JAYALAKSHMI MURUGAN

@kalasalingam.ac.in

Associate Professor
kalasalingam academy of research and education

JAYALAKSHMI MURUGAN


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https://researchid.co/jayalacsmi

EDUCATION

B.E M.E Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications, Computer Engineering, Computer Vision and Pattern Recognition
36

Scopus Publications

379

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Enhanced MRI Segmentation and Severity Classification of Parkinson’s Disease Using Hierarchical Diffusion-driven Attention Model
    , Redhya M., M. Jayalakshmi, Rajermani Thinakaran
    International Journal of Intelligent Systems and Applications, 2026
    Early identification of Parkinson's disease (PD) from MRI remains challenging due to subtle structural alterations and the complexity of brain tissues. To address these challenges, this paper proposes a hierarchical framework termed Hierarchical Severity-Adaptive Diffusion Network, composed of three sequentially connected phases, where the output of each phase serves as input to the next for task-specific optimization. In the first phase, a graph diffusion-based convolutional network is employed to extract anatomical and structural features from multi-modal MRI data, enabling accurate segmentation of PD-relevant regions. Phase two introduces an edge-enhanced slice-aware recurrent network that incorporates Wiener filters and Sobel-based edge enhancement to reduce noise and partial volume effects while capturing structural continuity across adjacent MRI slices. Finally, for severity classification, non-linear severity-adaptive attention network is introduced, which emphasizes discriminative feature deterioration patterns across stages. This model uses Figshare PD dataset and demonstrates superior performance compared to established models like DenseNet121, VGG16, ResNet, MobileNet and Inception-V3, and achieves high accuracy (98.67), precision (0.99), recall (0.98), and F1 score (0.99), indicating its potential as an AI-assisted tool for PD severity assessment using MRI.
  • Adaptive Attention based Augmented LSTM-CatBoost Hybrid for Explainable and Edgeoptimized IoT Intrusion Detection
    C. Kalpana, M. Jayalakshmi
    Proceedings of 2nd International Conference on Multi Agent Systems for Collaborative Intelligence Icmsci 2026, 2026
    The Internet of Things (IoT) offers numerous benefits and opportunities in many aspects of our lives due to its wide variety of applications and popularity. However, the Internet of Things is susceptible to a number of illegal attacks and vulnerable assaults. Unbalanced data and limited resources are the major issues of the existing intrusion detection system. To overcome these issues, a novel framework called Adaptive Attention Based Augmented LSTM-CatBoost Hybrid for IoT intrusion detection is proposed. It incorporates adaptive attention to focus on the vital patterns of network traffic. The Class imbalance is handled by applying SMOTE. The classification accuracy is enhanced by utilize the CatBoost algorithm. To determine the significant features for prediction, SHAP is used in the proposed work. Our proposed method achieves 99.5% accuracy, 99.2 % precision, 99.2 % recall and 99.4 % F1 using the CICIoT2023 dataset. Edge quantization is employed in order to reduce the model size by 94.1 % and the latency by 86.2 %. This enables the efficient deployment on resource constraint devices.
  • AI-Driven Monitoring and Predictive Analysis of Indian Forest Ecosystems
    M. Jayalakshmi, S. Mahammad Sami, S K. Mahammad Vali, P. Vishnu Vardhan, P.L. Narasimha Chowdary
    Proceedings of the 2026 6th International Conference on Image Processing and Capsule Networks Icipcn 2026, 2026
