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Energy Efficient Unequal Fault Tolerance Clustering Approach

computer Systems Science & engineering 2023年第5期45卷 1971-1983页

作者： Sowjanya Ramisetty Divya Anand Kavita Sahil Verma NZ Jhanjhi Mehedi Masud Mohammed Baz Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadIndia School of Computer Science and Engineering Lovely Professional UniversityPhagwaraIndia Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

关键词： Energy-efficiency unequal fault tolerant clustering approach particle swarm optimization master data aggregator energy efficient time division multiple access optimal nodes

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A novel dominating set and centrality based graph convolutional network for node classification

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Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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Multivariate Broadcast Encryption with Group Key Algorithm for Secured IoT

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computer Systems Science & engineering 2023年第4期45卷 925-938页

作者： M.Suresh Kumar T.Purosothaman Department of Computer Sceince and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatore641110India Department of Electronics and Communication Engineering Government College of TechnologyCoimbatore641013India

The expanding and ubiquitous availability of the Internet of Things(IoT)have changed everyone’s life easier and more *** time it also offers a number of issues,such as effectiveness,security,and excessive power consumption,which constitute a danger to intelligent IoT-based *** managing is primarily used for transmitting and multi-pathing communications that are secured with a general group key and it can only be decrypted by an authorized group member.A centralized trustworthy system,which is in charge of key distribution and upgrades,is used to maintain group *** provide longitudinal access controls,Software Defined Network(SDN)based security controllers are employed for group administration *** service providers provide a variety of security *** are just a few software security answers *** the proposed system,a hybrid protocols were used in SDN and it embeds edge system to improve the security in the group ***-based algorithms compared with Group Key Establishment(GKE)and Multivariate public key cryptosystem with Broadcast Encryption in the proposed *** all factors are considered,Broadcast Encryption(BE)appears to become the most logical solution to the *** enables an initiator to send encrypted messages to a large set of recipients in a efficient and productive way,meanwhile assuring that the data can only be decrypted by defining *** proposed method improves the security,efficiency of the system and reduces the power consumption and minimizes the cost.

关键词： Internet of things encryption decryption group key software defined network public key security

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An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease

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SN computer Science 2024年第8期5卷 1140页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

The early Alzheimer’s disease (AD) classification is highly important as this neuro-degenerative disease causes severe problems particularly, loss of memory among the patients. In addition, classifying Normal Control (NC), Mild Cognitive Impairment (MCI) and AD in time assists the patient to undergo treatments in the initial period of the disease. Hence, it is necessary to develop a trust worthy categorization model to classify the patients with or without AD. One of principal modalities commonly used for AD classification is the magnetic resonance imaging (MRI), the analysis of which achieved improved classification performance in categorizing the stages of AD. Hence, this review paper presents the detailed survey of 55 research articles showing different strategies to classify AD. The two important categories of the research papers are the methods using machine learning strategies, and the methods using deep learning strategies. The papers are classified based on various modalities, datasets, performance indices, and the software tools used for implementation. In addition to this, various research gaps and the challenges associated with the existing works of AD classification are discussed. In addition, this review provides the future scope for researchers with the analysis of research issues seen in the literary works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease Deep learning and machine learning Magnetic resonance imaging Mild cognitive impairment

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An intelligent approach for anomaly detection in credit card data using bat optimization algorithm

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Inteligencia Artificial 2023年第72期26卷 202-222页

作者： Haseena, S. Saroja, S. Suseandhiran, N. Manikandan, B. Department of Information Technology Mepco Schlenk Engineering College India Department of Computer Applications National Institute of Technology Trichy India

As technology advances, many people are utilising credit cards to purchase their necessities, and the number of credit card scams is increasing tremendously. However, illegal card transactions have been on the rise, costing financial institutions millions of dollars each year. The development of efficient fraud detection techniques is critical in reducing these deficits, but it is difficult due to the extremely unbalanced nature of most credit card datasets. As compared to conventional fraud detection methods, the proposed method will help in automatically detecting fraud, identifying hidden correlations in data and reducing the time for the verification process. This is achieved by selecting relevant and unique features by using Bat Optimization Algorithm (BOA). Next, balancing is performed in the highly imbalanced credit card fraud dataset using the Synthetic Minority over-sampling technique (SMOTE). Then finally the CNN model for anomaly detection in credit card data is built using a full center loss function to improve fraud detection performance and stability. The proposed model is tested with the Kaggle dataset and yields around 99% accuracy. © IBERAMIA and the authors.

关键词： Optimization

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Elbow angle monitoring and error analysis using a wearable PDMS embedded FBG sensor

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IEEE Sensors Journal 2025年第11期25卷 19341-19348页

作者： Chalackal, Archana Thrikkaikuth Kanakambaran, Srijith Indian Institute of Information Technology Design and Manufacturing Kancheepuram Electronics and Communication Engineering Department Chennai India

