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

共 89784 条记录，以下是371-380 订阅

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OTFS-Based Multicast Communications with reconfigurable Intelligent Surfaces

OTFS-Based Multicast Communications with Reconfigurable Inte...

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IEEE conference on Wireless Communications and Networking

作者： Mohamad H. Dinan Arman Farhang Department of Electronic and Electrical Engineering Trinity College Dublin Dublin Ireland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we explore the integration of two groundbreaking technologies, reconfigurable intelligent surfaces (RISs) and orthogonal time frequency space (OTFS) modulation, with a focus on their use in multicast communications to improve high-speed wireless connectivity. We introduce a phase shift design algorithm for RIS-assisted OTFS systems to maximize the channel gain in the presence of both delay and Doppler spread. Furthermore, we propose a method that utilizes the majorization-minimization (MM) algorithm to jointly optimize beamforming and adjust the phase shifts of RIS elements in a multicast scenario. We validate the effectiveness of the proposed algorithms by numerical simulations. The results demonstrate significant performance improvements over existing benchmark approaches.

关键词： Time-frequency analysis Multicast algorithms Array signal processing reconfigurable intelligent surfaces Multicast communication Benchmark testing Time-varying channels Numerical simulation Multiuser channels Optimization

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A Novel Channel Selection and Classification in Motor Imagery for Brain Computer Interface Using Meta Heuristic algorithms

A Novel Channel Selection and Classification in Motor Imager...

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international conference on Smart systems and Wireless Communication, SSWC 2024

作者： Devarakonda, Nagaraju Kamarajugadda, Raviteja Anandarao, Sarvani School of Computer Science and Engineering VIT-AP University Amaravathi India Department of Information Technology Lakireddy Bali Reddy College of Engineering Mylavaram India Department of Computer Science and Engineering SRM University AP Amaravati India

ISBN: (纸本)9789819613472

A vital input for a task using the brain in BCI (Brain Computer Interface) applications is the motor imagery (MI) signal from the brain. Users of BCI systems can operate external equipment by using their brain activity, using motor imagery as a control method. Innumerous Electroencephalography (EEG) channels are used to gather nerve impulses from the brain, which are the most prevalent input for Brain Computer Interface systems while they are minimally invasive, flexible, and low in price. The computational overhead is increased by multichannel BCI systems’ high-dimensional data, which causes processing to be slower and to cost more money. EEG details are regularly gathered from over 100 different brain regions;therefore, it is essential to use channel selection algorithms to choose the ideal channels for a given circumstance. However, the primary objective of choosing the channel in EEG data analysis is to lessen the computer intricacy, improve the precision of classification by eliminating over fitting, and save setup time. In this study, we suggested a remora optimization technique that was inspired by nature to lessen the computational load brought on by several channels. Using predetermined criteria, a number of channel selection evaluation techniques, whether classification-based methods used or not it extracted the proper channel subsets. In order to determine the greatest classification accuracy, the classification procedures were utilized in the end. Three publicly available EEG datasets are used to validate the experiment (BCI Competition IV-1,2a, Competition III-3a), and it resulted in superior classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Random forests

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A Review on Web Data Mining to Detect Online Spread of Terrorism Activity 2

A Review on Web Data Mining to Detect Online Spread of Terro...

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

作者： Jha, Saurav Kumar Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine learning Maharashtra Wardha India

ISBN: (纸本)9798331505745

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector Machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement. © 2025 IEEE.

关键词： Crop protection

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AES Efficient Encryption of Multimedia Data using Threads for 5G System 2

AES Efficient Encryption of Multimedia Data using Threads fo...

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

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Kumar, Nathi Kranthi Venkateswaramma, Jarabhala Nirosha, Chelumala Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India St. Martin's Engineering College Dept. of Artificial Intelligence and Machine Learning Hyderabad India Hyderabad India Secunderabad India St. Martin's Engineering College Department of EEE Telangana Secunderabad500 100 India

ISBN: (纸本)9798331505745

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method. © 2025 IEEE.