    Forests are essential in stabilizing the eco-system, population of the earth, and fighting climate change. In India, due to the rapid deforestation, degradation of habitats and unsustainable land use, thus forest ecosystems have been in danger and this has given way to new methods of monitoring and managing forests. This study introduces a Forest Monitoring AI based Indian Forest Monitoring App that will add real-time surveillance, forecasting, and geospatial mapping to Indian forests. The application combines a variety of disparate data sources, such as satellite images, government forest surveys and climate records as well as wildlife census data, to produce actionable information to be used in conservation planning. Its core functionalities are monitoring rate of deforestation, assessment of forest health indicators, estimate of the population of wild life and assessment of conservation metrics. Predictive modules are based on advanced machine learning: a Random Forest Regressor model assesses the suitability of afforestation based on the soil fertility, rainfall, temperature, elevation, slope, vegetation cover, population density, and the distance to water sources; time-series forecasting is used to predict deforestation risks, using historical trends and patterns and lags; and climate impact analysis uses linear regression, time-series decomposition and correlation analysis to gauge environmental influences to forest health. This application has a dynamic dashboard constructed using Streamlit and complemented by Plotly and Folium to create the ability to join multidimensional data and render dashboards that are adaptive in view of data and geospatial visualization. Locally-specific models are trained on four regions (the Western Ghats, Eastern Ghats, Central India and Northeast India) to provide accurate, regional predictions.
  • A detailed review of the concepts, technologies, and industrial applications of Digital Twin
    J. Loyola Jasmine, M. Carmel Sobia, M. Jayalakshmi
    A Study on Next Generation Materials and Devicesv, 2025
  • Sign Language to Text Conversion using Random Forest
    M JayaLakshmi, Allu Pranathi Chowdari, Andra Gowthami, Akki Deepthika
    6th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2025 Proceedings, 2025
    In this study, a comprehensive approach for translating sign language into text using computer vision, machine learning, and real-time processing techniques is presented. The proposed system uses OpenCV for capturing the images and frames and Media Pipe is utilized for tracking hand movements and detection of landmarks. The landmarks extracted are processed and normalized then they are used as features for a Random Forest Classifier which is a machine learning algorithm well known for its classification accuracy. The process begins with capturing the images using the camera then the images are organizes into different categories representing numerous signs. By these images the hand landmarks are extracted and stored using Pickle for training process that happen later and data is being ready.In real-time scenario, the system collect the images, detect hand landmarks and classifies the images based on the landmarks.This research provide communication accessibility for the deaf and disabled community.
  • Medipath: An Intelligent Emergency Medical Service System with NLP and Real-Time Coordination
    M. Jayalakshmi, B S Vikash, Divya Dharshini M, Kishore Kumar R, N Kathirmathi
    2025 2nd Asia Pacific Conference on Innovation in Technology Apcit 2025, 2025
    This paper discusses the creation and assessment of MediPath, a smart web-based emergency medical assistance system. It connects patients, hospitals, and ambulance services in one digital platform. The system uses technologies like Natural Language Processing (NLP) to match hospital specialties with patient symptoms. It also uses real-time location services and smart route planning to address gaps in emergency medical response. MediPath has a three-part structure that supports logins for patients or attendants, hospital management portals, and navigation systems for ambulance drivers. The platform uses machine learning to link patient symptoms with hospital specialties, Firebase Realtime Database for easy data syncing, and mapping APIs for better traffic-aware routes. Performance tests show average response times of under 30 seconds for hospital matches and 95% accuracy in matching symptoms to specialties. There is also a significant drop in ambulance dispatch delays. User studies at different healthcare facilities indicate improved coordination, shorter emergency response times, and better use of resources. Although there are challenges with highly specialized medical conditions and connectivity in rural areas, MediPath greatly enhances emergency medical service delivery by creating a unified ecosystem that connects all parties in real time. This research presents a scalable, smart solution that addresses disconnected emergency medical services. It uses modern web technologies alongside healthcare knowledge to help save lives through quicker and more coordinated emergency responses.
