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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Armacki, Aleksandar Sharma, Himkant Bajović, Dragana Jakovetić, Dušan Chakraborty, Mrityunjoy Kar, Soummya Carnegie Mellon University PittsburghPA United States Indian Institute of Technology Kharagpur Kharagpur India University of Novi Sad Faculty of Technical Sciences Department of Power Electronics and Computer Engineering Novi Sad Serbia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Novi Sad Serbia

ISBN: (纸本)9798350354058

We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the K-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization. © 2024 IEEE.

关键词： K-means clustering

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Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 801-816页

作者： J.Jean Justus M.Thirunavukkarasan K.Dhayalini G.Visalaxi Adel Khelifi Mohamed Elhoseny Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India School of Computing Science and Engineering Galgotias UniversityUttar Pradesh203201India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science&Engineering Bharath Institute of Higher Education and ResearchChennai600073India Department of Computer Science and Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates College of Computer Information Technology American University in the EmiratesDubaiUnited Arab Emirates Faculty of Computers and Information Mansoura UniversityEgypt

Wireless Sensor Network(WSN)forms an essential part of *** is embedded in the target environment to observe the physical parameters based on the type of *** nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing *** WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the ***,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in *** this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for *** presented model involves a two-stage process such as clustering and data ***,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster ***,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at ***,the aggregated data is transmitted to the BS where it achieves energy *** experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of *** experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

关键词： Clustering data aggregation energy consumption cluster head selection wireless sensor networks

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Improving Accuracy and Speed of Deep Learning Algorithms for Real-Time Data Analysis in Machine Learning

Improving Accuracy and Speed of Deep Learning Algorithms for...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Inamdar, Fatima M. Singar, Mahendra Kumar Naval, Preeti Pattanaik, Ashutosh Santhoshkumar, M.P. Maheswaran, S. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

This have a look at examines using deep learning algorithms for stepped forward accuracy and velocity in real-time facts evaluation in gadget studying. The goal is to offer a comprehensive assessment of recently developed optimization strategies for deep gaining knowledge of algorithms, as well as techniques to enhance their accuracy and overall performance. Additionally, the paper evaluates the potential of deep learning algorithms for actual-time information analysis, in phrases of their accuracy, pace, reliability, and scalability. In the end, the paper offers a survey of to be had datasets for deep learning, and gives hints for in addition studies. The results of this examine suggest that deep mastering algorithms can be used to enhance accuracy and speed of system getting to know for actual-time data evaluation, and that further studies must attention on exploring the ability of optimization techniques for accomplishing higher accuracy and pace. © 2023 IEEE.

关键词： Learning algorithms

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Secure Communication with a Multiple-Antennas Untrusted Relay in Presence of Randomly Distributed Adversary Jammers

SSRN

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SSRN 2024年

作者： Saedi, Hamed Mohammadi, Abbas Kuhestani, Ali The Electrical Engineering Department Amirkabir University of Technology Tehran*** Iran The Communications and Electronics Department Faculty of Electrical and Computer Engineering Qom University of Technology Qom*** Iran

In this article, we examine the achievable secrecy rate of a cooperative network including one source, one destination and one untrusted amplify-and-forward relay, where randomly located adversary jammers try to damage the wireless communications. In the studied system model, the relay has multiple-antennas, while the other nodes have a single-antenna. Additionally, the adversary jammers are placed following a Poisson Point Process (PPP) who propagate jamming with the goal of damaging the untrusted relay’s channel. The untrusted relay receives by maximal-ratio combining (MRC) strategy to mitigate the impact of jammers and retransmits the signal to the destination by exploiting maximum ratio transmission (MRT). To forbid the powerful untrusted relay from capturing the message sent by the source, the destination injects a pre-known artificial noise towards the relay, which is known as destination-assisted cooperative jamming. We represent a new closed-form solution for the ergodic secrecy rate (ESR) of the considered system model with Rayleigh fading channels. In the next step, we design an optimization problem for maximizing the achievable secrecy rate. The result is a new closed-form formula for the optimal power allocation (OPA) between source and destination. Several numerical results are provided to gain engineering insights from the impact of network/system parameters on the ESR. Our results illustrate that by increasing the number of relay antennas or the density of jammers, the ESR is reduced. © 2024, The Authors. All rights reserved.

关键词： Antennas

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An Enhanced Hybrid Cryptography Model for Online Banking Authentication and Security 2nd

An Enhanced Hybrid Cryptography Model for Online Banking Aut...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Awotunde, Joseph Bamidele Brahma, Biswajit Adeniyi, Abidemi Emmanuel Lauretta Nkonyeasua, Edogbo Imoize, Agbotiname Lucky Mejdoub, Youssef Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria McKesson Corporation 32559 Lake Bridgeport Street FremontCA94555 United States Department of Computer Science Bowen University Iwo Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