关键词： Sensitive data

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Design and Development of IoT Smart Driving Protocol for Unmanned Ground Vehicles 6

Design and Development of IoT Smart Driving Protocol for Unm...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Seelam, Santhosh Kumar Nagakishore Bhavanam, S. Midasala, Vasujadevi Reddy, Edara Sreenivasa Department of Computer Science and Engineering University College of Engineering and Technology Acharya Nagarjuna University Andhra Pradesh Guntur India Jabalpur Madhya Pradesh Barela India Department of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331522667

UGVs have enormous potential in industries such as defense, agriculture, and autonomous transportation. Efficiency and effectiveness can be achieved by UGVs through the sophistication of their control systems, mainly with regard to navigation and decision-making. In this study, a new IoT-based smart direction control system is proposed for UGVs. The system presented herein integrates state-of-the-art sensor fusion techniques against the backdrop of a robust and adaptive communication framework for enhanced navigation accuracy and reduced latency, with the overall goal of improving system efficiency. Equipped with GPS, IMU, and LiDAR sensors, our system provides accurate environmental perception and real-time adaptability to various terrains. The heart of this device is a high-performance, military-grade microcontroller, which ensures the reliability and handling of numerous data streams simultaneously with minimal latency. On the software side, modular design makes use of edge computing and machine learning algorithms to facilitate adaptive control and real-time decision-making. The performance was verified by rigorous testing in simulated environments and real-world field trials. The results show an improvement in the navigation accuracy and system responsiveness compared to previous solutions. A comparative deep analysis shows the superiority of our approach to hardware design, software integration, and prototype testing. © 2025 IEEE.

关键词： Adaptive control systems

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Predicting Thyroid Disease using Machine Learning: A Comparative Study of algorithms 3

Predicting Thyroid Disease using Machine Learning: A Compara...

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

作者： Katore, Khushi Balvir, Parul Daronde, Subodh Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331509675

Predicting thyroid illness has become a significant problem in recent times. Thyroid illness is getting more frequent in women over thirty, but it is also becoming more prevalent in men, women, and children. Long-term effects include difficulties with the heart, eyes, fertility, and pregnancy. Therefore, it remains essential to assess the thyroid data in instruction to detect the disease at an initial stage and take preventative measures against the lethal thyroid cancer condition. Two prevalent thyroid illnesses that affect the thyroid's ability to produce thyroid hormones and control the body's metabolism are hyperthyroidism and hypothyroidism. Data cleaning procedures were utilised to make the data simple sufficient for analytics to demonstration the likelihood of affected role having thyroid illness. Thirteen clinicopathologic characteristics from the Kaggle data set are employed in this paper's analysis and classification models for thyroid illness with the goal of predicting the recurrence of well-differentiated thyroid cancer. An important factor in the process of illness prediction is machine learning. It is important to guarantee a strong foundation of information that container be integrated and employed as a hybrid model for multifaceted knowledge tasks, such medical diagnostic and prognosis tasks. Additionally, we suggested many machine learning methods and diagnostics for thyroid prevention in this research. Machine Learning algorithms, support vector machine (SVM), K-NN, Decision Trees, Logistic Regression , Random Forest , XGBoost were used to forecast the projected risk on a patient's chance of obtaining thyroid disease. © 2025 IEEE.

关键词： Decision trees

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Soft Computing Paradigms for Load Balancing in Cloud Computing 3rd

Soft Computing Paradigms for Load Balancing in Cloud Computi...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Ghafir, Shabina Alam, M. Afshar Alankar, Bhavya Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9789819719457

This paper provides a comprehensive comparison of load-balancing algorithms employed in cloud computing, spanning from the earliest to the most recent developments. Emphasizing the significance of load balancing for system performance, resource optimization, and equitable allocation among users, the paper offers insights into various techniques, their effectiveness across workloads, and associated limitations. With an overview of each algorithm, this study caters to practitioners and researchers in the dynamic field of cloud computing, aiming to contribute valuable perspectives for enhanced system efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Decision systems in Cybersecurity: A Machine Learning Perspective 12th

Decision Systems in Cybersecurity: A Machine Learning Perspe...

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12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Dhablia, Anishkumar Ganesh, R. Senthil Rizvi, Nuzhat Arora, Himanshu Borade, Monali Ravindra Bhujbal, Pallavi Altimetrik India Pvt. Ltd Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Artificial Intelligence and Data Science VIIT Pune India Department of Information Technology School of Computing MIT ADT University Loni Kalbhor Pune India