  • HerbAI: A Deep Learning-based Model for Real-Time Identification and Documentation of Medicinal Plants
    M. Jayalakshmi, Chintha Vamsidhar Reddy, Kollapaneni Pranadeep, Badugu Avinash, Chinimilli Dhanush
    Proceedings of the 4th International Conference on Intelligent Computing Information and Control Systems Icoiics 2025, 2025
    Accurate identification of herbal plants is needed for biodiversity conservation, sustainable agriculture, and preservation of traditional medicinal knowledge. Due to lifestyles being followed by the current world, generations of today have lost contact with herbal medicines, and therefore natural resources remain unused. This paper suggests a hybrid herbal plant detection system that combines deep learning-based image classification with an NLP-based chatbot. The leaf-image classification module, trained from a set of randomly chosen leaves, seeds, and full-plant photos, relies on Convolutional Neural Networks (CNNs) and Vision Transformers to facilitate robust identification of diverse species. The chatbot module provides ease of convenience by providing real-time feedback, plant descriptions, and medical uses such that allowing non-experts can gain useful insights. Experimental verification demonstrates that the proposed system achieves 90 % accuracy for 80 herbal species compared to conventional models. This system not only assists in precise plant identification but also encourages the revival of herbal wisdom through online sources.
  • Empowering Speech-Impaired Individuals: A Hand Gesture to Text and Voice Conversion System
    M. Jayalakshmi, K. Hrithik, K. Pavan Kalyan Reddy, D. Raheem, D. Krishna Mohan
    6th International Conference on Innovative Trends in Information Technology Secure Trustworthy and Socially Responsible AI Icitiit 2025, 2025
    This paper introduces an innovative system for converting hand gestures into text and voice, aimed at assisting individuals with speech disabilities. Utilizing the power of Convolutional Neural Networks (CNN), our system achieves an exceptional recognition accuracy of over 99% for hand gestures corresponding to the 26 letters of the alphabet. The model is trained using a comprehensive dataset from Kaggle, ensuring accurate translation of gestures into readable text. To accommodate various user needs, we've incorporated a Graphical User Interface (GUI) built with key libraries such as OpenCV and Keras. This easy-to-use interface enables smooth user interaction and offers real-time word suggestions, making it easier to form sentences. Furthermore, to support users with visual impairments, we have added an option to listen to the generated sentence, promoting inclusivity. With its high precision, user-friendly design, and accessibility features, our Hand Gesture to Text and Voice Conversion System not only showcases the capabilities of CNN-based models but also emphasizes our dedication to creating accessible and inclusive technological solutions.
  • Vision Based Traffic Control System for Pedestrian Safety
    M. Jayalakshmi, N. Sai Sidhardha, M. Sumanth Reddy, N. Deepak, N. Lokesh
    Proceedings 2025 IEEE Delcon International Conference on Recent Smart Technologies in Engineering for Sustainable Development, 2025
    Contemporary cityscapes confront increasing issues in traffic rationalization and road safety due to constraints in traditional fixed-time signals and human-based enforcement. This study introduces a scalable Intelligent Traffic Management System based on state-of-the-art computer vision and deep learning the YOLOv8n model trained on real-world data for dynamic, real-time signal control as well as self-sustaining violation detection. The system utilizes video-based vehicle detection for object recognition of cars, pedestrians, traffic signs, and lights and fuses these with adaptive timing algorithms to minimize unnecessary delays and actively respond to changing road conditions. Stringent testing demonstrates that the YOLOv8n detection pipeline obtains 95.7 % traffic participant