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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Optimizing Federated Semantic Learning in Distributed AIGC-Enabled Human Digital Twins: A Multi-Criteria and Multi-Shard User Selection Framework

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IEEE Transactions on Mobile computing 2025年第7期24卷 5916-5933页

作者： Okegbile, Samuel D. Gao, Haoran Talabi, Oluwasegun Cai, Jun Niyato, Dusit Shen, Xuemin Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada University of the Fraser Valley School of Computing AbbotsfordBCV2S 7M8 Canada Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (VTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized VTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT. © 2002-2012 IEEE.

关键词： Federated learning

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Comparative Analysis of GPU Stream Processing between Persistent and Non-persistent Kernels 13

Comparative Analysis of GPU Stream Processing between Persis...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Suhwan Jung, Changue Kim, Younghoon College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Employing GPU to accelerate stream data processing has shown to be a considerable success in recent research. The stream processing engines need to process continuous data. Persistent Thread (PT), where GPU threads remain in a loop throughout executions, rather than non-Persistent Thread (nonPT) kernels can give several advantages. PT provides high-performance low-latency data processing by reducing kernel launch overhead and hiding memory copy overhead to the GPU. In this paper, we comparatively analyze the performances of PT and nonPT in various aspects. We also figure out the cause of advantages and what needs to be considered in order to obtain actual performance gain. The evaluation was done with four distinct application scenarios and the result shows that PT yields at most x4.4 higher performance over nonPT under desirable conditions. © 2022 IEEE.

关键词： Graphics processing unit

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End To End Comments Filtering Feature Using Sentimental Analysis 3

End To End Comments Filtering Feature Using Sentimental Anal...

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3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Sindhu, A. Jayakumar, D. Sasivardhini, S. Ramkumar, M.O. Rajmohan, R. IFET College of Engineering Computer Science and Engineering Tamil Nadu Villupuram India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Villupuram India

ISBN: (纸本)9798350391565

In the age of digital communication, social media platforms have become essential tools for interaction and information sharing worldwide. However, these platforms often face the challenge of managing negative and harsh comments, which threaten to disrupt a healthy online environment. This paper presents an innovative end-to-end comments filtering feature that uses sentiment analysis to detect and mitigate such undesirable comments. The proposed system employs advanced Multilingual Sentiment Analysis techniques using Long Short-Term Memory (LSTM) networks and the Naive Bayes Classifier to analyze comments in Tamil, Tanglish (a blend of Tamil and English), and *** negative comments are identified, the system filters and deletes them, sends warning emails to offending users, and blocks repeat offenders. This approach not only curtails harmful content but also discourages users from posting negative comments in the future. Experimental results demonstrate the system's effectiveness in significantly reducing negative comments and maintaining a positive online community. The combination of LSTM networks and Naive Bayes Classifier provides a robust framework for sentiment analysis and automated moderation, paving the way for healthier social media environments. © 2024 IEEE.

关键词： Electronic mail filters

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A Novel CNN-Based IoT System Architecture for Real-Time Detection and Prevention of Animal Intrusion in Farmland 4

A Novel CNN-Based IoT System Architecture for Real-Time Dete...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ashokkumar, C. Kumar, M. Arun Krishnan, R. Santhana Priya, S. Mohanap Narayanan, K. Lakshmi Julie, E. Golden School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai India PSNA College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad Polytechnic College Department of Computer Science and Engineering Cheranmahadevi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350300888

