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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Gopal, Vidhya Surendar, U. Bajulunisha, A. Tamilselvi, M. Sathiyapriya, V. Saranya, N. Sona College of Technology Department of Computer Science and Engineering Salem India Srm institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331508456

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST-WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment. © 2025 IEEE.

关键词： Reinforcement learning

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Real-Time Underwater Trash Object Detection Using Enhanced YOLOv8 3

Real-Time Underwater Trash Object Detection Using Enhanced Y...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Pratap, Adimulam Raghuvira Reddy, Thumu Rampraveen Nikhila, Lanke Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331523893

The accumulation of trash in aquatic and marine environments has severe and significant consequences on marine ecosystems, resulting in a persistent environmental hazard. While volunteer trash pickup efforts have grown in recent years, they still demand considerable time, effort, and resources. To effectively address these challenges, the implementation of automated solutions is essential. This project aims to develop an efficient object detection deep learning model which helps in effectively identifying the underwater trash objects. The key challenges of underwater image-based localization come from object detection under low light conditions, also the presence of numerous small and overlapping targets, which significantly increase the complexity. In this project we have used improved Yolov8n object detection model achieving an impressive accuracy rate of mAP of 0.951. We have utilized diverse datasets covering a range of environmental conditions, including challenging low-light scenarios. Also, the integration of advanced preprocessing techniques and data augmentation has further enhanced our model's performance, ensuring reliable predictions in various underwater environments, thereby improving detection performance without introducing additional computational complexity. © 2025 IEEE.

关键词： Marine pollution

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Energy-aware multi-attribute trust modal for secure MANET-IoT environment

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Multimedia Tools and Applications 2024年第38期83卷 85637-85662页

作者： Aravindan, S. Rajaram, A. Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611 002 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam611 002 India

A self-organizing network with dispersed functionalities is known as a mobile-adhoc network (MANET). Modern technology has made integration, MANET, and the Internet of Things (IoT) very desirable. The MANET-IoT architecture that has been suggested uses a multi-cluster setup. Hybrid K-Mode Clustering (HKMC) is a method that has been suggested for use in cluster building. An approach called Spider Monkey Optimization (SMO) is used in each cluster to identify the best cluster head (CH). This algorithm takes into account numerous objective functions. An Energy-aware Multi-Attribute Trust (EMAT) model is proposed to ensure trusted environment. The EMAT algorithm works on twofold: (i) first, multi-attribute based trust value is computed by Multi-Agent Reinforcement Learning (MA-RL) algorithm and (ii) next, optimal route is selected by Secure & Energy Score (SES) based methodology. For data security, Multi-Attribute Cryptography (MAC) approach is presented. On the whole, the energy efficiency and security is improved for MANET-IoT environment. There is an improvement to the network in terms of residual energy, protection level, throughput, routing overhead, energy efficiency, and packet delivery ratio (PDR) according to the testing findings. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Breast cancer diagnosis using machine learning model 4th

Breast cancer diagnosis using machine learning model

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Arthi, T.S. Partheeban, N. Singh, Bhoomika Singh, Aparajitha Tripathi, Nikhil Department of Computer Science and Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitations in terms of time, cost, and potential subjectivity. This study proposes a lightweight Machine Learning (ML) model for IDC classification using digital whole-mount breast cancer slide images. The model aims to achieve high accuracy while being suitable for deployment in resource-constrained environments. We evaluate our model on a publicly available breast histopathology dataset and compare its performance with other Deep Learning (DL) models, focusing on both accuracy and computational efficiency. The results demonstrate that our model achieves a high classification accuracy (around 89.3 percent) with minimal processing power and memory requirements. Furthermore, successful deployment on a Raspberry Pi embedded system validates its potential for real-world application in resource-limited settings, potentially leading to improved breast cancer diagnosis accessibility. Index Terms—Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Deep Learning (DL), Invasive Ductal Carcinoma (IDC), Multilayer Perceptron (MLP), Raspberry Pi, Tiny Machine Learning (TinyML). © 2025 the Author(s).

