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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thirugnanasammandamoorthi, Puviyarasi Ghosh, Debabrata Dewangan, Ram Kishan Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331534950

Flood mapping is a crucial task in mitigating the adverse impacts of flooding by providing accurate spatial information about inundated areas. This study explores flood mapping using the ETCI dataset, leveraging the U-Net model and its enhanced variants. Initially, a basic U-Net architecture is employed to generate flood maps, followed by progressively improved versions incorporating batch normalization, dropout, and attention mechanisms. These modifications aim to enhance model generalization, robustness, and feature extraction capabilities. For the purpose of evaluating the model's performance, metrics like accuracy, F1-score, and Intersection over Union (IoU) are utilised. The generated flood maps demonstrate the potential of advanced U-Net architectures for precise and efficient flood delineation, providing a valuable tool for disaster management and planning. © 2024 IEEE.

关键词： Disaster prevention

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Black Hole Attack Detection Using Dynamic Threshold Values and Residual Energy in VANETs

Black Hole Attack Detection Using Dynamic Threshold Values a...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Shalom Priscilla, I. Senthil Kumar, C. Buvaneswari, B. Shiva Kumar, M. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India SRM Madurai College for Engineering and Technology Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798331512965

In black hole attack, a hostile vehicle or node within the VANET pretends to have the optimal route to the destination, convincing other nodes to route their data packets through it as they head towards the destination, trailing behind the malicious node, receives the data, it either discards the packets completely or forwards them selectively, causing disruptions in communication. To overcome this dilemma the research discusses a novel approach for categorizing nodes in AODV (Adhoc On demand vector Routing Protocol) networks according to their residual energy (RE) and forward rate (FR). With the goal to identify nodes that are considered energy-constrained, the proposed approach defines a residual energy threshold (RET) and includes dynamic threshold values (MinDTV and MaxDTV) for the forward rate. Three categories are applied to nodes: 1. (Trusted), 2. (Intermediate), and 3. (Untrusted or Energy-Constrained). This methodology results in a more trustworthy and energy-efficient network by dynamically updating node selection based on present forwarding behavior and levels of energy. The proposed approach boosts the overall strength, longevity as well, and integrity of RPL networks through the elimination of energy-depleted and unstable nodes;hence, it is especially suitable for applications in scenarios with scarce resources and malicious activity. © 2025 IEEE.

关键词： Black holes

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Feature Extraction Using Levy Distribution-Based Salp Swarm Algorithm 4th

Feature Extraction Using Levy Distribution-Based Salp Swarm ...

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Jayachitra, S. Devi, S. Nandhini Hariprasath, S. Khan, Javed Akhtar Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamilnadu India Department of Computer Science Engineering VSB Engineering College Tamilnadu India Department of Electronics and Communication Engineering Saranathan College of Engineering Tamilnadu India Department of Computer Science and Engineering Gyan Ganga College of Technology Madhya Pradesh Jabalpur India

ISBN: (纸本)9789819700363

Feature selection is a major pre-processing stage in machine learning to identify the optimal features through diminishing the redundant features to enhance the classification accuracy. The swarm intelligence algorithm provides superior optimal solution through distributive search over complete search in the feature space. Nevertheless, the SSA algorithm falls with local optima and poor convergence rate. In this paper, the levy distribution-based Salp Swarm Algorithm (LDSSA) framework is proposed to strengthen the balance among explorative and exploitative samples. The goal of this work is to minimize the higher dimensionality for larger datasets by feature extraction. The experimental results were examined on 10 Benchmark datasets from UCI repository to legitimize the supremacy of proposed algorithm with other existing methodologies. The comparative outcome shows the distribution improves the performance of SSA method and surpasses over other optimization algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Swarm intelligence

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Non-proliferative Diabetic Retinopathy(NPDR) using Deep learning approach 15

Non-proliferative Diabetic Retinopathy(NPDR) using Deep lear...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reethika, A. Priyadharsini, S. Sheetal, Attar Mahay Nivethitha, T. Kavitha, N. Vijaykumar, G. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India CMR Institute of Technology Department of Computer Science and Engineering Bangaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Electronics and Instrumentation Engineering Coimbatore India Sri Shanmugha College of Engineering & Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350370249

Non-proliferative diabetic retinopathy (NPDR), a frequent eye problem in diabetics, can result in vision loss. For prompt treatment and prevention, early detection is essential. Promising outcomes in NPDR detection have been observed in recent breakthroughs in computer vision and deep learning systems. In order to detect NPDR utilizing retinal fundus pictures, a unique deep learning-based approach is proposed in this study. The approach employs a fully connected layer for classification and a pre-trained convolutional-neural-network for extraction of feature. The algorithm performs better than state-of-the-art techniques by accuracy, specificity, sensitivity, and under the (ROC) receiver operating characteristic curve, according to experiments conducted on a publicly accessible dataset. © 2024 IEEE.

