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2nd Inter.a.ional Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Dhar.a.eja, M. rama, Prabha K.P. Asha, N. Nithya, P. Lalitha, S. Manojkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350382952

Wireless Sensor.a.tuator Networks (WSANs) are sensitive to cyber thr.a.s due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect thr.a.s in r.a.-time. The proposed system achieved remar.a.le perfor.a.ce, with scores of 98% data integrity, 96% error repair, and 95% thr.a. detection accuracy. Encryption rates r.a.h their max at 200 Mbps, with optimal resource usage (30% CPU, 25% rAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, sur.a.sing existing methods. The paper overcomes the limitations of static encryption methods and proposes a pr.a.tive protection mechanism against developing cyber thr.a.s, making a substantial contribution to WSAN security improvement. © 2024 IEEE.

关键词： Cryptography

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Perfor.a.ce Evaluation of IoT-Fog-Cloud System for Data Storage, Analysis and Visualisations Using retr.a. Queues Appr.a.h 8th

Performance Evaluation of IoT-Fog-Cloud System for Data St...

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8th Inter.a.ional Conference on Infor.a.ion System Design and Intelligent Applications, ISDIA 2024

作者： Qurashi, Shahazad N. Goswami, Veeena Mund, G.B. Barik, rabindra K. Department of Health Informatics College of Public Health and Tropical Medicine Jazan University Jazan Saudi Arabia School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9789819748945

The significance of the Internet of Things (IoT) is growing in the contemporary infor.a.ion age due to its quick acquisition of diverse data. Both the IoT and cloud computing systems generate vast quantities of data. Fog computing, a technique that employs leveraging, is utilised to process substantial volumes of data. Subsequently, the processed data are transmitted to a cloud storage system using fog nodes. The installation of an IoT-Fog-Cloud system leads to enhancements in both latency and throughput. The primary responsibility of the fog nodes inside the IoT-Fog-Cloud system is to perform localised collection, monitoring, and storage of IoT workload and data. This function serves to enhance efficiency and availability within the system. The discussion of compliance with Quality of Service (QoS) standards constitutes the pertinent subjects and challenges that necessitate attention in fog computing. The present article employs an M/M/1 retr.a. queueing model and analytical methodology to assess and examine the perfor.a.ce of the IoT-Fog-Cloud system, thereby offering a solution to the problem at hand. The mathematical methods employed in this study enable the determination of the service arrival required to meet the QoS criter.a.in IoT layer. It also provides mathematical expressions for various perfor.a.ce metrics of the system, including the arrival rate, service rate, retr.a. rate, and the average waiting time for requests in the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality of service

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Data sampling appr.a.h using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction

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Jour.a. of Intelligent and Fuzzy Systems 2023年第3期44卷 3867-3876页

作者： Amanullah, M. Thanga ramya, S. Sudha, M. Gladis Pushparathi, V.P. Haldorai, Anandakumar Pant, Bhaskar Department of Information Technology Aalim Muhammad Salegh College of Engineering Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India Department of Electronics and Communication Srinivasa Ramanujan Centre Sastra Deemed to Be University Kumbakonam India Department of Computer Science and Engineering Velammal Institute of Technology Pancheeti Chennai India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Graphic Era Deemed to Be University Bell Road Clement Town Uttarakhand Dehradun India

On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software ar.a. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved random Synthetic Minority Over.a.pling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling appr.a.h, a strategy for software defect prediction is given in this paper. To address the class imbalance, the defect datasets were initially processed using the Improved random-SMOTE Over.a.pling technique. Then, using the Linear Pearson Correlation appr.a.h, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPr, and accuracy rate for two separate datasets, the perfor.a.ce of the proposed strategy is contrasted with the appr.a.hes to classification that presently exist. © 2023 - IOS Press. All rights reserved.