关键词： Convolutional neural networks

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Skin Cancer Detection: A Hybrid Approach Combining CNNs and Explainable AI 2

Skin Cancer Detection: A Hybrid Approach Combining CNNs and ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kumar, M. Kiran Vaishnavi, J. Anjibabu, T. Jayasri, V. Lakireddy Bali Reddy College of Engineering Computer Science Engineering Department Mylavaram India

ISBN: (纸本)9798331505745

One of the most common and potentially deadly types of cancer is skin cancer, for which prompt and precise detection is essential to successful treatment and better patient outcomes. Conventional diagnostic techniques mostly rely on dermatologists' manual examinations, which can be laborious, subjective, and prone to mistakes, particularly in environments with limited resources. Furthermore, because of their similar visual features, the variety of skin cancer types from vascular lesions to melanoma and actinic keratosis presents further difficulties. In order to overcome these constraints, this study suggests an advanced deep learning-based method for automated skin cancer detection that makes use of Explainable Artificial Intelligence (XAI) and Convolutional Neural Networks (CNN). We utilized the Synthetic Minority Oversampling Technique (SMOTE) to rectify class imbalances and guarantee a well-balanced dataset after performing significant data augmentation strategies to increase data diversity and improve model robustness. This improved dataset was used to train a CNN model using convolutional operations, which demonstrated its capacity to generalize complex patterns in the data with an amazing accuracy of 95%. The DenseNet121 model then achieved an accuracy of 78%. Furthermore, the model's approach to decision-making was made interpretable through the use of XAI techniques like Grad-CAM, which improved understanding of its predictions and promoted confidence among medical experts. This method is a viable way to help dermatologists with early diagnosis and promote AI-driven healthcare solutions because it not only showed great accuracy in identifying skin cancer b © 2025 IEEE.

关键词： Convolutional neural networks

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Fatigue driving detection of urban road at night based on multimodal information fusion

Advances in Transportation Studies

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Advances in Transportation Studies 2023年第Special issue期2卷 171-188页

作者： Wang, W.X. Sun, B.G. Xia, R. College of Computer Engineering Chongqing College of Humanities Science & Technology Hechuan Chongqing401520 China

Due to the poor accuracy, low efficiency and poor stability of fatigue driving detection of urban road at night, this paper proposes a fatigue driving detection of urban road at night based on multimodal information fusion. Firstly, the multi parameter extraction of fatigue driving state of driver's eyes, mouth and head is completed;Then, based on multimodal information fusion rules, the weighted average method is used to measure fatigue parameters and achieve classification of fatigue state levels. Finally, the fatigue detection model of the neural network is established, and the driver's fatigue detection is completed through SVM model classification. The experimental results show that this method can effectively realize accurate detection of fatigue driving of urban roads at night. © 2023, Aracne Editrice. All rights reserved.

关键词： Fatigue testing

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Document Clustering Using Graph Based Fuzzy Association Rule Generation

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computer Systems science & engineering 2022年第10期43卷 203-218页

作者： P.Perumal Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatoreIndia

With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is ***,document summarization that is extracting the essential task with appropriate information,removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting *** this research,a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation(gFAR).Initially,the graph model is used to map the relationship among the data(multi-source)followed by the establishment of document clustering with the generation of association rule using the fuzzy *** method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with *** framework is provided in an interpretable way for document *** iteratively reduces the error rate during relationship mapping among the data(clusters)with the assistance of weighted document ***,this model represents the significance of data features with class *** is also helpful in measuring the significance of the features during the data clustering *** simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns(RRP),ROUGE score,and Discrimination Information Measure(DMI)***,DailyMail and DUC 2004 dataset is used to extract the empirical *** proposed gFAR model gives better trade-off while compared with various prevailing approaches.

关键词： Document clustering text summarization fuzzy model association rule generation graph model relevance mapping feature patterns

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Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

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computer Systems science & engineering 2022年第5期41卷 511-524页

作者： P.Rajeswari K.Jayashree Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data ***,web service composition gains maximum attention among researchers due to its significance in real-time *** of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole *** these models have failed to handle the uncertainties of *** resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by *** the other hand,trip planning is an essential technique in supporting digital map *** aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the *** the available web service composition solutions do not consider the complicated spatio-temporal *** resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location *** presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,*** the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition *** by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite ***,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user *** extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

关键词： Web service composition trip planning hybrid models firefly algorithm QoS aware services fuzzy logic

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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