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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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MediGlove: Integrating IoT and Machine Learning for Personalized Remote Health Care 2

MediGlove: Integrating IoT and Machine Learning for Personal...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Nanda, K.V. Keerthan Kumar, T.G. Achyutha Prasad, N. Raviprakash, M.L. Anand Babu, J. Naveen Kumar, M.R. Sri Siddhartha Institute Of Technology Master Of Computer Applications Tumkur India Siddaganga Institute Of Technology Information Science And Engineering Tumkur India East West Institute Of Technology Computer Science And Engineering Bangalore India Kalpataru Institute Of Technology Artificial Intelligence And Machine Learning Tiptur India Malnad College Of Engineering Information Science And Engineering Hassan India Vidyavardhaka College Of Engineering Information Science And Engineering Mysore India

ISBN: (纸本)9798350381665

Mobile operating systems have advanced significantly in recent decades, with Android emerging as a widely used open-source platform. Simultaneously, the Internet of Things (IoT) has transformed computer equipment by enabling seamless data transfer over networks without human involvement. This study makes use of intelligent gloves equipped with various sensors, as well as IoT and Android applications, to improve healthcare. In this work, we introduce a pioneering framework that combines IoT and Android technology to revolutionize remote health monitoring called IoT-enabled Smart Gloves for Remote Health Monitoring using an Android-based solution approach (MediGlove). MediGlove uses intelligent smart gloves with robust sensors to continuously collect crucial health data. The solution works with IoT devices to send data quickly to a cloud-based server. The cloud server provides real-time health metric analysis with great precision by utilizing cutting-edge algorithms and machine learning techniques. A simple Android software acts as a dynamic health dashboard, allowing users to track their health in real-time. The framework generates real-time alerts for problematic health markers, allowing prompt medical action. Extensive testing validates the system's efficacy and dependability in health monitoring and the seamless integration of smart gloves with Android applications. © 2024 IEEE.

关键词： Internet of things

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Enhancing Customer Experience in Retail for Adaptive Store Layouts with Reinforcement Learning

Enhancing Customer Experience in Retail for Adaptive Store L...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Suresh, P. Sivakumar, T. Revathy, N. Pandi, M. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Hindustan College of Arts and Science Department of Master of Computer Applications Tamil Nadu Coimbatore India Srm Madurai College for Engineering and Technology Department of Information Technology Tamil Nadu Sivagangai India

ISBN: (纸本)9798331521394

In the competitive retail industry, improving customer experience is essential for increasing sales and cultivating brand loyalty. A crucial determinant of consumer happiness is the shop layout, which impacts customers' navigation, exploration, and interaction with merchandise. Traditional store layouts are often inflexible and do not adjust to evolving customer behaviour, hence diminishing their efficacy in enhancing the shopping experience. This paper examines the use of Reinforcement Learning (RL) to create adaptable shop layouts that adjust based on real-time consumer interactions. Through the analysis of customer behaviour and feedback, RL algorithms may dynamically modify the layout of merchandise, aisles, and promotional areas, therefore optimizing the retail environment for both consumers and merchants. The RL algorithm analyses historical data, progressively optimizing store layouts to correspond with consumer preferences, so improving the entire shopping experience. It provides substantial advantages, such as enhanced consumer involvement, more operational efficiency, and elevated revenue designs. It examines the potential of RL in revolutionizing retail settings in optimizing adaptable shop layouts and its effects on consumer happiness and corporate performance. © 2025 IEEE.