In the digital age, protecting the security and privacy of sensitive data has become of the utmost importance, particularly while conducting online financial transactions. This paper intends to build an advanced hybrid cryptography model, combining the strengths of the Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES) algorithms, to improve the authentication and security procedures of online banking systems. To provide a strong and effective encryption framework, the suggested model makes use of RSA’s asymmetric key advantages and AES’s symmetric key capabilities. Because AES is quick and efficient at encrypting large amounts of data, it is used to optimize performance while encrypting the actual data being transmitted. Meanwhile, RSA is used to solve the problem of key distribution, secure the exchange of symmetric keys, and enable secure communication. The goal of the paper is to create an extensive system that incorporates this improved hybrid cryptography model into the online banking authentication procedure. User authentication, transaction verification, and data secrecy are the system’s three main building blocks. To ensure the viability and feasibility of the suggested paradigm in real-world online banking scenarios, implementation difficulties such as key management, algorithm integration, and performance optimization are addressed. This research advances cryptographic methods of contemporary banking security by utilizing the advantages of AES and RSA in a hybrid approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

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computers, Materials & Continua 2022年第4期71卷 255-268页

作者： R.Krishnaswamy B.Sivakumar B.Viswanathan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India School of Computing SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Factually of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

关键词： Biomedical signals ecg disease diagnosis artificial intelligence parameter tuning gru model

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Anomaly Detection in Industrial Quality Control with computer Vision and Deep Learning

Anomaly Detection in Industrial Quality Control with Compute...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Mary, A. Jenita Umesh, V. Khairnar, Prerana Nilesh Sahana, R. Sangeethaa, S.N. Shanthi, H.J. Srm Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Department of Computer Science and Applications Chennai India Ksou Mysore Department of It Mysuru India Visvesvaraya Institute of Technology Department of Electronics and Telecommunication Engineering Chincholi Nashik India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of Artificial Intelligence and Machine Learning Chennai India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam India Hindustan Institute of Technology and Science Deemed to Be University Department of Computer Applications Padur India

ISBN: (纸本)9798350375442

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Smart manufacturing

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Design and Implementation of Blockchain-Based Smart Contracts for Enhancing Ancillary Services Management in Electricity Markets 10

Design and Implementation of Blockchain-Based Smart Contract...

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10th IEEE International Conference on Power and Energy, PECon 2024

作者： bt Junaidi, Nazreen Abdullah, Md Pauzi Shaaban, Mohamed Hashim, Nurul Izzati bt Abdullah, Johari B. Fazari, Atiq Firdaus bin Department of Electrical and Electronics Engineering University of Malaysia Sarawak Sarawak Kota Samarahan94300 Malaysia Faculty of Electrical Engineering University Teknologi Malaysia Johor Bharu Johor81310 UTM Malaysia Department of Electrical Engineering National Chung Cheng University Minxiong Chiayi621301 Taiwan Faculty of Computer Science and Information Technology University of Malaysia Sarawak Kota Sarawak Samarahan94300 Malaysia

ISBN: (纸本)9798350361971

This paper presents a possible solution to the challenges of managing ancillary services on power systems, focusing on the development and execution of robust smart contracts on the Ethereum blockchain. These contracts automate essential procedures like bid submission, Market Clearing Price (MCP) calculation, and settlement, reducing manual involvement and improving operational efficiency. They ensure transparency and accountability through security mechanisms like role-based access restriction and event monitoring. The contracts were created using Solidity programming language and implemented on the Ethereum Sepolia test network. Unit tests using Foundry confirmed the functionality of each contract component, indicating their accuracy and effectiveness. The study highlights the novel features of the smart contract architecture and its potential impact on ancillary service administration. ©2024 IEEE.

关键词： Smart contract

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Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism

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Frontiers of Information technology & Electronic engineering 2025年第1期26卷 93-108页

作者： Yinhong XIANG Kaiqing ZHOU Arezoo SARKHEYLI-HÄGELE Yusliza YUSOFF Diwen KANG Azlan Mohd ZAIN School of Communication and Electronics Engineering Jishou UniversityJishou 416000China Internet of Things and People Research Center Department of Computer Science and Media TechnologyMalmöUniversityMalmö20506Sweden Faculty of Computing Universiti Teknologi MalaysiaSkudai 81310Malaysia

The state space explosion,a challenge analogous to that encountered in a Petri net(PN),has constrained the extensivestudy of fuzzy Petri nets(FPNs).Current reasoning algorithms employing FPNs,which operate through forward,backward,andbidirectional mechanisms,are *** algorithms streamline the inference process by eliminating irrelevant componentsof the ***,as the scale of the FPN grows,the complexity of these algorithms escalates sharply,posing a significantchallenge for practical *** address the state explosion issue,this work introduces a parallel bidirectional reasoningalgorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation *** algorithminvolves hierarchically dividing a large-scale FPN into two sub-FPNs,followed by a converse operation to generate the reversalsub-FPN for the *** detailed mapping between the original and reversed FPNs is thoroughly *** operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN,with the final resultderived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs.A case study ispresented to illustrate the implementation process,demonstrating the algorithm’s significant enhancement of inference efficiencyand substantial reduction in execution time.

关键词： Fuzzy Petri net(FPN) State explosion Decomposition Parallel Bidirectional reasoning

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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