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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151-160 订阅

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Quantum Task Allocation and Scheduling with Security Enhancements via Quantum Key Distribution 5

Quantum Task Allocation and Scheduling with Security Enhance...

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

作者： Singh, Animesh Mohan, Ankita Nayak, Satyakam Sharma, Neeraj Chakraborty, Sanjukta Bandyopadhyay, Anjan Kiit University School of Computer Engineering Bhubaneswar751024 India Lovely Professional University School of Computer Engineering Jalandhar144411 India Techno International New Town Kolkata700156 India

ISBN: (纸本)9798331522100

As quantum computing progresses, the need for optimized task allocation and scheduling becomes paramount to efficiently harness quantum resources. Simultaneously, ensuring the security of communications in quantum systems is critical, given their vulnerability to quantum-specific attacks. This paper introduces a framework for quantum task allocation and scheduling, augmented with security enhancements using Quantum Key Distribution (QKD). By incorporating novel quantum scheduling algorithms and cryptographic protections, this mechanism offers both high efficiency and strong security. Analytical formulations and simulations confirm the robustness of our approach, demonstrating improvements in both scheduling efficiency and secure communication. © 2025 IEEE.

关键词： Qubits

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Mitigating Security Challenges in Edge Computing: Attacks, Defence Strategies, and algorithms 3

Mitigating Security Challenges in Edge Computing: Attacks, D...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Matta, Venkata Durga Rao Maheswara Rao, V.V.R. Battula, Venkata Sai Jayasurya Addagatla, Mahesh Babu Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331527549

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the Internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies. © 2025 IEEE.

关键词： Cloud security

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Sustainable AI systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, D. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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An extensive critique of various modern optimization techniques for electric vehicles and their applications

An extensive critique of various modern optimization techniq...

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

作者： Sarathkumar, D. Arun Shunmugam, D. Maheswari, P. Sajin, J.B. Jayakumar, T. Manivel, Murugesan Bashkaran, K. Kongu Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Excel Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Sree Buddha College of Engineering Department of Mechanical Engineering Kerala Pattoor690 529 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Kongunadu College of Engineering and Technology Department of Biomedical Engineering Tamil Nadu Thottiam621215 India

ISBN: (纸本)9798331529833

The rapid advancement of electric vehicles (EVs) has prompted significant research into optimizing their performance, efficiency, and integration into smart grid systems. This paper provides an extensive critique of modern optimization techniques applied to EVs, covering areas such as battery management, energy consumption, routing, and charging infrastructure. We analyze both traditional and emerging optimization methods, including heuristic algorithms, machine learning approaches, and hybrid techniques. The paper evaluates these methods in terms of computational efficiency, scalability, adaptability, and real-world applicability. Furthermore, we discuss the challenges and future directions for optimizing EVs in the context of evolving technologies and infrastructures. By synthesizing the latest research findings, this critique aims to offer a comprehensive understanding of the strengths and limitations of various optimization strategies, providing valuable insights for researchers, engineers, and policymakers involved in the development and deployment of electric vehicles. © 2025 IEEE.

关键词： Heuristic algorithms

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Research on road object detection algorithms based on YOLOv8-RC 4

Research on road object detection algorithms based on YOLOv8...

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2024 4th international conference on Intelligent Traffic systems and Smart City, ITSSC 2024

作者： Li, Wenqiang Sheng, Hu Geng, Na Yang, Xili Xu, Chuanxin School of Computer and Communication Engineering Dalian Jiaotong University Liaoning Province Dalian116028 China

ISBN: (纸本)9781510686304

In response to issues of low detection accuracy and poor robustness caused by missed detections of small objects and occlusions among dense objects in road scenes, a YOLOv8-RC object detection algorithm is proposed. First, by introducing receptive field attention convolution, the network can flexibly adjust the level of attention to targets of different scales, thereby enhancing the feature extraction capability of the backbone network and improving detection accuracy. Secondly, a lightweight CARAFA is used to replace the upsampling operator in the neck network, which reduces the number of parameters while improving detection accuracy. Finally, by adding a small object detection layer, local feature extraction is performed on larger-sized feature maps, thereby improving the accuracy of small object detection. Experimental results show that on a dataset excerpted from BDD100K, the average precision of the YOLOv8-RC algorithm model in this study increased by 5.9% compared to the model before improvement, with the model's parameter count reduced by 0.05M. © 2025 SPIE.

