Balajee Jeyakumar

@mtieat.org

HoD & Associate Professor
Mother Theresa Institute of Engineering and Technology

Balajee Jeyakumar


                  Download Compact PDF  
https://researchid.co/jbbala04
Dr. Balajee Jeyakumar is currently serving as Associate Professor and Head of the Department of CSE (Artificial Intelligence) at Mother Theresa Institute of Engineering and Technology, Palamaner, Andhra Pradesh, India. He completed his Undergraduate degree from University of Madras and his Postgraduate degree from Vellore Institute of Technology,Vellore where he was also awarded his Ph.D. He has published more than 40 research papers in reputed Scopus and SCI-indexed journals and holds over 10 national and international patents. His research interests include Machine Learning, Deep Learning, Internet of Things (IoT), and Big Data Analytics. He actively guides UG and PG students in various academic and research projects, fostering innovation and research excellence in Artificial Intelligence and emerging technologies.

EDUCATION

MCA ., MBA., PhD

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Information Systems, Computer Science
46

Scopus Publications

726

Scholar Citations

13

Scholar h-index

20

Scholar i10-index

Scopus Publications

  • Intelligent Interventions: Practical Applications of Machine Learning for DataDriven Decision-Making in Healthcare
    J. Balajee, K. Kathiravan, G. Sangar
    Machine Learning in Healthcare Data Driven Decisions Predictive Modelling Personalized Medicine, 2026
  • Securing the Future: AI-Powered Weapon Systems, Ethics, and Adversarial Defense
    J. Balajee, K.V. Ravikumar, G. Sangar, Kaavya Kanagaraj
    Robotics in Weaponry Using Machine Learning and Engineering, 2026
    The fast deployment of Artificial Intelligence (AI) in present day weapon systems has revolutionized warfare, introducing unheard of autonomy, velocity, and adaptability. However, this progress has also ushered in new cybersecurity dangers that threaten the protection and reliability of autonomous weapon platforms. This bankruptcy addresses the dual project of making sure cybersecurity and enhancing robustness in AI-driven weapon systems, especially in excessive-stakes and opposed environments. We explore the structure and vulnerabilities of AI-integrated combat platforms, together with self-sufficient drones, robot arms, and surveillance structures. By integrating concepts from hostile system mastering, explainable AI (XAI), zero-believe structure, and Blockchain-more advantageous logging, we endorse a protection-aware AI lifecycle for stable weapon deployment. Real-international case studies, including spoofing attacks on UAVs and poisoning of laptop imaginative and prescient classifiers, spotlight the urgent need for resilient AI frameworks. This bankruptcy serves as a complete resource for protection researchers, policy-makers, and engineers, aiming to construct straightforward and fail-secure AI-pushed weapon systems.
  • Adaptive Deep Defense: Reinforcing Network Intrusion Detection with Hybrid Transformer-LSTM Models
    S Sathesh Kumar, Balajee J, Jayapal Lande, Ashok Bhansali, Babitha Lincy R, Rayappan Lotus
    2026 International Conference on ICT and Photonics Ictp 2026 Advancing ICT Photonics for A Smarter Sustainable World Proceedings, 2026
    In the current digital infrastructures, the rapid evolution of cyber threats such as low-rate, polymorphic and adversarial attacks have made conventional network intrusion detection systems (NIDS) more and more unsuitable. These traditional approaches that are usually based on shallow learning or static rule-based models cannot adapt to dynamical network patterns. To overcome this limitation, the present study suggests an Adaptive Deep Defense, a novel hybrid deep learning(DL) framework to use Transformer-Long short Term Memory (LSTM) architectures with adaptive adversarial defense mechanisms for robust and intelligent intrusion detection. The global contextual relationships that exist among the network flow features are captured by the Transformer encoder and sequential dependencies are refined by the Bidirectional LSTM(BiLSTM) component to get effective temporal modeling. Experimental evaluations on the CICIDS2017 benchmark dataset resulted in 99.12% accuracy, 99.1% F1-score, and 0.997 AUC to surpass 5 state-of-the-art baselines, CNN-LSTM, and GNN-based models. The obtained results show the high accuracy, robustness, and flexibility of the DL learning with adaptive mechanisms offers a future-ready, reliable, and scalable security defense mechanism for the intelligent network security applications.
