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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Darney, P. Ebby Vallileka, N. Manoj, Sumitha Fernando, A. Vegi Krishnan, R. Santhana Prasath, S. Ram RajaRajeswari College of Engineering Department of Electrical And Electronics Engineering Bangalore India Psna College of Engineering And Technology Department of Computer Applications Dindigul India RajaRajeswari College of Engineering Department of Electronics And Communication Engineering Bangalore India Bangalore Harohalli India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

In contemporary vehicular communication systems, Vehicular Ad Hoc Networks (VANETs) serve a crucial role in enabling seamless data exchange among vehicles and infrastructure components. However, the dynamic and unpredictable nature of vehicular environments renders VANETs susceptible to various faults and disruptions, potentially compromising network performance and jeopardizing safety-critical applications. Therefore, the development of robust fault prediction and recovery mechanisms is imperative to ensure the reliability and resilience of VANETs. This research introduces a novel Hybrid Fault Prediction and Recovery Framework for VANETs utilizing the paradigm of Artificial Intelligence (AI) and Federated Internet of Things (IoT). The proposed algorithm, Deep Neural Network with Federated IoT Model (DNN-FIOT), is presented to enhance fault prediction and recovery efficacy in VANET environments. Extensive simulation analyses comparing DNN-FIOT with existing algorithms are conducted, employing suitable simulation metrics. Results demonstrate the superior performance of DNN-FIOT in terms of fault prediction accuracy, recovery time, and network stability. The proposed framework offers a promising solution for robust fault management in VANETs, ensuring uninterrupted vehicular communication and safety. © 2024 IEEE.

关键词： Internet of things

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Determination of the time location points and investigation of the body sensor network using RATDMA and PATDMA

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9043-9055页

作者： Mohammed Mustafa, M. Kalpana Devi, S. Althaf Ali, A. Gunavathie, M.A. Department of Information Technology Sri Krishna College of Engineering & Technology Coimbatore India Department of Computer Science and Engineering RV University Bengaluru India Andhra Pradesh India Department of Information Technology Panimalar Engineering College Chennai India

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate. © 2024 – IOS Press.

关键词： Location

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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Artificial Intelligence Based Smart Routing in Software Defined Networks

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computer Systems science & engineering 2023年第2期44卷 1279-1293页

作者： C.Aswini M.L.Valarmathi Department of Information Technology Government College of TechnologyCoimbatore641013India Department of Computer Science and Engineering Dr.Mahalingam College of Engineering and TechnologyPollachi642003India

In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the *** Deﬁned Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible *** various features of routing are supported by the information centric network,which minimizes the congestion in the dataﬂow in a network and pro-vides the content awareness through its mined *** to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-deﬁned *** there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in *** this regard,artiﬁcial intelligence meth-odology has been used in the proposed approach to solve these difﬁculties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with *** novel aligned internet investigation technique has been proposed to process the deep reinforcement *** performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.

关键词： Content aware routing software deﬁned networks deep learning load balancing

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models 2

Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Mo...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ravikumar, S. Raj, Arockia Babu, R. Vijay, K. Ramani, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai600062 India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Chennai603203 India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamilnadu Chennai602117 India Department of Computer Science and Engineering P.S.R. Engineering College Tamilnadu Sivakasi626140 India

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Network architecture

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

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IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Enriched Glove of Word Embedding and BERT of Contextual Embedding in Sarcasm Detection with AdaGrad Optimization Technique 3

Enriched Glove of Word Embedding and BERT of Contextual Embe...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Arul Jothi, S. Pandeeswari, N. Nadar Saraswathi College of Engineering and Technology Computer Science and Engineering Tamilnadu Theni India PSNA College of Engineering and Technology Department of Information Technology Tamilnadu Dindigul India

ISBN: (纸本)9798350343632

The word Sarcasm related to other terms such as derision, irony, mockery, ridicule, scorn, contempt, bitterness, etc. is a challenging task in the area of Natural Language Processing (NLP). Even humans can find it difficult to understand the ironic words. The sentences may be categorized into positive sentiments. But in-depth contextual information points to negative or sarcasm. Lack of understanding of the contextual meaning of text and POS tagging and grammar may lead to critical tasks in identifying sarcasm. Nowadays, Sarcasm is a prominent crime, and being increases exponentially through social media in the form of text. Identifying the text as sarcasm is only by way of understanding the contextual information. After the preprocessing steps involved, features can be extracted through Word Embedding Techniques of GloVE(Global vectorization) and Contextual Embedding of the Transformer based approach, (Bi-Directional Encoder Representation of Transformer) BERT of neural networks models to classify the sentence as Sarcasm or Non-sarcasm. The main objective of the proposed work is to enrich contextual information with deep learning models like GloVe and BERT with the Adagrad optimization techniques. Furthermore, the work was tested with the SARC benchmark dataset and compared with related works which highlights the accuracy rate by increasing 7%. © 2023 IEEE.

关键词： Adagrad Optimization BERT GloVe Natural Language Processing

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