关键词： Object detection Roads Target detection Detection and tracking algorithms Performance modeling Feature extraction Data modeling Small targets Convolution Education and training

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IoT-Assisted AI Framework for Preserving Biodiversity: Monitoring Rare Trees in Dynamic Forest Ecosystems

IoT-Assisted AI Framework for Preserving Biodiversity: Monit...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vidyasree, G. Jothi, J. Nirmal Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India

ISBN: (纸本)9798331509828

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoLiA-Net (Forest Lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoLiA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoLiA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts © 2025 IEEE.

关键词： Biodiversity Forest Ecosystems Machine Learning Rare Trees Simulation Sustainability

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Indian Language Recognition Using SVM and KNN 1

Indian Language Recognition Using SVM and KNN

引用

1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Watve, Shreyasi Patil, Mangal Thuse, Sanjyot College of Engineering Department of Electronics and Communication Engineering Pune India PES'S Modern College of Engineering Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350355611

The demand for accurate language recognition systems has surged due to the growing need for multilingual applications. This paper presents a comparative analysis of classifiers for Indian language recognition using machine learning techniques. The system is designed to recognize a broad range of languages., including less commonly spoken ones. A comprehensive multilingual audio dataset is collected., and key features such as prosodic characteristics and Mel-Frequency Cepstral Coefficients (MFCCs) are extracted. Machine learning algorithms., including K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)., are trained on these features. Cross-validation ensures model reliability., while performance is evaluated using metrics like accuracy., Fl-score., recall., and precision. Results indicate that the proposed approach effectively identifies spoken languages with high accuracy. Additionally., feature extraction methods are explored to enhance system performance., supporting the development of multilingual applications for more personalized user experiences. © 2025 IEEE.

关键词： Support vector machines

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Navigating the Landscape: A Comprehensive Survey on Healthcare Recommendation systems 4th

Navigating the Landscape: A Comprehensive Survey on Healt...

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4th international conference on Smart IoT systems: Innovations and Computing, SSIC 2023

作者： Gupta, Megha Singhal, Shilpa Tomar, Abhinav Computer Science and Engineering Netaji Subhas University of Technology Dwarka Delhi New Delhi110078 India Computer Engineering Marwadi University Rajkot-Morbi Road Gujarat Rajkot360003 India

ISBN: (纸本)9789819751990

A recommender system (RS) is become an essential part of present day specially in healthcare systems (HS) as professionals performed maintenance of health through treatment of diseases, diagnosis, and other mental and physical destruction. To provide solutions to the health-related issues includes massive amount of data of patients so healthcare recommender system has become a growing platform. Doctors believe that these systems provide recommendations based on clinical experiences as patients past records are also being used. For RS in health sector includes many types of filtering like content-based, collaborating, knowledge-based, hybrid, etc. Machine learning (ML) algorithms and deep learning (DL) algorithms are used to do the same. The purpose of this paper is to review the usage of RS to solve many problems of patients and provide best solutions to them based on recommendations given by different professionals. The article discussed recent market trends, limitations and possibilities related to HRS, and new strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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YOLO-DenseFuse: A Visible-Infrared Feature Fusion Framework for Intelligent Camouflage Target Detection in Complex Environments 24

YOLO-DenseFuse: A Visible-Infrared Feature Fusion Framework ...

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2nd international conference on Artificial Intelligence, systems and Network Security, AISNS 2024

作者： Gong, Jincheng Xu, Le Sun, Dianxing Peng, Ruihui Qingdao Innovation and Development Base Harbin Engineering University Shandong Qingdao China

ISBN: (纸本)9798400711237

Camouflage target detection is one of the key challenges in the field of deception detection, especially with the continuous advancement of camouflage technologies. The high fusion of target and environmental background makes conventional single-modal object detection algorithms perform significantly worse in complex environments. To address this issue, this paper proposes a novel camouflage target intelligent detection algorithm based on visible-infrared feature-level fusion, called YOLO-DenseFuse. First, a YOLO-Dense dual-stream feature extraction channel for visible and infrared images is designed to efficiently extract and reuse information from both infrared and visible light bands. Then, to efficiently utilize the positional information in infrared images, residual connections are employed to extract target location information. Finally, a camouflage target multi-spectral dataset is constructed for performance validation of multi-modal image fusion methods. Experimental results show that the proposed method achieves a detection accuracy of 91.91% and a recall rate of 93.33%, outperforming the YOLOv7 algorithm. Compared with other mainstream object detection algorithms, the proposed method demonstrates significant advantages in detection accuracy and other evaluation metrics. This approach has significant application prospects and practical significance for camouflage target detection in complex field battle environments, contributing to enhanced battlefield situational awareness. © 2024 Copyright held by the owner/author(s).

关键词： Infrared imaging

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Advanced Machine Learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

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