  • Load-Aware Fog-Based Workflow Scheduling for Reliable IIOT Connectivity in Industry 4.0
    Sudhir Anakal, K. Shravani, J. Balajee, Harish Kunder, M. V. H. Bhaskara Murthy, Ravi Kumar Tata, S. Murugesan
    IEEE Communications Standards Magazine, 2026
  • 6G-Enabled Edge-Enhanced Functional Chain Scheduling for Intelligent Medical Emergency Communication in Smart Healthcare
    Haewon Byeon, Desidi Narsimha Reddy, Divya Nimma, J. Balajee, G. Siva Nageswara Rao, Aseel Smerat, Mukesh Soni
    IEEE Communications Standards Magazine, 2026
    With the advent of 6G and edge intelligence, medical emergency systems require ultra-reliable, low-latency, and adaptive communication to support real-time diagnosis and collaborative treatment. This study proposes a 6G-enabled edge-enhanced functional chain scheduling framework for intelligent medical emergency assistance in smart healthcare networks. Medical services are classified and prioritized into four categories based on urgency and service region, and corresponding priority weights are assigned. A next-generation health information network architecture integrating 6G communication, edge computing, Software Defined Networking (SDN), and Network Function Virtualization (NFV) is designed to ensure dynamic and context-aware resource orchestration. A service function chain (SFC) scheduling model is formulated with the objective of minimizing the total weighted completion time, representing the latency–priority tradeoff across diverse healthcare demands. To optimize scheduling, a matching game algorithm is developed for small-scale edge scenarios, while a Q-learning reinforcement learning algorithm is designed for large-scale distributed networks. Simulation experiments demonstrate that the proposed hybrid model effectively balances computational load and network latency, ensuring prioritized service delivery for critical medical emergencies. This research provides a scalable and intelligent foundation for 6G edge-assisted healthcare communication, enabling seamless collaboration among hospitals, ambulances, and remote medical units.
  • Secure AI-Driven Framework for Predicting Drug Toxicity Using Computational Modeling
    Ravi Kumar, Balajee J, Jayapal Lande, Hitendra Garg, Tamilarasi M, Pothuraju Rajarajeswari, S. Siva Shankar
    2026 International Conference on ICT and Photonics Ictp 2026 Advancing ICT Photonics for A Smarter Sustainable World Proceedings, 2026
    Drug toxicity is one of the biggest hurdles in pharmaceutical research and the cause of costly late-stage drug failures as well as safety concerns. Despite improvements in computational toxicology, current AI models are constrained by data privacy issues, the inability to interpret AI systems, and potential adversarial manipulation of AI systems. To overcome these issues, this paper proposes a framework that can address the security challenges simultaneously through a unified pipeline for predicting drug toxicity using computational modeling, which incorporates federated learning, differential privacy, and adversarial robustness. Public datasets such as Tox21 were used for the evaluation of the model including standardized molecular representations, multi-modal feature extraction (graph, fingerprint, descriptor, and sequence embeddings), and a hybrid Message Passing Neural Network-Transformer architecture. Secure aggregation and privacy preserving mechanisms were used to ensure conformity in data protection standards, without jeopardizing accuracy. Experimental results showed that the AUROC score is 86.1% and AUPRC is 43.6%, which is better than state-of-the-art baselines. The proposed framework guarantees robust, interpretable, and privacy-preserving toxicity prediction, which can provide valid grounds for safe and secure AI deployment in computational drug discovery.
  • Isolation Forest-Driven Anomaly Detection Framework for UAV-Based Power Line Inspection in the Cloud
    P. Kaushik, Balajee Jeyakumar, Mandalapu Varadhan Sheela Devi, Dinesh Kumar, Vandana Kushwaha, Kanchan Israni
    2025 5th International Conference on Advancement in Electronics and Communication Engineering Aece 2025, 2025
    Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a Cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm’s efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system’s cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales.
  • Leveraging Advanced Data Analytics for Improved Extreme Weather Event Assessment and Mitigation
    P. Kaushik, Balajee Jeyakumar, Molla Khamar, Rizwan Arif, Vineet Kumar, Priya Sharma
    2025 5th International Conference on Advancement in Electronics and Communication Engineering Aece 2025, 2025