accuracy with a false positive rate of 4.3 %, while violation detection module attains 93 % accuracy and enables fast adaptation with an average response time of 0.42 seconds per frame. Real-world deployment demonstrates significant gains, accelerating traffic flow by up to 50 % and shortening vehicle wait times by over 70 % compared with fixed-schedule implementations. In addition, the solution has a module-based architecture that is open to digital signal controllers and urban observation, which can integrate easily with different infrastructure configurations for smart city deployment. Through adaptive signal, density control, violation recording, and centralized reporting, the system enables traffic authorities to make sound, fact-based decisions. This strategy significantly promotes urban mobility, pedestrian security, and enforcement clarity, setting a new benchmark in smart transportation infrastructure with the integration of deep learning, scalable deployment, and operational effectiveness.
  • Stacked hybrid model approach for crop yield prediction using machine learning techniques for improved accuracy
    Jayalakshmi Murugan, S. Sureshkumar, Lakshmi Narasimman P, Arunkumar S, J Jasin Joel Raj, Harish Balaji R
    International Conference on Computational Robotics Testing and Engineering Evaluation Iccrtee 2025, 2025
    Adequate predictions of crop yields serve as essential components for creating sustainable agricultural strategy while ensuring food stability. This research develops a predictive methodology which combines Random Forest with Bagging Regressor and Voting Regressor to achieve superior prediction results for crop yield forecasting. The dataset includes essential agronomic variables which comprise crop variety along with cultivated land area, production output, seasonal impacts, annual precipitation, fertilizer application and pesticide usage and final yield represents the dependent measurement. The combination of different ensemble learning methods provides the proposed system with improved predictive accuracy as it reduces overfitting errors. The proposed stacked model surpasses single regression systems since it provides more accurate predictions with lower prediction margins according to experimental data. Multiple ensemble learning techniques jointly operate for optimized feature interactions and prediction uncertainty minimization in this research. Farming professionals together with policymakers become able to utilize data-driven techniques thanks to this model which provides them insight for better agricultural resource distribution and output enhancement.
  • Quantum Disturbance-based Photon Cloning Attack
    Hui-Kai Su, K.MahaRajan, Sanmugasundaram R, M.Jayalakshmi, A. Santhosh Nantha, Wen-Kai Kuo
    Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications Icimia 2025, 2025
  • Analysis of network performance using markov autoregressive and arimax models using optimization
    B. Jothi, Jeyasudha J., Sujatha M., Jayalakshmi M., Nagendar Yamsani, Aarthi C.
    Emerging Advancements in AI and Big Data Technologies in Business and Society, 2024
  • Revolutionizing precision agriculture using artificial intelligence and machine learning
    Jayalakshmi Murugan, Maharajan Kaliyanandi, Carmel Sobia M.
    Data Science for Agricultural Innovation and Productivity, 2024
  • An Ensembled Grid based Machine Learning Approach For PD Classification From MRI Images
    Redhya M, M. Jayalakshmi
    2024 4th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2024, 2024
  • Bio-Inspired Metaheuristic Feature Fusionmethod for Multi-Biometric Identification
    Vijay M, Vinoth Raj R, M Jayalakshmi, G Shanmugaraj
    2024 11th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2024, 2024
  • Human Walking Computational Models using Reinforcement Learning
    Jayalakshmi Murugan, K. Maharajan, M Vijay, S.Chitra Selvi
    2024 5th International Conference on Innovative Trends in Information Technology Icitiit 2024, 2024
  • Multi-model Human-Computer Interaction System with Hand Gesture and Eye Gesture Control
    M. Jayalakshmi, T. Pardha Saradhi, Syed Mohammed Rahil Azam, Sk. Fazil, S. Durga Sai Sriram
    2024 5th International Conference on Innovative Trends in Information Technology Icitiit 2024, 2024
  • Density Based Traffic Management System
