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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Singaravelan, M. Saleem Raja, A. Thangam, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India College of Computing and information Sciences University of Technology and Applied Sciences Shinas Oman Amrita school of computing Department of computer science and engineering Bengaluru India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

RL stands for reinforcement learning, is a component of the broader field of machine learning. It provides a framework its features help to select an efficient action using previously learned information of interaction and its environment. Mobile nodes are grouped to form a network for communicating themselves termed autonomous network with mobile nodes (MANET). This is a dynamic and independent network requiring no administrative control. The mobile nodes in the network are to be part or apart at any time that causes the topology changes. The frequent topology changes make it hard to determine the destination route. The route discovery in the dynamic topology that consumes more node energy and, minimize the network lifetime. The energy usage and network lifetime are a vital part of the MANET. This paper proposes a new global routing protocol using a reinforcement learning algorithm called modified energy and signal strength Q-learning ad hoc on-demand distance vector algorithm (ESQAODV). This process conducts a result comparison to a local routing algorithm known as AODVSARSA. The proposed algorithm proved a better result with reference to average distribution ratio compared to the other, and complete delay metrics © 2024 IEEE.

关键词： Adversarial machine learning

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SMS-Shield: A Lightweight Approach for Smishing Detection using Machine Learning 1

SMS-Shield: A Lightweight Approach for Smishing Detection us...

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1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Samad, Saleem Raja Abdul Ganesan, Pradeepa Thangam, S. Balasubramaniyan, Sundaravadivazhagan Rajiakodi, Saranya Al Kaabi, Amna Salim Rashid University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences Information Technology Department Shinas Oman Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India University of Technology and Applied Sciences-Al-Musannah College of Computing and Information Sciences Information Technology Department Al-Musannah Oman Central University of Tamil Nadu Department of Computer Science Tamil Nadu Thiruvarur India

ISBN: (纸本)9798350348637

SMS continues to be an indispensable component of contemporary digital communication. Because it is so widely available, cost-effective, reliable, and delivers information instantly. Its versatility enables its utilization for real-time chats, alerts, and verification purposes. Nevertheless, the ubiquitous nature of SMS renders it an easy prey for smishing, a deceitful form of cyberattack. Cybercriminals exploit the trust associated with SMS messages to deceive recipients. Smishing, an increasingly prevalent deceptive SMS fraud, poses a threat to both businesses and individuals. These messages include websites or phone numbers and purport to be from banks, governmental bodies, or well-known businesses. These misleading text messages lure their users into disclosing private information or clicking on links that could be harmful in many ways. Early detection of such malicious messages prevents users from falling victim to them. Various techniques were presented by researchers to accurately detect smishing attacks. Machine learning models exhibit great potential as a strategy to counter the increasingly dangerous SMS phishing attacks. Machine learning models can discern legitimate from fraudulent SMS messages by analyzing datasets. This paper introduces a novel method (SMS-SHIELD), which combines Machine Learning and Natural Language Processing techniques to enhance smishing detection. This approach yields outstanding results, with random forest and extreme gradient boosting algorithms achieving remarkable accuracy rates of 99.05% and 98.03%, coupled with impressive F1-scores of 98.95% and 97.86%, respectively. © 2024 IEEE.

关键词： Phishing

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Whale Optimization Algorithm Strategies for Higher Interaction Strength T-Way Testing

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Computers, Materials & Continua 2022年第10期73卷 2057-2077页

作者： Ali Abdullah Hassan Salwani Abdullah Kamal Z.Zamli Rozilawati Razali Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Computing College of Computing and Applied SciencesUniversiti Malaysia PahangPekan26600PahangMalaysia

Much of our daily tasks have been computerized by machines and sensors communicating with each other in *** is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of *** testing(CT)can be used in this case to reduce risks and ensure conformance to *** existing metaheuristic-based solutions aim to assist the test suite generation for combinatorial testing,also known as t-way testing(where t indicates the interaction strength),viewed as an optimization *** previous research,while helpful,only investigated a small number of interaction strengths up to t=*** lightweight applications,research has demonstrated good fault-finding ***,the number of interaction strengths considered must be higher in the case of interactions that generate large amounts of *** to resource restrictions and the combinatorial explosion challenge,little work has been done to produce high-order interaction *** this context,the Whale Optimization Algorithm(WOA)is proposed to generate high-order interaction *** ensure that WOA conquers premature convergence and avoids local optima for large search spaces(owing to high-order interaction),three variants of WOA have been developed,namely Structurally Modified Whale Optimization Algorithm(SWOA),Tolerance Whale Optimization Algorithm(TWOA),and Tolerance Structurally Modified Whale Optimization Algorithm(TSWOA).Our experiments show that the third strategy gives the best performance and is comparable to existing state-of-thearts based strategies.

关键词： Software testing optimization problem swarm intelligence algorithm combinatorial optimization IoT

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms 4

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

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4th IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Thangam, S. Dixit, Aniket Karn, Alok Kumar Thakur, Arnav Arunbalaji, Cg Jesy Janet Kumari, J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350393354

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion;despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Chest X-Ray-Based Covid-19 Detection Using Preprocessing Techniques and Deep Learning Algorithms 7th

Chest X-Ray-Based Covid-19 Detection Using Preprocessing Tec...

