisation strategies, and practical uses of next-generation embedded systems are the foci of this study, which investigates the ways in which VLSI and deep learning might work together. These systems have the potential to transform several industries, such as transportation, medicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the improvements in semiconductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithms. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient mapping of deep learning models onto these devices. We show case studies that show how these methods work and talk about the trade-offs between performance, power consumption, and adaptability. The development of next-generation embedded systems relies heavily on optimisation approaches. Model compression, quantisation, and pruning are some of the optimisation strategies that we examine;they lessen the memory and computational demands of deep learning models without drastically altering their accuracy. For embedded devices with limited resources, these methods are crucial for implementing deep learning models. Additionally, we explore the practical uses of embedded systems augmented with VLSI and deep learning. By capitalising on the complementary strengths of VLSI and deep learning, applications like autonomous driving, medical imaging, and smart home automation are revolutionising entire industries. In this paper, we examine the design

关键词： Integrated circuit design

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AI in the Detection of Breast Cancer and Related Ethical and Legal Issues 5th

AI in the Detection of Breast Cancer and Related Ethical and...

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5th international Ethical Hacking conference, eHaCON 2024

作者： De Mukhopadhyay, Keya Guha, Tapas Basu, Abhisikta Department of Biotechnology Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science Engineering Techno International New Town India University of Engineering and Management Kolkata India

ISBN: (纸本)9789819784561

Breast Cancer accounts for a significant percentage of malignancy among women globally. It is a disorder where abnormal breast cells develop uncontrollably and form tumours when left untreated, the tumour could spread throughout the body and become fatal. So, early detection is critical for survival. The clinical outcomes of breast cancer can be significantly improved with an accurate and timely diagnosis. Significant advancements in breast cancer diagnosis and customized treatments have been made possible, by the emergence of Artificial Intelligence particularly in the field of image analysis, the K-Nearest Neighbors algorithm is one of the simplest and easily interpretable supervised ML algorithms, which can determine whether the cancer lumps are malignant or benign. KNN, SVM, and GRNN are machine learning algorithms that make use of an openly accessible dataset, features taken from images, data pre-processing techniques, and Principal Component Analysis (PCA) in order to improve accuracy and decrease diagnostic time. Artificial Intelligence can identify both missed cancers and breast tissue features, which in turn, can help to predict future cancer development. Some Artificial Intelligence algorithms excel at predicting patients with a high risk of interval cancer, which is aggressive. When no cancer was clinically detected by screening mammography, even Artificial Intelligence algorithms trained for short time horizons can forecast the chance of developing cancer for up to five years in the future. The WHO reports that low- and middle-income nations account for over 80% of fatalities from breast and cervical cancer. In the developing economic scenario of India, one in 28 women is likely to develop breast cancer. The Artificial Intelligence detection model is a cost-effective precautionary measure for the early detection of breast cancer in women. The data protection laws should be convenient enough to carry out the test with the informed consent of the patients

关键词： Diseases

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Performance Evaluation of Machine Learning Approaches for Wearable Sensor-Driven Human Activity Recognition for Elderly People 2

Performance Evaluation of Machine Learning Approaches for We...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Shafi, Shaik Jeeharshitha, K. Sony, G. Reddy, Varshitha Kumar, K Charan Kiran Kumar, G. Electronics and Communication Engineering B V Raju Institute of Technology Medak Narsapur India ECE BVRIT Medak Narsapur India ECE VNR VJIET Hyderabad India

ISBN: (纸本)9798331505745

In the recent past, due to the availability of data from Wearable Sensors and Smart Phones at lower cost with energy efficiency, recognizing and tracking uncharacteristic behavior particularly in elderly people has become focal point in research. In addition, there exists more than 962 million aged people globally and 1 in 6 people will be aged 60 years or over by 2030. Thus, in this paper, we aim to analyze and assist elderly people in performing daily activities using wearable sensor data, considering the decline in both physical activity and mental abilities due to aging factor. To achieve this, monitoring elderly people's daily activities in indoor environments via wearable sensors data is essential. Machine Learning algorithms like Support Vector Machine (SVM), Decision Tree, and Random Forest are utilized for activity recognition using the data collected from wearable sensors. These algorithms are chosen to handle timestamped data and for their ability to deliver high accuracy, and optimized memory usage, making them well-suited for this application. Furthermore, performance of three machine learning algorithms has been evaluated using accuracy, precision, F1 score, recall and support parameters. Among all the techniques tested, the SVM accomplished superior accuracy, achieving 96.34 %, followed by Random Forest at 92.67%, and Decision Tree with 85.57%. © 2025 IEEE.

关键词： Random forests

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