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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua Science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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An Efficient Quantum Enabled Machine Algorithm by Universal Features for Predicting Botnet Attacks in Digital Twin Enabled IoT Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 947-956页

作者： Katta Rajesh Babu Naramula Venkatesh K.Shashidhar Yellampalli Dasaratha Rami Reddy K.Naga Prakash Department of ECE Koneru Lakshmaiah Education FoundationVijayawada 520002India School of Computer Science and Artificial Intelligence SR UniversityWarangal 506371India Department of Electronics & Communication Engineering Siddhartha Institute of Technology & SciencesHyderabad 500091India Department of CSE Chaitanya Bharathi Institute of TechnologyProddatur 520002India Department of ECE Seshadri Rao Gudlavalleru Engineering CollegeProddatur 520002India

In this manuscript,the authors introduce a quantum enabled Reinforcement Algorithm by Universal Features(REMF)as a lightweight solution designed to identify and assess the impact of botnet attacks on 5G Internet of Things(IoT)***'s primary objective is the swift detection of botnet assaults and their effects,aiming to prevent the initiation of such *** algorithm introduces a novel adaptive classification boosting through reinforcement learning,training on values derived from universal features extracted from network transactions within a given training *** the prediction phase,REMF assesses the Botnet attack confidence of feature values obtained from unlabeled network *** then compares these botnet attack confidence values with the botnet attack confidence of optimal features derived during the training phase to predict the potential impact of the botnet attack,categorizing it as high,moderate,low,or not-an-attack(normal).The performance evaluation results demonstrate that REMF achieves the highest decision accuracy,displaying maximum sensitivity and specificity in predicting the scope of botnet attacks at an early *** experimental study illustrates that REMF outperforms existing detection techniques for predicting botnet attacks.

关键词： botnet reinforcement learning 5G-enabled Internet of Things(IoT) network security machine learning

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Design Analysis of Stroke Risk Prediction Model Employing the Hybrid Structure Implementation of Deep Transfer Learning System

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Journal of The Institution of Engineers (India): Series B 2024年第5期105卷 1197-1206页

作者： Ahammad, Shaik Hasane Pande, Sandeep Dwarkanath Rajesh, V. Inthiyaz, Syed Priya, P. Poorna Krishna, M. Rama Hossain, Md. Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram522302 India MIT Academy of Engineering Alandi Pune India Department of ECE Dadi Institute of Engineering and Technology Anakapalle Visakhapatnam India Department of ECE Andhra Loyola Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of VLSI Microelectronics Saveetha Institute of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Saveetha University Tamilnadu Chennai602105 India

Stroke has been given the highest priority among all widespread diseases which effectuate mortality or interminable debility in aging individuals over the globe. The study aims to progress the conventionally developed stroke risk design and predict the risk level based on the dataset collected from various sources concerning weighing factors. Moreover, the study considers employing artificial intelligence attributed to the comparative analysis of risk estimated with the probability of occurring in 10 years. A framework of Deep learning can outperform all the conventional models even with the dependency on big data. Because of the stringent confidentiality safety policy in healthcare systems, the data related to stroke is typically delivered among numerous hospitals in insignificant parts. It is a proven state-of-the-art technology for modeling stroke risk prediction. Nevertheless, the instances of data with positive and negative values undergo life-threatening and imbalanced cases. Therefore, a novel technique of Hybrid Deep Transfer Learning-based Stroke Risk Prediction arrangement for the potential to develop the configuration of multiple associated sources (such as chronic diseases, external stroke data, such as diabetes and hypertension). Extensive investigation has been conducted for the proposed framework in both synthetic and real-world circumstances. Finally, outcomes demonstrate the ability of real-world implementation, including several clinics facilitated with 5G/B5G substructures. © The Institution of Engineers (India) 2024.

关键词： Learning systems

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A Simulation Study of n-ZnO/Perovskite/p-Cu2SnS3 Based Self-Powered Photodetector: Design and optimisation

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Materials Research Innovations 2023年第7期27卷 503-510页

作者： Yadav, Sanjeev Mani Pandey, Amritanshu Department of Electronics and Computer Science National Institute of Advanced Manufacturing Technology Jharkhand Ranchi India Centre for Research in Microelectronics Department of Electronics Engineering IIT-BHU Varanasi India

Perovskite has been widely used in the field of optoelectronics owing to its superb optoelectronic properties. The simplest fabrication along with low-temperature handling of perovskite-based planner structures (p-i-n or n-i-p) has drawn their potential impact in organic–inorganic lead halide optoelectronic devices. The poor conductivity, stability, and high processing cost of absorber materials hinder the optical performance of the device. The sandwiching of the perovskite as absorber materials between two inorganic materials working as electron transport layer (ETL) and hole transport layer (HTL) may be a possible solution. An active absorber perovskite (CH3NH3PbI3) assisted by two n-ZnO and p-CTS materials working as an ETL and HTL, respectively, has been used to maximize the overall performance of the proposed device. The resultant optimized device shows the maximum responsivity of ∼0.35 (A/W) for a broad visible spectrum (300–900 nm), while the EQE (%) reported in the range of 25–80%. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Photodetectors

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Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

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Multimedia Tools and Applications 2025年第5期84卷 2209-2260页

作者： Karthiga, R. Narasimhan, K. Thanikaiselvan, V. Hemalatha, M. Amirtharajan, Rengarajan Department of ECE SRM Institute of Science and Technology Ramapuram Chennai600089 India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613 401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Breast cancer is a significant health concern;early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Design and Analysis of Printed Conformal Antenna System for Inter and Intra Vehicular (V2V) Communication Utilizations