    Amaranatha Sasthry S, Shyam Sundar R, Sri Ganesh M, Anand Kumar R, Jayalakshmi M
    2024 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2024, 2024
  • Empowering Farmers: A Real-Time Bidding System with Integrated Machine Learning
    Jayalakshmi Murugan, Kotte Sai Vamsi, K Dheeraj Datta Reddy, K Pavan Chand, K Mohamad Feroz
    5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024
  • Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance
    Kalaipriyan Thirugnanasambandam, Jayalakshmi Murugan, Rajakumar Ramalingam, Mamoon Rashid, R. S. Raghav, Tai-hoon Kim, Gabriel Avelino Sampedro, Mideth Abisado
    Peerj Computer Science, 2024
  • Design and development of novel security approach designed for cloud computing with load balancing
    Maharajan Kaliyanandi, Jayalakshmi Murugan, Senthil Kumar Subburaj, Saritha Ganesan, Visalaxi Gandhimathinathan
    Aip Conference Proceedings, 2023
  • Dependable and Non-Dependable Multi-Authentication Access Constraints to Regulate Third-Party Libraries and Plug-Ins across Platforms
    Santosh Kumar Henge, Gnaniyan Uma Maheswari, Rajakumar Ramalingam, Sultan S. Alshamrani, Mamoon Rashid, Jayalakshmi Murugan
    Systems, 2023
  • Cervical Cancer Screening Approach Using AI
    D. Santhi, M. Carmel Sobia, M. Jayalakshmi
    Applied Artificial Intelligence A Biomedical Perspective, 2023
  • Navigating College Campuses with Virtual Assistants
    Jayalakshmi Murugan, K Sai Vamsi, K.Dheeraj Datta Reddy, K.Pavan Chand, K.Mohammed Feroz
    2023 International Conference on the Confluence of Advancements in Robotics Vision and Interdisciplinary Technology Management IC Rvitm 2023, 2023
  • Elliptic Seizure Detection on EEG Signals Using Bidirectional Long Short-Term Memory Model
    Shreya Patchala, Bura Vijay Kumar, Kotha Chandrakala, Abbas Hameed Abdul Hussein, M. Jayalakshmi
    IEEE 1st International Conference on Ambient Intelligence Knowledge Informatics and Industrial Electronics Aikiie 2023, 2023
  • Air quality prediction using remote sensing
    R. Raja Selvi, M. Shruthi, G. Nithya, S. Kalaiselvi, M. Jayalakshmi, V. Gomathi
    Aip Conference Proceedings, 2022
  • Deep Learning for Phishing Website Detection
    Ksn Sushma, M. Jayalakshmi, Tapas Guha
    Mysurucon 2022 2022 IEEE 2nd Mysore Sub Section International Conference, 2022
  • A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks
    R. Muthukkumar, Lalit Garg, K. Maharajan, M. Jayalakshmi, Nz Jhanjhi, S. Parthiban, G. Saritha
    Peerj Computer Science, 2022
  • Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
    N.B. Prakash, M. Murugappan, G.R. Hemalakshmi, M. Jayalakshmi, Mufti Mahmud
    Sustainable Cities and Society, 2021
  • Novel Deep-Learning Approaches for Future Computing Applications and Services
    M. Jayalakshmi, K. Maharajan, K. Jayakumar, G. Visalaxi
    Simulation and Analysis of Mathematical Methods in Real Time Engineering Applications, 2021
  • Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients
    M. Jayalakshmi, Lalit Garg, K. Maharajan, K. Jayakumar, Kathiravan Srinivasan, Ali Kashif Bashir, K. Ramesh
    Computers Materials and Continua, 2021
  • Sensor-Cloud based Precision Agriculture Approach for Intelligent Water Management
    M. Jayalakshmi, V. Gomathi
    International Journal of Plant Production, 2020
  • Pervasive health monitoring through video-based activity information integrated with sensor-cloud oriented context-aware decision support system
    M. Jayalakshmi, V. Gomathi
    Multimedia Tools and Applications, 2020
  • Analysis of Precision Agriculture based on Random Forest Algorithm by using Sensor Networks
    K. Pavithra, M. Jayalakshmi
    Proceedings of the 5th International Conference on Inventive Computation Technologies Icict 2020, 2020
  • Secure communication between wireless medical sensor networks and data servers using paillier and elgamal key cryptosystem
    A. Vasukidevi, M. Jayalakshmi, V. Gomathi
    2016 International Conference on Computing Technologies and Intelligent Data Engineering Icctide 2016, 2016
  • An enhanced underground pipeline water leakage monitoring and detection system using Wireless sensor network
    M. JayaLakshmi, V. Gomathi
    Proceedings of the IEEE International Conference on Soft Computing and Network Security Icsns 2015, 2015