Animal intrusion is a significant challenge for farmers, causing extensive crop damage, human injuries, and substantial financial losses. Traditional animal movement monitoring and surveillance methods alone are insufficient to provide a permanent solution. To solve this problem, a novel system architecture that combines the latest convolutional neural network (CNN) algorithm and Internet of Things (IoT) technology is proposed. Our proposed system architecture integrates various components, including a Raspberry Pi as a central processing unit, cloud storage for efficient data management, and a GSM module for instant alert generation. To train the CNN algorithm, a comprehensive and diverse Animal Dataset consisting of various animal species commonly found in farmland areas is curated. The dataset encompasses a wide range of annotated images, enabling the CNN algorithm to accurately identify and classify animals. The Raspberry Pi serves as the core of the system, responsible for real-time image processing and analysis. Utilizing the power of the CNN algorithm, the Raspberry Pi processes the captured images from strategically placed surveillance cameras. When an animal intrusion is detected, the system promptly generates an alert via the integrated GSM module, providing immediate notifications to farmers and relevant authorities. Furthermore, the system leverages cloud storage to store and manage the collected data, facilitating easy access and retrieval for analysis and system improvement. This cloud-based approach enables scalability, allowing the system to handle large amounts of data efficiently. By integrating the CNN algorithm, IoT, Raspberry Pi, cloud storage, and GSM module, a comprehensive and robust framework for real-time detection and prevention of animal intrusion is provided. The system's ability to swiftly identify and alert farmers and authorities about potential threats minimizes crop damage, ensures human safety, and significantly reduces financial l

关键词： Signal detection

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Optimization of Traffic and Time Control with Sensor-Driven Transmission Control System using MANET and Machine Learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. This study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. These sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. This model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. The exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 IEEE.

关键词： Sensor networks

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Empowering the adoption of blockchain-based cryptocurrency as a payment method: A user-friendly educational approach

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Alrehaili, Ahmed White, Martin Beloff, Natalia University of Sussex School of Engineering and Informatics Informatics Brighton United Kingdom Islamic University of Madinah College of Computing and Information Systems Computer Science Department Medina Saudi Arabia

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： blockchain platforms blockchains cryptocurrencies models and analysis

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

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8th International Conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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Understanding Cybercrime: A Study of Malware Analysis, Phishing Attacks, and Sentiment Trends 3

Understanding Cybercrime: A Study of Malware Analysis, Phish...

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3rd International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kumaran, U. Sreeraag, G. Narendran, Advik Meena, V.P. Gupta, Sushil Kumar Appaji, Abhishek Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India National Institute of Technology Department of Electrical Engineering Jharkhand Jamshedpur831014 India Bms College of Engineering Department of Medical Electronics Engineering Bangalore India

ISBN: (纸本)9798331515911

With the rapid evolution of computer systems and technologies, individuals face an increasing threat of cybercrime. This research explores the changing landscape of cybercrime by focusing on three key areas: malware analysis, phishing attack analysis, and sentiment analysis. The study aims to identify malware strategies for evading detection and analyze the prevalence of spyware by examining diverse malware samples. Additionally, trend analysis highlights the rising complexity and frequency of phishing attacks, with a notable shift toward targeting reputable platforms. Sentiment analysis of media headlines surrounding data breaches uncovers a paradox: while concerns about cybersecurity remain, there is a growing confidence in the ability to manage cybersecurity crises. These findings provide insights into emerging threats and evolving public perceptions, offering a comprehensive understanding of the current cyber- crime landscape. © 2025 IEEE.

关键词： Phishing

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Instant Ambulance Connection Rescue Radar 5

Instant Ambulance Connection Rescue Radar

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Baskar, M. Kumar, Ankit Bajaj, Khushi Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chengalpattu603203 India Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chengalpattu603203 India

ISBN: (纸本)9798400709418

In the field of emergency medical services, the element of time significantly influences patient outcomes. The groundbreaking Instant Ambulance Connection- Rescue Radar system is introduced to expedite responses to medical emergencies. This research paper explores the system's structure, approach, and potential impact, highlighting its role in providing swift and efficient emergency medical services. The primary objective of this system is to bridge the gap between patients and ambulance services, simplifying ambulance requests and hospital visit scheduling. A user-friendly Android app is central to its functionality, enabling one-tap ambulance requests and harnessing modern technologies like the Internet of Things and the Google Maps API to transmit user information and precise locations to local ambulance drivers rapidly. This system also empowers patients with real-time access to ambulance availability and the ability to track the ambulance's location in real-time. By enhancing communication between patients, ambulance drivers, and healthcare institutions, the system significantly improves the efficiency of the emergency medical response network. © 2023 ACM.

关键词： Location

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