关键词： User experience

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Investigating Domain Adaptation for Medical Image Classification 5th

Investigating Domain Adaptation for Medical Image Classifica...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Ankita Seth, Jyoti Sharma, Ambrish Kumar Karthikeyan, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a critical trouble in scientific imaging obligations, consisting of most cancers prognosis, because it lets in for the switch of understanding throughout particular medical imaging modalities, obligations, and affected person cohorts. It can help address record distribution shifts and make undertaking medical photo classes more dependable and accurate. In this examination, we look into several area model techniques and benchmark their performance in the venture of the medical photo category. We examine supervised and unsupervised methods, as well as deep studying-based methods. The results of our experiments offer insights into the performance of numerous area model techniques for clinical picture class that could assist in deciding the pleasant method to use in any given medical imaging task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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OHDLL:Optimized Hybrid Deep Learning Model for Classification of Leukaemia images

OHDLL:Optimized Hybrid Deep Learning Model for Classificatio...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Saju, Binju Asha, V. Tressa, Neethu Suhas, R.A. Jagesh, Shah Yash Sandhya, V. Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306088

A serious haematological cancer that causes mortality and morbidity in people of all ages is leukaemia. The haematologists examine bone marrow and blood to determine Leukaemia. Techniques for manual blood testing that have been in use for a long period are frequently slow and produce less reliable diagnoses. The study proposes a hybrid model to classify leukaemia cell images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an F1 score of 98.98%, an accuracy of 98.20%, sensitivity of 98.9%, specificity of 98.1%, and precision of 98%. © 2023 IEEE.

关键词： Median filters

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Blockchain Based Verification of Educational and Professional Certificates 2

Blockchain Based Verification of Educational and Professiona...

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2nd International Conference on Computational Systems and Communication, ICCSC 2023

作者： Singh, Anjali Chauhan, S.P.S. Goel, Amit Kumar Master of Technology in Computer Science and Engineering School of Computing Science and Engineering Galgotias University Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781665493932

Educational Documents and Certificates are a proof of professional achievements for anyone. Without these documents, it is not possible to start your career and hence play a very important role in everyone's professional life. Till date, Colleges, Universities and various educational institutes issue paper based degrees and certificates which are prone to get damaged or may be lost. The other drawback is that this process of issuing of degrees and manual process of verification of those documents by third parties is a time taking and cumbersome process. The employers take a lot of time for verifying these paper based degrees and certificates before they give the job offer. Forgery of paper based documents is also easier and has led to many educational scams. This paper aims to give the solution to the problems mentioned above through Blockchain Implementation. Block chain technology is not only limited to crypto currencies but is also of great applications in various fields like health, supply chain management, finance, etc. It has disrupted the traditional education system. This paper highlights as to how the educational certificates can be verified using the Ethereum platform and smart contracts. The traditional paper certificates would be converted to digital certificates on student's request, their hash value would be calculated using cryptographic hash functions and stored on Blockchain. A unique certificate ID and transaction hash value would be generated which would then be used to verify the certificates through a common platform. © 2023 IEEE.

关键词： Smart contract

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Securing Comparative Study of Leaf Disease Identification with Different Deep Learning Models Using Hyperledger Fabric 4

Securing Comparative Study of Leaf Disease Identification wi...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Das, Subhashis Banerjee, Soumendu Banerjee, Projat Das, Nabanita Computer Science and Engineering Academy of Technology West Bengal India Master of Computer Applications Academy of Technology West Bengal India Blockchain Researcher and Developer Ibm India Private Limited Greater Noida India

ISBN: (纸本)9798350389128

Plant Disease is a serious problem that needs our utmost attention. There are different techniques to detect plant diseases. Our focus is the analysis of various models to detect leaf diseases from images. Such analysis will help in choosing an appropriate model for such tasks which can be incorporated in handheld devices through an app which can be a frugal way to detect leaf diseases and appropriate measures can be taken. © 2024 IEEE.

关键词： Contrastive Learning

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Design of Fuzzy Logic Control Framework for QoS Routing in MANET

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Intelligent Automation & Soft Computing 2023年第3期35卷 3479-3499页

作者： M.Vargheese S.Vanithamani D.Stalin David Ganga Rama Koteswara Rao Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Master of Computer Applications M.Kumarasamy College of EngineeringKarur639113Tamil NaduIndia Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062Tamil NaduIndia Department of Computer Science and Information Technology Koneru Lakshmaiah Education FoundationVaddeswaram522502Andhra PradeshIndia

