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Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks

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China communications 2024年第10期21卷 113-131页

作者： P.Vinoth Kumar K.Venkatesh Department of Networking and Communications School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur 603203India

Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic *** adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par wi

关键词： clustering energy stability network lifetime seagull optimization algorithm(SEOA) whale optimization algorithm(WOA) wireless sensor networks(WSNs)

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A new perspective exploration of machine learning algorithms for defending Side-Channel attacks

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Multimedia Tools and Applications 2025年第16期84卷 17021-17040页

作者： Ramakrishnan, Vedhavathy Thoguluva Seethapathy, Murugaanandam Sundar, Ramesh Dhandapani, Saveetha Munusamy, Sundarrajan Balasubrahmaniam, Lakshmi Dhevi Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Machine learning algorithms are used in various real-time applications, where security is one of the major problems. Security is applied in various aspects of the application in cloud computing. One of the security issues is a side-channel attack, which is not a common problem that can be solved by a point solution. The emerging computing industries need a better solution for side-channel attack detection since the amount of data used in communication and sharing on the Internet is enormous. Several old research works have concluded that machine learning algorithms are highly suitable for data analytics. Hence, this paper has aimed to propose a better machine-learning model for side-channel attack detection. This paper explains some machine learning algorithms from a technical point of view. The explanation identifies that the Convolution Neural Network algorithm is selected as a better algorithm for side-channel attack detection. The proposed CNN model has experimented with Python software to verify the results. Since CNN is more efficient in data analytics, the analysis result produces more accurate predictions than earlier algorithms. The performance of the proposed CNN is compared with the earlier algorithms for evaluating it. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data Analytics

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Advanced Persistent Threat Detection and Mitigation Using Machine Learning Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 3691-3707页

作者： U.Sakthivelu C.N.S.Vinoth Kumar Department of Networking and Communications College of Engineering and Technology(CET)SRM Institute of Science and TechnologyKattankulathurChennai603203India

The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s *** cyber-attacks lead to the compromise of data *** proposed system offers complete data protection from Advanced Persistent Threat(APT)attacks with attack detection and defence *** modified lateral movement detection algorithm detects the APT attacks,while the defence is achieved by the Dynamic Deception system that makes use of the belief update *** termination,every cyber-attack undergoes multiple stages,with the most prominent stage being Lateral Movement(LM).The LM uses a Remote Desktop protocol(RDP)technique to authenticate the unauthorised host leaving footprints on the network and host *** anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of *** extracting various feature sets from the logs,the RDP sessions are classified using machine-learning techniques with high recall and *** is found that the AdaBoost classifier offers better accuracy,precision,F1 score and recall recording 99.9%,99.9%,0.99 and 0.98%.Further,a dynamic deception process is used as a defence mechanism to mitigateAPTattacks.A hybrid encryption communication,dynamic(Internet Protocol)IP address generation,timing selection and policy allocation are established based on mathematical models.A belief update algorithm controls the defender’s *** performance of the proposed system is compared with the state-of-the-art models.

关键词： Advanced persistent threats lateral movement detection dynamic deception remote desktop protocol Internet Protocol attack detection

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Neuro-facial Fusion for Emotion AI: Improved Federated Learning GAN for Collaborative Multimodal Emotion Recognition

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IEIE Transactions on Smart Processing and Computing 2024年第1期13卷 61-68页

作者： Saisanthiya, D. Supraja, P. Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

In the context of artificial intelligence technology, an emotion recognition (ER) has numerous roles in human lives. On the other hand, the emotion recognition techniques most currently used perform poorly in recognizing emotions, which limits their wide spread use in practical applications. A Collaborative Multimodal Emotion Recognition through Improved Federated Learning Generative Adversarial Network (MER-IFLGAN) for facial expressions and electro encephalogram (EEG) signals was proposed to reduce this issue. Multi-resolution binarized image feature extraction (MBIFE) was initially used for facial expression feature extraction. The EEG features were extracted using the Dwarf Mongoose Optimization (DMO) algorithm. Finally, IFLGAN completes the Emotion recognition task. The proposed technique was simulated in MATLAB. The proposed technique achieved 25.45% and 19.71% higher accuracy and a 32.01% and 39.11% shorter average processing time compared to the existing models, like EEG based Cross-subject and Cross-modal Model (CSCM) for Multimodal Emotion Recognition (MERCSCM) and Long-Short Term Memory Model (LSTM) for EEG Emotion Recognition (MERLSTM), respectively. The experimental results of the proposed model shows that complementing EEG signals with the features of facial expression could identify four types of emotions: happy, sad, fear, and neutral. Further more, the IFLGAN classifier can enhance the capacity of multimodal emotion recognition. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Emotion Recognition

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Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs

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China communications 2024年第9期21卷 159-178页

作者： N Tamilarasan SB Lenin P Mukunthan NC Sendhilkumar Department of Electronics and Communications Engineering Sri Indu College of Engineering and TechnologyHyderabadTelanganaIndia Department of Electronics and Communications Engineering Sri Manakula Vinayagar Engineering CollegePondicherryIndia

In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data *** clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more *** this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network *** SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor *** specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global *** the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation ***,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and *** results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.