RECENT SCHOLAR PUBLICATIONS

  • Enhanced MRI Segmentation and Severity Classification of Parkinson’s Disease Using Hierarchical Diffusion-Driven Attention Model
    RT Redhya M,M.Jayalakshmi
    Intelligent Systems and Applications 18 (1), 119-131 , 2026
    2026
  • A detailed review of the concepts, technologies, and industrial applications of Digital Twin
    JL Jasmine, MC Sobia, M Jayalakshmi
    A Study on Next-Generation Materials and Devices, 54-59 , 2025
    2025
  • Quantum Disturbance-based Photon Cloning Attack
    HK Su, K MahaRajan, M Jayalakshmi, AS Nantha, WK Kuo
    2025 4th International Conference on Innovative Mechanisms for Industry … , 2025
    2025
  • Empowering Farmers: A Real-Time Bidding System with Integrated Machine Learning
    J Murugan, KS Vamsi, KDD Reddy, KP Chand, KM Feroz
    2024 International Conference on Sustainable Communication Networks and … , 2024
    2024
  • Human Walking Computational Models using Reinforcement Learning
    J Murugan, K Maharajan, M Vijay, SC Selvi
    2024 5th International Conference on Innovative Trends in Information … , 2024
    2024
  • Revolutionizing Precision Agriculture Using Artificial Intelligence and Machine Learning
    J Murugan, M Kaliyanandi
    Data Science for Agricultural Innovation and Productivity, 110-126 , 2024
    2024
    Citations: 7
  • Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance
    K Thirugnanasambandam, J Murugan, R Ramalingam, M Rashid, ...
    PeerJ Computer Science 10, e1816 , 2024
    2024
    Citations: 5
  • Navigating college campuses with virtual assistants
    J Murugan, KS Vamsi, KDD Reddy, KP Chand, KM Feroz
    2023 International Conference on the Confluence of Advancements in Robotics … , 2023
    2023
    Citations: 4
  • Cervical cancer screening approach using AI
    D Santhi, MC Sobia, M Jayalakshmi
    Applied Artificial Intelligence, 121-132 , 2023
    2023
    Citations: 1
  • Design and development of novel security approach designed for cloud computing with load balancing
    M Kaliyanandi, J Murugan, SK Subburaj, S Ganesan, ...
    CONFERENCE PROCEEDINGS ON 3RD INTERNATIONAL CONFERENCE ON ENGINEERING … , 2023
    2023
    Citations: 12
  • Dependable and Non-Dependable Multi-Authentication Access Constraints to Regulate Third-Party Libraries and Plug-Ins across Platforms
    SK Henge, GU Maheswari, R Ramalingam, SS Alshamrani, M Rashid, ...
    Systems 11 (5), 262 , 2023
    2023
    Citations: 5
  • Dependable and Non-Dependable Multi-Authentication Access Constraints to Regulate Third-Party Libraries and Plug-Ins across Platforms. Systems 2023, 11, 262
    SK Henge, GU Maheswari, R Ramalingam, SS Alshamrani, M Rashid, ...
    2023
  • Deep learning for phishing website detection
    KSN Sushma, M Jayalakshmi, T Guha
    2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-6 , 2022
    2022
    Citations: 18
  • A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks
    R Muthukkumar, L Garg, K Maharajan, M Jayalakshmi, N Jhanjhi, ...
    PeerJ Computer Science 8, e1029 , 2022
    2022
    Citations: 35
  • Air quality prediction using remote sensing
    RR Selvi, M Shruthi, G Nithya, S Kalaiselvi, M Jayalakshmi, V Gomathi
    AIP Conference Proceedings 2444 (1), 040005 , 2022
    2022
  • Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
    NB Prakash, M Murugappan, GR Hemalakshmi, M Jayalakshmi, ...
    Sustainable Cities and Society 75, 103252 , 2021
    2021
    Citations: 91
  • Novel Deep‐Learning Approaches for Future Computing Applications and Services
    M Jayalakshmi, K Maharajan, K Jayakumar, G Visalaxi
    Simulation and Analysis of Mathematical Methods in Real‐Time Engineering … , 2021
    2021
    Citations: 1
  • Fuzzy logic-based health monitoring system for COVID’19 patients
    M Jayalakshmi, L Garg, K Maharajan, K Jayakumar, K Srinivasan, ...
    Tech Science Press , 2021
    2021
    Citations: 61
  • Quantum Inspired Membrane Computing to Enhance Security in Cloud Network
    K Maharajan, S Thangam, G Visalaxi, A Shenbagarajan, M Jayalakshmi
    Solid State Technology 63 (6), 14735-14751 , 2020
    2020
    Citations: 1
  • Sensor-cloud based precision agriculture approach for intelligent water management
    M Jayalakshmi, V Gomathi
    International Journal of Plant Production 14 (2), 177-186 , 2020
    2020
    Citations: 39