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7th International Conference on Soft computing: Theories and Applications, SoCTA 2023

作者： Kurian, Thanu Thangam, S. Raja, A. Saleem Reddy, K. Lokeswar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman

ISBN: (纸本)9789819720880

Covid-19 is a transmissible infection identified in Wuhan, China, in December 2019 where more than million people were killed. When the contamination enters body, it contacts the mucous film that lies in your mouth, eyes, and nose. The virus entering a cell makes new disease parts making use of the cell and spreads infections by attacking nearest cells. Signs could begin one to fourteen days subsequent to presenting to the infection. Majority of them suffer with pneumonia, while a few suffer with extreme side effects like shortness of breath, headache, and fast heartbeat. Medical practitioners can see indications of respiratory infections on a chest X-ray. So, to detect if a person is suffering with Covid-19, the chest images help in the early analysis and the treatment of the patient. This study utilizes multiple datasets containing images of chest X-ray and makes use of preprocessing techniques like image augmentation, resizing, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and trained using Deep learning algorithms like CNN, VGG-16, VGG-19, and AlexNet. CNN model with a maximum pooling layer and a flatten layer outperformed other algorithms when trained on image augmentation and normalization images and achieved accuracy of 0.98. Finally, integrating the best suitable model with web applications like Flask helps to reduce the burden on doctors and minimize the human error in Covid-19 detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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On a class of nonlinear elliptic problems with double phase effects and laplacian-type operators in Musielak-Orlicz-Sobolev spaces

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Annali dell'Universita di Ferrara 2025年第2期71卷 1-20页

作者： Moujani, Hasna El Mfadel, Ali Kassidi, Abderrazak El Omari, M.’hamed Laboratory of Applied Mathematics and Scientific Computing Sultan Moulay Slimane University Beni Mellal Morocco Higher School of Technology Sultan Moulay Slimane University Khenifra Morocco

This paper addresses the existence of weak solutions for a class of nonlinear Dirichlet boundary value problems governed by a double phase operator. The main results are established under precise assumptions on the nonlinearity of the second term. The analysis is carried out within the advanced framework of Musielak-Orlicz-Sobolev spaces, which accommodate the variable growth conditions induced by the double phase structure. To handle challenges related to weak convergence, we employ the Young measures technique. Additionally, approximate solutions are systematically constructed through the Galerkin method, ensuring a rigorous and structured approach to the problem. © The Author(s) under exclusive license to Università degli Studi di Ferrara 2025.

关键词： (α,β)-Laplacian-like operator Fractional double phase problem Galerkin method Weak solution Young measure

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Adaptive Strategies for Scalable and Heterogeneous VANET Network 5

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Thangam, S. Jha, Priyanshu Hawari, Jawed Sah, Nabin Kumar Jain, Nikunj Jesy Janet Kumari, J.J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences It Department Shinas Oman

ISBN: (纸本)9798350385649

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud 5

QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-ba...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Jesy Janet Kumari, J. Thangam, S. Saleem Raja, A. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas It Department Oman

ISBN: (纸本)9798350385649

Due to the growing number of cloud users working on various cloud apps on specific infrastructure, resource allocation in cloud computing is implicitly difficult. Most resource allocation strategies currently in use focused on delivering efficiency determined by the workload of applications across many domains, such as business and scientific. Cloud service brokers compete strongly with one another to provide quality of service improvements based on the need, demand and the rapid growth of the services that are offered. Such conflict makes it difficult and complex to provide simple service selection and composition services in the cloud, this needs to be addressed to lighten the load on local resources. Because cloud-based services are too sophisticated and scalable, it is preferable to use an optimization strategy to choose the services that will meet the needs of the clients. To do this, a hybrid algorithm Modified Ant Colony System Cloud Services Composition (MACSCSC) is proposed to employ Ant Colony Optimization (ACO) incorporated with Genetic Algorithm (GA) to smoothly increase cloud-based services. When allocating cloud resources these meta-heuristic algorithms can achieve significantly better performance, lower costs and instances, better utilization of resources, and increased energy efficiency. The empirical findings on different real datasets have been demonstrated to show the improvements of the suggested method to overcome the drawbacks such as allocation of resources and high power consumption. © 2024 IEEE.

关键词： Quality of service

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Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization

Smart Traffic Management in Green VANETs: Ant-Inspired Routi...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Thangam, S. Jahnavi, M. Chandana, K. Dheeraj, K. Janet Kumari, J Jesy Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350309140

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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A roulette wheel-based pruning method to simplify cumbersome deep neural networks

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Neural computing and Applications 2024年第22期36卷 13915-13933页

作者： Chan, Kit Yan Yiu, Ka Fai Cedric Guo, Shan Jiang, Huimin School of Electrical Engineering Computing and Mathematics Sciences Curtin University Bentley Australia Department of Applied Maths The Hong Kong Polytechnic University Hong Kong School of Business Macau University of Science and Technology China

Deep neural networks (DNNs) have been applied in many pattern recognition or object detection applications. DNNs generally consist of millions or even billions of parameters. These demanding computational storage and requirements impede deployments of DNNs in resource-limited devices, such as mobile devices, micro-controllers. Simplification techniques such as pruning have commonly been used to slim DNN sizes. Pruning approaches generally quantify the importance of each component such as network weight. Weight values or weight gradients in training are commonly used as the importance metric. Small weights are pruned and large weights are kept. However, small weights are possible to be connected with significant weights which have impact to DNN outputs. DNN accuracy can be degraded significantly after the pruning process. This paper proposes a roulette wheel-like pruning algorithm, in order to simplify a trained DNN while keeping the DNN accuracy. The proposed algorithm generates a branch of pruned DNNs which are generated by a roulette wheel operator. Similar to the roulette wheel selection in genetic algorithms, small weights are more likely to be pruned but they can be kept;large weights are more likely to be kept but they can be pruned. The slimmest DNN with the best accuracy is selected from the branch. The performance of the proposed pruning algorithm is evaluated by two deterministic datasets and four non-deterministic datasets. Experimental results show that the proposed pruning algorithm generates simpler DNNs while DNN accuracy can be kept, compared to several existing pruning approaches. © The Author(s) 2024.

关键词： Deep neural networks

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