关键词： Contrastive Learning

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An Initial Solution Based Selective Harmonic Elimination Method for Multilevel Inverter

CPSS Transactions on Power Electronics and Applications

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CPSS Transactions on Power electronics and Applications 2024年第1期9卷 63-78页

作者： Kala, Peeyush Sharma, Abhinav Jately, Vibhu Joshi, Jyoti Yang, Yongheng Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Ghaziabad201204 India Department of Electrical and Electronics Engineering Dehradun248007 India Dehradun248007 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun248002 India College of Electrical Engineering Zhejiang University Hangzhou310027 China

This article presents an initial solution based selective harmonic elimination (SHE) method for multilevel inverter (MLI) that aims to solve SHE problem with high accuracy while significantly reducing the number of iterations. Initial SHE solution is defined as a set of initial switching angles that has less accuracy as compared to the final SHE solution. To find initial SHE solution, tunicate swarm algorithm (TSA), grey wolf optimization (GWO) and whale optimization (WO) are used to solve five-dimensional SHE problem. A comparative analysis demonstrates that TSA achieved the desired initial SHE solution within lesser iterations as compared to GWO and WO for a wide range of modulation indices. The initial switching angles found using TSA are further optimized using Newton-Raphson (NR) method being the proposed TSANR method, which exploits the SHE search space with quadratic rate of convergence to find final SHE solution. The proposed TSANR method significantly minimizes the computational time and further improves the accuracy of the SHE solution as compared to the state-of-the-art SHE methods. Detailed simulations, experimental and harmonic analysis under dynamic change in modulation index are presented to show the efficacy of the proposed SHE method in terms of control over fundamental and detrimental harmonics. © 2017 CPSS.

关键词： Harmonic analysis

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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

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computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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Classification of Crop Across Heterogeneous Landscape Through Experienced Artificial Bee Colony

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SN computer Science 2024年第4期5卷 428页

作者： Ravikiran, H.K. Jayanth, J. Sathisha, M.S. Yogeesh, G.H. Dileep, R. Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan India Department of Electronics and Communication Engineering GSSS Institute of Engineering and Technology for Women Karnataka Mysore India Department of Computer Science and Engineering Navkis College of Engineering Karnataka Hassan India

This study aims to develop an experienced artificial bee colony algorithm which is proposed for classification of crops using remote sensed data. The study was conducted in Devnur village located in Nanajangudu taluk of Mysore district, where a highly differed irrigated agriculture system is present. The study is designed to overcome the issues of misclassification in crop patterns due to similar crop Phenology. In Artificial Bee Colony algorithm a single parameter like waggle dance is used for training and validating the classes particularly in high resolution images, this may lead to misclassification of classes like crops which has a same spectral features. In order to avoid this misclassification, a group of experienced employed bee is been trained and updated by each employed bee through a waggle dance where each employed bee depends on its own experience and the current terrain in each space. Here these features help in identifying the classes and updating of the weights in order to map the agents and identifying the classes and robust the spatial and spectral features. This resultant algorithm is called Experienced Artificial bee colony (EABC). The proposed strategy gives a structure within which a pixel based EABC gives commonly integral data to one another, so characterization process is refined through waggle dance. The findings of the experiment indicate that the suggested approach exhibits a 7% and 6% enhancement in Level 2, and an 8% and 5% improvement in Level 3 categories, respectively, compared to the Artificial Bee Colony (ABC) algorithm and Support Vector Machine (SVM). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ABC Cropped area EABC LISS IV data SVM

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Dynamic Resource Allocation in Edge Computing via Deep Deterministic Policy Gradient Reinforcement Learning 3

Dynamic Resource Allocation in Edge Computing via Deep Deter...

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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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An Efficient, Affordable and Reliable SMPS Battery Chargers for Automobile Industry 2

An Efficient, Affordable and Reliable SMPS Battery Chargers ...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Niveditha, J. Venkatesh, T. Reethika, A. Ramya, U. Sreejaa, S. Vinothini, V. Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

As we are utilizing Conventional vehicles, Electric Vehicles require charging the batteries, so the chargers for batteries are becoming more and more crucial within the automobile industry. While the current chargers mostly rely on bulky and inefficient transformer models, this proposed work aims to explore the integration of Switched-Mode Power Supply (SMPS) technology. The method employed in this work is that replaces the charging based on transformers with SMPS technology, with the goal of improving efficiency, reducing size, and reducing heat generation. The result of the project has a successfully developed battery charger that utilizes SMPS technology. It has achieved increased efficiency, reduced size, and lower heat generation in comparison to traditional models. Furthermore, the revised charger exhibits a simpler and more affordable circuit, catering to a wider consumer base. As a result, this product can be used in both workshops and automobile showrooms with more efficiency at an affordable cost. © 2024 IEEE.

关键词： Automotive industry

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A Smart Bin with Real-Time Monitoring and Garbage Level Tracking Using IoT 11

A Smart Bin with Real-Time Monitoring and Garbage Level Trac...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Ayyanagoudar, Anand Mohankar, Akash Gharte, Jayesh Pasnsare, Pooja Ainapure, Bharati Appasani, Bhargav Vishwakarma University Department of Computer Engineering India School of Electronics Engineering Kalinga Institute of Industrial Technology India

ISBN: (纸本)9798350350357

This paper presents a novel system for smart waste management using IoT technology. With the rapid growth of urban areas and population, proper waste management has become a crucial issue for maintaining a healthy environment. The proposed system utilizes weight sensors and ultrasonic transducers to monitor the fill levels of dust bins in real-time. The system provides a user-friendly web page that displays a visual representation of the garbage levels in each bin, allowing users to take appropriate actions based on the current status. The system also sorts trash according to their weight, providing a precise idea for transportation of products. The proposed system offers an innovative and advantageous approach to waste management, promoting efficient and sustainable practices. © 2024 IEEE.

关键词： Sustainable development

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