Wireless networks with no infrastructure arise as a result of multiple wireless devices working *** Mobile Ad hoc Network(MANET)is a system for connecting independently located Mobile Nodes(MNs)via wireless links.A MANET is self-conﬁguring in telecommunications,while MN produces non-infrastructure networks that are entirely *** the MAC and routing layers of MANETs take into account issues related to Quality of Service(QoS).When culling a line of optical discernment communication,MANET can be an effective and cost-saving route cull *** maintain QoS,however,more or fewer challenges must be *** paper proposes a Fuzzy Logic Control(FLC)methodology for specifying a probabilistic QoS guaranteed for *** framework uses network node mobility to establish the probabil-istic quality of *** Logic(FL)implementations were added to Network Simulator-3(NS-3)and used with the proposed FLC framework for *** have found that for a given node’s mobility,the path’s bandwidth decreases with time,hop count,and *** is resolutely based on this fuzzy rule that the priority index for a packet is ***,by avoiding sending pack-ets(PKT)out of source networks when there are no beneﬁcial routes,bandwidth is not *** FLC outperforms the scheduling methods with a wide range of *** improve QoS within MANETs,it is therefore recommended that FLC is used to synchronize ***,using these performance metrics,the QoS-responsible routing can opt for more stable *** on network simulation,it is evident that incorporating QoS into routing protocols is meant to improve traf-ﬁc performance,in particular authentic-time trafﬁc.

关键词： MANET quality of service fuzzy logic mobile node network trafﬁc

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Mental Health Predictive Analysis using Multiple Machine Learning Techniques 2

Mental Health Predictive Analysis using Multiple Machine Lea...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Tressa, Neethu Asha, V. Prasad, Arpana Prathith Pramodh Kumar, D. Saju, Binju New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India

ISBN: (纸本)9798350369960

The growth in mental health issues and the demand for high-quality medical care have prompted the requirement of machine learning techniques in the psychological research area. For the better prediction of mental health complications, this work presents an up-to-date thorough evaluation of machinelearning algorithms. This article reviews the difficulties, limitations and future directions of using machine learning approaches in psychological health. By searching reliable databases, relevant studies and publications on machine learning methods were collected for predicting mental health related issues. For the completion of this work, 40 research outcomes were analyzed using screening and classification procedures. The analyzed works were categorized according to mental health conditions such as schizophrenia, bipolar disorder, depression or anxiety, PTSD, and mental health conditions affecting children. While discussing the results, the difficulties and limitations that many researchers have encountered when studying mental health related problems were also considered. The paper also encompasses specific directions for future research work that are feasible and the need for utilizing varying approaches in machine learning for psychological health disorder predictions. © 2023 IEEE.

关键词： Decision Tree Classifier Flask Deployment K Nearest Neighbor Classifier Logistic Regression Algorithm Naïve Bayes Random Forest Stacking

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Leveraging Machine Learning for Pattern-Based Detection of Respiratory Illnesses

Leveraging Machine Learning for Pattern-Based Detection of R...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Shetty, Anusha Prashanth Deepthi, L. Puthran, Raksha Shetty, Surendra Hegde, Pavan Kumar, Shravan NMAM Insitute of Technology Department of Master of Computer Applications Karkala Nitte India Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangalore India Father Muller Medical College and Hospital Department of Paediatrics Mangalore India

ISBN: (纸本)9798331527518

Lung sound analysis presents a promising, noninvasive, and potentially cost-effective approach for detecting respiratory diseases. This research aims to assess the feasibility of using lung sounds for pattern recognition and detection of various respiratory disorders, specifically by conducting an analysis of cough sounds. We review a range of recent studies that explore the use of deep learning models, machine learning algorithms, and feature extraction techniques in this context. These studies are categorized based on their methodologies, and their performance in accurately classifying lung sounds is discussed. Research findings highlight the significant diagnostic potential of lung sound analysis, demonstrating improved classification accuracies across various disease types. However, challenges such as data inconsistency, noise interference, and limited model generalizability remain. It captures the current state of lung sound analysis research in respiratory disease detection, offering insights that could drive future advancements in this critical field. © 2025 IEEE.

关键词： Lung cancer

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