关键词： Defects

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Alzheimer's Disease Detection and Classification: A Survey 1

Alzheimer's Disease Detection and Classification: A Survey

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1st Inter.a.ional Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Saini, rajat Shar.a. K.P. DR. B.R. Ambedkar National Institute of Technology Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350365016

Alzheimer's disease stands as a progressive irreversible neurodegenerative issue affecting the mind, marked by cognitive decline and memory impairment. It is the most prevalent form of dementia globally, contributing significantly to dementia cases worldwide. The condition occurs through the collection of unusual protein aggregates like beta-amyloid plaques and tau tangles in the brain, disrupting cell level function and communication in brain neurons. This leads to a gradual decline in cognitive abilities, encompassing memory, language, problem-solving, and judgment. In the ar.a.of medical diagnostics, various machine-learning models are deployed to detect different stages of Alzheimer's disease. These methods include Transformers, random forest, recurrent Networks, and Convolutional Networks etc. Their purpose is to recognize and categorize the disease's progression, aiding in early diagnosis and intervention. Evaluating the perfor.a.ce of these models involves utilizing diverse datasets such as the Alzheimer's Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. The reported accuracy ranges from 77 % to 99 %, showcasing the efficacy of machine and deep learning models in medical applications. These results highlight how impor.a.t machine learning is to the medical field. By leveraging advanced computational techniques, researchers can enhance disease detection, prognosis, and tr.a.ment planning, ultimately improving patient outcomes. © 2024 IEEE.

关键词： Patient tr.a.ment

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Enhancing Signal Coverage and Perfor.a.ce for router Placement Using Deep Learning Model 4

Enhancing Signal Coverage and Performance for Router Placeme...

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4th Inter.a.ional Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Avinash Sangal, A.L. rani, rajneesh Dr. B.R.Ambedkar National Institute of Technology Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350364729

Predicting nodes localization position is Still challenging due to different abstacles and dynamic nature of nodes (Customer devices), This research focuses on optimizing router placement in Wi-Fi 11ax networks to enhance signal coverage and perfor.a.ce in indoor settings. Traditional appr.a.hes to router placement often rely on heuristic methods that may not fully exploit the dynamic nature of Wi-Fi environments. To address this challenge, we Discussed and Compare a novel appr.a.h that integrates deep learning with traditional optimization techniques. Specifically, we develop an LSTM Based improved recurrent Neural Network (rNN) model tailored for spatial analysis of received Signal Strength Indicator (rSSI) values obtained from multiple reference nodes. Unlike conventional rNN architectures, our model incorporates advanced techniques such as Long Short-Term Memory (LSTM) layers for capturing long-term dependencies and spatial correlations in Wi-Fi signal propagation. The improved rNN analyzes sequential rSSI data to understand spatial dynamics and predict optimal router placements. By leveraging LSTM layers, the model can effectively learn and adapt to changing network conditions and environmental factors in r.a.-time. This adaptive capability enables the rNN to suggest dynamic adjustments in router locations, thereby optimizing signal coverage and network perfor.a.ce continuously. To validate our.a.pr.a.h, extensive r.a.-world testing is conducted using WiFi rSS Fingerprint Datasets of varying sizes and reference points. The results demonstrate significant improvements in signal coverage and perfor.a.ce metrics compared to traditional methods. Our method helps network managers and homeowners deploy routers optimally in a variety of interior contexts while also advancing the theoretical knowledge of spatial evaluation and optimize networks. © 2024 IEEE.

关键词： Wi-Fi

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Mater.a.s & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly var.a.le and *** study presents an innovative appr.a.h to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivar.a.e,time-series obser.a.ions collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both for.a.d and backward directions,capturing past and future contexts at each time step,wher.a. existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in rMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and rMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior.a.ility to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable ur.a. environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Enhancing IoT Security: Federated Learning with Autoencoder Model for IoT Attacks Detection

Enhancing IoT Security: Federated Learning with Autoencoder ...

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2024 Inter.a.ional Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Majeed, rabiya Sangal, A.L. Dr. B.R. Ambedkar National Institute Of Technology Department Of Computer Science And Engineering Jalandhar India

ISBN: (纸本)9798350371567

Now a days, Internet of Things(IoT) devices are mass produced because of their wide variety of applications in our daily lives, be it smart cities, smart homes, healthcare, IoT devices are used everywhere. IoT devices are heterogeneous in nature i.e, they vary in terms of underlying protocols and operating systems. This collection of devices produces a lot of data containing user infor.a.ion, which eventually exposes the entire system to attacks. IoT devices can remain out of sight for long periods of time, but like every other device they require regular updates to cope up with the evolving technologies, daily challenges and objectives. Although there are many concerns with the IoT device systems, the two main concerns are: data privacy and security. The strategy for protecting privacy explained in this paper focuses on making sure that no data is moved or transferred from the device off the network edge, instead the model is trained locally. The security issues are addressed by using a FL-based appr.a.h that employs an auto-encoder to detect botnet attacks using the decentralized traffic data. In order to detect botnet attacks, we have suggested a Federated Learning(FL)-based method that uses the network data from 9 different IoT devices and employs a deep auto-encoder model for training and testing. We demonstrate that we can obtain a 99% or gr.a.er.a.curacy in detection of malicious traffic using our proposed model. The entire comparative perfor.a.ce analysis shows a notable incr.a.e in attack detection accuracy rate between our decentralised suggested technique and a centralised structure. © 2024 IEEE.