关键词： Adaptive Grasshopper Optimization Algorithm(AGOA) Cluster Head(CH) network lifetime Teaching-Learning-based Optimization Algorithm(TLOA) Wireless Sensor Networks(WSNs)

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Enhanced Database Encryption System 5

Enhanced Database Encryption System

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Piyush Hanif, Laraib Srinivas, L.N.B. Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Kattankulatur India

ISBN: (纸本)9798350383522

This study, in particular, highlights the critical nature of Impacts of the Enhanced Database Encryption. System within contemporary enterprises. It places significant focus on critical goals, such as data consolidation [1]. Preservation of integrity, and enhanced security controls. Moreover, It emphasizes the essential nature of Database Management System. (DBMS) in streamlining various operational domains including customer relationship management, supply chain optimization and advanced analytics. Furthermore, the paper emphasizes the importance of dealing with such data privacy issues. regulatory compliance, as well as setting up a strong data governance frameworks. These efforts collectively contribute to the development of stable and highly reliable databases that are necessary for contemporary organizations that want to protect their strategic resources and preserve the faith in the digital-centered landscape. © 2024 IEEE.

关键词： Advanced analytics Data consolidation Data governance framework Integrity preservation

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An AlexNet Model for Diagnosing Left Ventricular Hypertrophy Using ECG Signals 12th

An AlexNet Model for Diagnosing Left Ventricular Hypertrophy...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ryakala, Deepika Balaji Srikaanth, P. Department of Networking and Communications SRM Institute of Science and Technolgy College of Engineering and Technology Kattankulathur India

ISBN: (纸本)9789819789450

Left ventricular hypertrophy represents the thickened wall condition related to the main pumping chamber of the heart which wither results in blood pressure elevation within the heart or poor action of blood pumping at certain times. The existing methods which contributed to the literature for diagnosing left ventricular hypertrophy using electrocardiogram (ECG) are determined to confirm a comparatively low sensitivity and classification accuracy. Deep learning (DL) approaches which are capable in attaining feature extraction automatically from ECG signals are determined to be potent in detecting cardiac diseases. In this paper, an AlexNet-based deep learning model is proposed for effective and rapid diagnosis of left ventricular hypertrophy using 12 lead ECG. This methodology initially removed the baseline drift and fine noise by denoising signals using discrete wavelet transformation. Then AlexNet model is applied dedicatedly by training and testing 90 and 10% of the data and determining the patterns from the samples and identify unknown patterns. The experimental investigation of the proposed AlexNet-based deep learning model confirmed better sensitivity of 97.21%, superior specificity of 96.79%, and maximized classification accuracy of 99.12%, compared to the CNN-LSTM, CNN-LSTM-AM and ResNet-based left ventricular hypertrophy diagnosis models used for comparison. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Heart

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A TRI-BAND AND AFFORDABLE MICROSTRIP PATCH ANTENNA FOR WIRELESS communications

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 29-41页

作者： Asileti, Suneel Kumar Srikanth, Mocherla Venkata Nagasrisha, Bhattiprolu Bhargavi, Rebba Department of Electronics and Communications Engineering Usha Rama College of Engineering and Technology Telaprolu India Department of Electronics and Communications Engineering Gudlavalleru Engineering College India Department of Mechanical Engineering Usha Rama College of Engineering and Technology Telaprolu India

This paper investigates the computational and experimental evaluation of a wireless communications tri-band, low-cost microstrip patch antenna (MPA). High-frequency structure simulator (HFSS), which is based on the frequency domain finite element method (FEM) simulation technology, is used to perform the numerical analysis of the proposed antenna. The recommended antenna, which was created and put together on a cheap FR-4 substrate, performs as predicted by simulation and measurement, which is used to support its effectiveness. The proposed antenna resonates in frequency bands of 13.4 GHz, 22.9 GHz, and 26.3 GHz. The antenna’s maximum gain values for these resonant frequencies are 5.66 dBi, 5.75 dBi, and 5.52 dBi, respectively. Nearly omnidirectional patterns offered by the proposed antenna make it ideal for wireless communication devices. © 2024 by Begell House, Inc.

关键词： Microstrip antennas

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Cricket Performance Analysis System Using Machine Learning Techniques 5

Cricket Performance Analysis System Using Machine Learning T...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mishra, Suraj Garg, Animesh Batra, Vansh Saxena, Devansh Om Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications 603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications India

ISBN: (纸本)9798331530013

The Cricket Performance Analysis System based on big data tools, like Hadoop and Apache Spark, gives realtime deep insights into performances from players and teams and conditions in matches based on large-scale cricket match data. It scales up speedily and finds patterns using distributed computing capabilities of Spark, which makes it possible to take decisions around performance improvements and strategies. The system applies a set of machine learning techniques including K-means clustering, Logistic Regression, and Random Forest classifiers to be used for the analysis of player performance metrics and match outcomes. Thus, through the conversion of raw data into actionable insights, teams can improve their strategies, make better improvements in performance, and thus gain an upper hand against other competing professional cricket teams. As a result, teams are best positioned to make adjustments in good time and direct finer tactics toward further enhancement of overall performance. © 2024 IEEE.

关键词： Adversarial machine learning

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DeepAllergy: A Deep Learning Approach for Accurate and Rapid Food Allergen Detection 10

DeepAllergy: A Deep Learning Approach for Accurate and Rapid...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vishal Aravinth, A. Aakash, G. Sanchay Kumar, S. Preethiya, T. College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

With food allergies becoming more common, technological developments provide new chances for enhancing safety and reducing hazards related to allergen exposure. Common allergens like milk, eggs, and peanuts can go unnoticed in a variety of food items, putting allergic individuals at a constant risk. This paper explores the intersection of deep learning and allergen detection, aiming to evaluate the performance of state-of-the-art models such as Vision Transform (VIT), YOLOv8, and EfficientNet with minimal fine-tuning, while addressing the pressing need for swift and effective allergen identification in the food industry. The dataset used for this comparative study consists of 6271 food images and is available on Kaggle. The model utilizes a textual dataset of food ingredients to identify the presence of allergens in the food. EfficientN et achieved a peak accuracy of 92.28%. © 2024 IEEE.

关键词： Food ingredients

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