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Progress In Electromagnetics Research C 2024年 142卷 143-150页

作者： Raju, Padmanabha Rao, Bathula Sadasiva Jackson, Beulah Islam, Tanvir Madhav, Boddapati Taraka Phani Das, Sudipta Devi, Yalavarthi Usha Department of ECE Sri Vishnu Engineering College for Women AP Vishnupur Bhimavaram India Department of ECE Swarnandhra College of Engineering and Technology Seetarampuram AP Narsapur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Antennas and Liquid Crystal Research Center Department of ECE Koneru Lakshmaiah Education Foundation AP India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

A multiple antenna placement system analysis for the improvement of efficiency and capacity for inter and intra vehicular communication is proposed in this article. Four antennas are placed in the four locations of the vehicular body which includes roof, side mirror, rear screen, and dashboard. The constituted antenna occupying the dimension of 40×38.5×0.2 mm3 on flexible substrate material of photo paper and the bending analysis of the model as per the conformal nature on vehicular body is also analyzed and presented in this work. The received power from each receiving antenna response with respect to the transmitter has been analyzed. The channel capacity with respect to the antenna position for V2V communication is analyzed in different areas and in different environmental conditions. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Receiving antennas

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

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computer Systems Science & engineering 2023年第4期45卷 279-292页

作者： R.Nandakumar P.Saranya Vijayakumar Ponnusamy Subhashree Hazra Antara Gupta Department of ECE K S R Institute for Engineering and TechnologyTiruchengode637215India Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication and Engineering SRM Institute of Science and TechnologyChennai603203India

A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before ***,early detection of DR is the necessity of the *** primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater *** procedures for diabetic retinopathy analysis are fundamental in taking care of these *** profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase *** Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal *** presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by *** results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed *** same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and Proliferative DR images with 93%accuracy.

关键词： Convolutional Neural Networks vision loss pathogenic blood vessels DenseNet AlexNet ResNet

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Improved Energy Balancing based Data Forwarding incorporating Whale optimization Technique in Underwater Acoustic Sensor Network 3

Improved Energy Balancing based Data Forwarding incorporatin...

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3rd International Conference on Electrical, electronics, Information and Communication Technologies, ICEEICT 2024

作者： Bhairavi, R. Thiruveni, M. Anita Christaline, J. Ashokkumar, N. Chandra, I. Nagarajan, P. Srm Institute of Science and Technology Department of Ece Chennai Vadapalani India Psna College of Engineering and Technology Department of Ece Dindigul India Vit Chennai School of Computer Science and Engineering India Department of Electronics and Communication Engineering Andhra Pradesh Tirupati India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350369083

UWASN has various challenges such as minimal bandwidth availability, long propagation delay, increased energy exhaustion, increased deployment cost and security. The routing protocols should facilitate an enhanced data forwarding towards sink nodes. In this paper, an Improved Energy Balancing based Data Forwarding incorporating Whale optimization Technique (IEBDF-WO) is proposed to effectively elect optimal forwarders to participate in the routing process. The Whale optimization algorithm has three phases namely, scouring phase, encompassing phase and final bubble net raiding phase for facilitating enhanced data transmission towards sink node. The proposed technique determines a fitness function by using the weighted depth difference WD, Potential Relay Count (PRC), Residual energy of each potential relay node. The performance of the proposed IEBDF-WO technique is analyzed and is collated with the existing Energy-Efficient Probabilistic Depth-Based Routing (EEPDBR) technique. It is seen that, for a network comprising of 200 nodes, the proposed scheme has improved Packet Delivery Ratio of 96.04% while minimizing the end to end delay, energy tax and average number of hops. © 2024 IEEE.

关键词： encompassing phase fitness function potential relay count scouring phase whale optimization

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Optimized Machine Learning Techniques for Precise Breast Cancer Detection in Mammograms

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SN computer Science 2025年第4期6卷 1-22页

作者： Kiran, Puttegowda Veeraprathap, V. Rajashekhar, U. Mahantesh, Mathapati Sudheesh, K.V. Bharath, Kumara Prabhavathi, K. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of Electronics and Communication Engineering ATME College of Engineering Mysuru Karnataka Mysuru India Department of ECE Government Engineering College Huvinahadagali Karnataka Vijayanagara India Department of Computer Science & Engineering Amruta Institute of Engineering and Management Sciences Karnataka Bengaluru India Department of ECE M S Ramaiah University of Applied Science (MSRUAS) Karnataka Bangalore India Department of ECE BGS Institute of Technology Adichunchanagiri University B. G. Nagara Karnataka Mandya India

Breast cancer has recently overtaken cervical cancer as the predominant cancer type in Indian urban areas. Despite considerable research and the development of automated diagnostic machines, current methods are far from perfect, necessitating more reliable medical assessments. Moreover, there's been relatively little research on Indian datasets compared to international resources, despite significant differences due to factors like denser breasts, varying textures, lesion sizes, and compositions. To address this gap, our work focuses on using machine learning to automate breast cancer detection, tailoring models to Indian breast types and utilizing metadata for more accurate assessments. Our analyses show promising results, with the best single model achieving a per-image AUC of 0.95, and averaging four models increasing AUC to 0.98 (sensitivity: 86.7%, specificity: 96.1%) on an independent test set from the INbreast database. These findings highlight the potential of deep learning techniques in improving mammography accuracy and reducing false positives and negatives in clinical settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomedical applications Breast cancer Deep neural networks Faster-RCNN Machine learning models Mammograms Object detection YOLO V3

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