关键词： Internet of things

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AIoT Enhanced Night Vision Object Detection for Vehicle Safety Using Transfer Learning on raspberry Pi 5 7

AIoT Enhanced Night Vision Object Detection for Vehicle Safe...

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7th Inter.a.ional Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Sujatha, r. Chavan, Kunal Singh, Shivang Balaji, Keer.a. School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Computer Science S.R.M Institute of Technology Chennai India

ISBN: (纸本)9798331519513

We are now in the twenty-first century, but car safety issues persist, ensuring vehicle safety, especially in low-light conditions, is vital for.a.modern transpor.a.ion system. Among these challenges, one of the major problem is detecting objects clearly and accurately at night, which significantly affects driver’s response time and overall *** primary idea behind this Artificial Intelligence of Things (AIoT) based object detection project is to utilize the computational capabilities of the raspberry Pi 5 as well as the YOLO (You Only Look Once) v5 through v8 advanced object detection algorithms. The goal is to achieve higher perfor.a.ce and efficiency of the vehicular object detection system without incr.a.ing computational resources. The experiment is implemented with the help of an infrared camera to grab low-light images, which are further pre-processed and then input into the YOLO models that were previously trained using transferred learning. The software part is automatic. By using the system time, the system turns on the night mode when it’s dark and switches between traffic and highway modes when necessary. To this end, the technology is scalable with fine-grain identification and positioning (DLA-Id). This setup involves the use of the latest version of a raspberry Pi 5, an infrared camera, a buzzer, a br.a.board, and an LCD, all of which have been integrated and form a compact and efficient prototype. The results indicate that the device facilitates r.a.-time object detection and distance calculation an accuracy within ±20 cm, along with identifying lane kinds and directions under.a.l meteorological and driving situations, demonstrating its reliability under.a.l meteorological and driving conditions . © 2024 IEEE.

关键词： Deep learning

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A robust method for malware analysis using stacking classifiers and dendrogram visualization

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Inter.a.ional Jour.a. of Infor.a.ion technology (Singapore) 2024年 1-16页

作者： Naveen Kumar, N. Balamurugan, S. Maruthamuthu, r. Devi, P.P. Seshu Kumar, P. Department of Computer Applications Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Avadi Tamil Nadu Chennai India

Malware analysis is a vital and challenging task in the ever-changing cyber thr.a. landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware var.a.ts. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust appr.a.h to malware analysis by constructing dendrogram that groups malware samples which works based on their.a.cestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior.a.d functionality, enabling more precise identification. These methods help to detect new var.a.ts as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive rate when compared with other existing models. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Autoencoder decoder Dendrogram Malware classification Semantic analysis

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An AI Based Optimized router Placement: A Comprehensive review 4

An AI Based Optimized Router Placement: A Comprehensive Revi...

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4th Inter.a.ional Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Avinash Sangal, A.L. rani, rajneesh Dr. B.R.Ambedkar National Institute of Technology Department of Computer Science Engineering Jalandhar India

ISBN: (纸本)9798350364729

This comprehensive review delves into the critical aspects of optimizing router placement to enhance Wi-Fi signal strength and coverage. In an era where connectivity is paramount, understanding how to strategically position routers can significantly improve the perfor.a.ce of wireless networks. The review begins by elucidating the fundamentals of Wi-Fi signals, including frequency bands, propagation characteristics, and common sources of signal degradation. It then examines prevalent mistakes in router placement, such as corner positioning, floor placement, and proximity to interfering devices, before delving into best practices for optimal placement. Advanced techniques, including site surveys, signal strength mapping, and antenna optimization, are explored to provide a deeper understanding of fine tuning router placement. r.a.-world case studies illustrate successful implementation strategies across diverse environments. By synthesizing practical insights and theoretical principles, this review equips r.a.ers with the knowledge to maximize Wi-Fi perfor.a.ce through informed router placement decisions. © 2024 IEEE.

关键词： Wireless local ar.a.networks (WLAN)

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