Elbow flexion and extension are necessary movements for daily tasks. Elbow joint impairments can be brought on by aging, accidents, and repetitive motions making the monitoring of elbow angles critical. In this work, a sensor for elbow angle monitoring during bending and extension using a Fiber Bragg Grating (FBG) embedded in polydimethylsiloxane (PDMS) with a wearable band is proposed. Two methods for measuring elbow angles, wearable and platform-based sensing are investigated. The wearable sensor based measurements are consistent over different trials with a linear variation of Bragg wavelength with flexion/extension angle up to 90°. The sensitivities for flexion and extension are 0.0029 nm/° and 0.0037 nm/° respectively. Further, the factors contributing to error in the measurement of elbow angle such as tightness of wearable band, spatial and angular misalignment of the sensor and the removal and replacement of sensor are studied. The findings show that the error is maximum for an angular misalignment followed by tightly bound sensor and spatial misalignment. The desirable configuration for elbow angle measurement is a tightly bound sensor at the mean position for high mean sensitivity with a tolerable level of error and a left/right-aligned sensor with moderate tightness for minimum error at a low sensitivity. The PDMS-embedded FBG-based wearable sensor, in an optimal configuration, can be used for monitoring elbow angles. © 2001-2012 IEEE.

关键词： Angle measurement

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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AI-Based Vehicle Detection and Its Emission Impact on AQI 1

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19th Control Instrumentation System Conference, CISCON 2022

作者： Bhat, Suhas Sudhir Pai, M. D. Varun Holla, B. Ashutosh Pai, M. M. Manohar Department of Computer Science and Engineering Manipal Institute of Technology Manipal576104 India Department of Computer Science and Engineering Government Polytechnic Karkala Karkala574104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal576104 India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

Vehicle traffic has a significant impact on urban quality of life. One major impact is on the air quality of the surrounding area. With increasing vehicle traffic, air quality gets drastically affected in urban cities. A dense accumulation of vehicles is commonly observed across a network of roads during peak hours, drastically affecting the air quality index of the surrounding environment. Adverse measures and prevention are required to control air quality to maintain a healthier environment. Hence, determining vehicle emission details is necessary to address the overall impact in the surrounding areas. For any organization/gated campus, it is required to minimize the air pollution caused by vehicles that regularly visit the campus. By identifying the vehicles appearing on the campus premises, their emission impact on the surrounding can be determined using the PUC certificate of the vehicle. This study performs a vehicle emission impact on a gated campus using a deep learning approach. Real-time surveillance footage is processed with a deep learning model to detect vehicle and its license plate. Furthermore, air quality sensors deployed at strategical locations provide real-time data on pollutant concentration. This combined information is further utilized by a Web application to provide statistics of AQI in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air quality

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Enhanced U-Net and PSO-Optimized ANFIS for Classifying Fish Diseases in Underwater Images

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International Journal of Fuzzy Systems 2024年第8期26卷 2518-2535页

作者： Khabusi, Simon Peter Huang, Yo-Ping Lee, Mong-Fong Tsai, Meng-Chun Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Aquaculture National Penghu University of Science and Technology Penghu88046 Taiwan General Competency Center National Penghu University of Science and Technology Penghu88046 Taiwan

Fish diseases are among the major limiting factors to increase global aquaculture production. They lead to increased fish mortality, low breeding and growth rates, and low meat quality. The success of aquaculture is heavily dependent on the timely identification of disease. Therefore, we propose a fuzzy U-Net model to automatically identify fish disease from underwater images. U-Net is enhanced with multi-head channel and spatial attention and used to segment infected fish regions from fish disease images. Color pixel intensity features are then extracted from the localized regions, a form of guided feature extraction. Fuzzy C-means clustering is then used to find the cluster centroids and data distribution within the clusters, for the design of fuzzy membership functions. Moreover, the number of clusters are determined by silhouette score. Adaptive neuro-fuzzy inference system (ANFIS) is then trained, tested, and cross-validated for fish disease identification. The model parameters are optimized using particle swarm optimization (PSO) algorithm and compared with gradient-based methods. For image segmentation, the enhanced U-Net achieved a mean intersection over union (IoU) of 86.29%, mean pixel accuracy of 90.94%, mean precision of 93.58%, and mean recall value of 89.94% on 42 test images. Subsequently, ANFIS with PSO achieved overall superior performance on fish disease identification over gradient-based methods, with accuracy of 99.31%, precision of 99.00%, recall of 99.00%, and F1-score of 99.00%. The high-performance results of the optimized ANFIS confirm the robustness and efficacy of the proposed method to automatically identify fish diseases in aquaculture. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fuzzy inference

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Deep Learning-Driven Edge-Enabled Serverless Architectures for Animal Emotion Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 33-48页

作者： Benedict, Shajulin Subair, Rubiya Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala India Technical University Munich Garching Germany

Animal emotion detection, including elephant emotions, is highly possible, but what the traditional emotion detection approaches highlight is their blatant ignorance of adopting edge-enabled intelligence and serverless-based solutions, both of which are affordable. Treating the emotions of animals increases their productivity, especially among trained elephants when subjected to carrying logs or undertaking gargantuan tasks. However, existing infrastructures are inefficient in handling long-running animal emotion detection-related tasks. This article proposes a deep learning-driven edge-enabled serverless architecture after evaluating several existing animal emotion detection techniques. Additionally, we perform an exploratory study on the cost impact of incorporating serverless-enabled approaches to animal emotion detection architectures. We observed that the proposed edge-enabled serverless architectures saved over 13,000 dollars annually compared to traditional animal emotion detection approaches. In addition, the article provided a few research directions to develop novel edge-enabled serverless architectures that boost socio-economic situations while avoiding human-animal conflicts. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Invertebrates

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