ISBN: (纸本)9789819601462

The evolving digital environment requires sophisticated decision systems to protect important information assets from the growing complexity and variety of cyber threats. This review paper examines the impact of machine learning (ML) on cybersecurity decision systems, with a specific focus on analyzing ML and deep learning (DL) methods. The study employs the UNSW-NB15 dataset, which is a widely used benchmark dataset in the field of network security. The paper starts with an overview of the present cybersecurity environment, emphasizing the difficulties presented by advanced and changing cyber threats. It highlights the importance of adaptive and intelligent decision systems that can efficiently identify and reduce cyberattacks as they occur. A significant part of the paper focuses on a thorough analysis of different machine learning and deep learning techniques used in cybersecurity. The review discusses conventional machine learning algorithms like decision trees, support vector machines, and ensemble methods, along with sophisticated deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each approach is thoroughly assessed for its strengths and limitations based on factors like accuracy, interpretability, and scalability. The performance of various ML and DL models is evaluated using the UNSW-NB15 dataset as a benchmark. The dataset covers various cyberattack scenarios, enabling a thorough assessment of algorithms’ ability to detect anomalies and classify malicious activities. The paper explores the incorporation of machine learning (ML) and deep learning (DL) into decision support systems, highlighting the significance of explainability and interpretability in the realm of cybersecurity. The conversation covers the difficulties of implementing machine learning (ML) and deep learning (DL) models in practical situations, such as concerns about data privacy, adversarial attacks, and model resilience. This rev

关键词： Adversarial machine learning

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Intelligent Battery Management System for Electric Vehicle with IoT-based Monitoring, AI-Driven Multiple Fault Diagnosis and Energy Optimization 3

Intelligent Battery Management System for Electric Vehicle w...

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

作者： Arun Kumar, P. Shalini, S. Sneha, R. Vasunthra, P. Suganyadevi, S. M Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Battery related issues have a significant effect on both the performance and safety of electric vehicle (EV). Overheating Battery Lifespan is meant to be reduced by simply the dangers of overheating, as they can even cause thermal runaway, as well as from over and deep revolution. Besides, EV batteries have poor performance even in extreme temperatures, capacity loss, and internal defects. In order to cope with these problems, a proposed IoT based EV battery monitoring and fault detection system provides the real time analysis to promptly notify the battery health. Built in IoT sensors are integrated in this system which monitors critical parameters constantly i.e. temperature, voltage, current and charge levels. For 95-98% battery parameters monitoring accuracy, the collected data is transferred to a centralized cloud platform for real time cloud processing. The system utilizes advanced edge computing techniques and continuous monitoring capable of detecting a fault with 95-98% probability. Electrochemical processes are analyzed with 90-95% accuracy using predictive analytics and machine learning algorithms to ensure optimal battery performance. Moreover, the system achieves energy efficiency of more than 95% through power management and control mechanisms. In 95% of the cases, intelligent charging algorithms will prevent overcharging. This cutting-edge technology dramatically improves EV battery safety, longevity as well as efficiency, assuring reliability and sustainability. © 2025 IEEE.

关键词： Predictive analytics

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reconfigurable Smart Grid Management for Electric Vehicle Charging and the Integration of Renewable Energy Sources using Pied Kingfisher Optimizer 2

Reconfigurable Smart Grid Management for Electric Vehicle Ch...

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

作者： Sivaramkrishnan, M. Ramkumar, M. Siva Uday, Kaparthi Kanan, Mohammad Raj, C. Mohan Giri, Jayant Karpagam College of Engnieering Dept of EEE India SNS College of Technology Dept of ECE India CVR College of Engineering Ibrahimpatanam EIE Department Hyderabad India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Department or EEE Coimbatore641202 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331505745

Electric Vehicle (EV) charging management under the Smart Grid (SG) framework with incorporated Renewable Energy Sources (RESs) functions to advance Energy Management (EM). The operational cost of EV charging varies because renewable energy availability can change so this affects the financial performance of charging operations. Integration of RESs with the SG becomes challenging for operational system efficiency particularly when renewable energy generation levels are low. To overcome these drawbacks, this manuscript proposes an efficient approach for SG management for EV charging with the integration of RESs. The suggested method is Pied Kingfisher Optimizer (PKO). The main aim of the suggested technique is to minimize the operational expenses of EV charging as well as maximize the efficiency of the SG system. The PKO is used to optimize the scheduling and allocation of energy resources within the SG system. By then, the suggested approach is implemented in MATLAB and contrasted with several other methods that are currently in use. The suggested technique outperforms all existing approaches such as Bald Eagle Search (BES), Red Kite Optimization (RKO), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), and Coati Optimization Algorithm-Constitutive Artificial Neural Network (COA-CANN). The suggested approach delivers superior performance than conventional methods by reducing operational costs to 638,322$ under coordinated charging and 629,745$ under smart charging without impacting system performance metrics. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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