MOST CITED SCHOLAR PUBLICATIONS

  • Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation
    NB Prakash, M Murugappan, GR Hemalakshmi, M Jayalakshmi, ...
    Sustainable Cities and Society 75, 103252 , 2021
    2021
    Citations: 91
  • Fuzzy logic-based health monitoring system for COVID’19 patients
    M Jayalakshmi, L Garg, K Maharajan, K Jayakumar, K Srinivasan, ...
    Tech Science Press , 2021
    2021
    Citations: 61
  • An enhanced underground pipeline water leakage monitoring and detection system using wireless sensor network
    M JayaLakshmi, V Gomathi
    2015 International Conference on Soft-Computing and Networks Security (ICSNS … , 2015
    2015
    Citations: 47
  • Sensor-cloud based precision agriculture approach for intelligent water management
    M Jayalakshmi, V Gomathi
    International Journal of Plant Production 14 (2), 177-186 , 2020
    2020
    Citations: 39
  • A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks
    R Muthukkumar, L Garg, K Maharajan, M Jayalakshmi, N Jhanjhi, ...
    PeerJ Computer Science 8, e1029 , 2022
    2022
    Citations: 35
  • Pervasive health monitoring through video-based activity information integrated with sensor-cloud oriented context-aware decision support system
    M Jayalakshmi, V Gomathi
    Multimedia Tools and Applications , 2018
    2018
    Citations: 24
  • Deep learning for phishing website detection
    KSN Sushma, M Jayalakshmi, T Guha
    2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-6 , 2022
    2022
    Citations: 18
  • Analysis of precision agriculture based on random forest algorithm by using sensor networks
    K Pavithra, M Jayalakshmi
    2020 international conference on inventive computation technologies (ICICT … , 2020
    2020
    Citations: 14
  • Design and development of novel security approach designed for cloud computing with load balancing
    M Kaliyanandi, J Murugan, SK Subburaj, S Ganesan, ...
    CONFERENCE PROCEEDINGS ON 3RD INTERNATIONAL CONFERENCE ON ENGINEERING … , 2023
    2023
    Citations: 12
  • A study on water leakage detection in buried plastic pipes using wireless sensor networks
    AR Iyeswariya, RM Shamila, M JayaLakshm, K Maharajan, V Sivakumar
    Int. J. Sci. Eng. Res 3 (1) , 2012
    2012
    Citations: 8
  • Revolutionizing Precision Agriculture Using Artificial Intelligence and Machine Learning
    J Murugan, M Kaliyanandi
    Data Science for Agricultural Innovation and Productivity, 110-126 , 2024
    2024
    Citations: 7
  • Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance
    K Thirugnanasambandam, J Murugan, R Ramalingam, M Rashid, ...
    PeerJ Computer Science 10, e1816 , 2024
    2024
    Citations: 5
  • Dependable and Non-Dependable Multi-Authentication Access Constraints to Regulate Third-Party Libraries and Plug-Ins across Platforms
    SK Henge, GU Maheswari, R Ramalingam, SS Alshamrani, M Rashid, ...
    Systems 11 (5), 262 , 2023
    2023
    Citations: 5
  • Navigating college campuses with virtual assistants
    J Murugan, KS Vamsi, KDD Reddy, KP Chand, KM Feroz
    2023 International Conference on the Confluence of Advancements in Robotics … , 2023
    2023
    Citations: 4
  • Secure communication between wireless medical sensor networks and data servers using Paillier and ElGamal key cryptosystem
    A Vasukidevi, M Jayalakshmi, V Gomathi
    2016 International Conference on Computing Technologies and Intelligent Data … , 2016
    2016
    Citations: 4
  • Instantaneous emotion detection system using vocalizations
    M JayaLakshmi, K Maharajan, B Paramasivan
    IOSR Journal of Engineering (IOSRJEN), July , 2012
    2012
    Citations: 2
  • Cervical cancer screening approach using AI
    D Santhi, MC Sobia, M Jayalakshmi
    Applied Artificial Intelligence, 121-132 , 2023
    2023
    Citations: 1
  • Novel Deep‐Learning Approaches for Future Computing Applications and Services
    M Jayalakshmi, K Maharajan, K Jayakumar, G Visalaxi
    Simulation and Analysis of Mathematical Methods in Real‐Time Engineering … , 2021
    2021
    Citations: 1
  • Quantum Inspired Membrane Computing to Enhance Security in Cloud Network
    K Maharajan, S Thangam, G Visalaxi, A Shenbagarajan, M Jayalakshmi
    Solid State Technology 63 (6), 14735-14751 , 2020
    2020
    Citations: 1
  • Enhanced MRI Segmentation and Severity Classification of Parkinson’s Disease Using Hierarchical Diffusion-Driven Attention Model
    RT Redhya M,M.Jayalakshmi
    Intelligent Systems and Applications 18 (1), 119-131 , 2026
    2026