    The study introduces an alternative method in which effectiveness can be significantly enhanced and it also addresses certain gaps that have been left by the past researches. Instead of looking at integrated methodologies, earlier studies often used individual analyses. It is the effective combination of GIS spatial analysis, numerical weather prediction models, historical data analysis, machine learning algorithms, and remote sensing technologies that improve the existing landscape. The proposed method is very efficient and Accurate with perfect prediction in terms of accuracy in both numerical models and machine learning. The output metrics include comprehensive thematic mapping, precise temperature prediction, and identifying of geographical risks. It is the overall and integrated nature of this study that makes it unique; it illuminates the extreme weather occurrences in subtle manner. The multi pronged approach assists in resilience by creating a more substantive base of assessment and aversion.
  • An Innovative Secure and Privacy-Preserving Federated Learning-Based Hybrid Deep Learning Model for Intrusion Detection in Internet-Enabled Wireless Sensor Networks
    Soumya Ranjan Jeyakumar, Mohammad Zia Ur Rahman, Deepak K. Sinha, P. Rajendra Kumar, Vrince Vimal, Kamred Udham Singh, Thalakola Syamsundararao, J. N. V. R. Swarup Kumar, J. Balajee
    IEEE Transactions on Consumer Electronics, 2025
    Cyberspace faces numerous security challenges, necessitating advanced research in intrusion detection systems (IDS) to mitigate vulnerabilities. Wireless Sensor Networks (WSNs) connected to the Internet are particularly vulnerable, requiring robust protection mechanisms. Traditional IDS struggle with identifying unknown attacks and maintaining data privacy, especially in WSNs. This study proposes a novel approach integrating Stacked Convolutional Neural Networks (SCNN), Bidirectional Long Short Term Memory (Bi-LSTM), and the African Vulture Optimization Algorithm (AVOA) within a framework of Federated Learning (FL). The integrated model, SCNN-Bi-LSTM-AVOA-FL, aims to enhance intrusion detection efficacy while preserving data privacy. A tailored AVOA optimization method fine-tunes SCNN-Bi-LSTM hyperparameters, leveraging specialized datasets (WSN-DS, CIC-IDS-2017, and WSN-BFSF) for attack detection and categorization. Evaluations compare variants with and without FL techniques (proposed-1 and proposed-2) across metrics such as accuracy, precision, recall, and F1-Score. Results demonstrate significant improvements in prediction performance, validating the efficacy of the proposed approach in enhancing IDS capabilities for WSNs. This research contributes a comprehensive framework for addressing security challenges in WSNs through advanced machine learning and optimization techniques.
  • A novel optimization-based blockchain technology using health care data for enhancing security and privacy in the medical system
    K. Kavita, Raghavendra Kulkarni, A. Hanumat Prasad, Balajee Jeyakumar, Manyam Thaile, Santosh Gore
    Journal of Discrete Mathematical Sciences and Cryptography, 2024
    This study introduces an Ant Lion-based Advanced Encryption Standard with Blockchain (ALAESB) model to enhance security and privacy in healthcare data management. Utilizing cloud storage, Electronic Health Records (EHR) are encrypted using AES and authenticated via blockchain. The methodology integrates hash functions and Merkle trees for data validation and secure block storage. Key generation is optimized through antlion fitness updates, ensuring efficient encryption and decryption processes. The model aims to mitigate privacy risks and secure patient data access for healthcare providers, emphasizing reliability and integrity in medical data handling.
  • Hybridized deep learning goniometry for improved precision in Ehlers-Danlos Syndrome (EDS) evaluation
    Thirumalesu Kudithi, J. Balajee, R. Sivakami, T. R. Mahesh, E. Mohan, Suresh Guluwadi
    BMC Medical Informatics and Decision Making, 2024
  • Deep Learning Safeguard: Exploring GANs for Robust Security in Open Environments
    Enhancing Security in Public Spaces Through Generative Adversarial Networks Gans, 2024
  • Rule Based Mamdani Fuzzy Inference System to Analyze Efficacy of COVID19 Vaccines
    Poonam Mittal, S P Abirami, Puppala Ramya, Balajee J, Elangovan Muniyandy
    Eai Endorsed Transactions on Pervasive Health and Technology, 2024
  • Employing a Hybrid Convolutional Neural Network and Extreme Learning Machine for Precision Liver Disease Forecasting
    Araddhana Arvind Deshmukh, R. V. V. Krishna, Rahama Salman, S Sandhiya, Balajee J, Daniel Pilli
    International Journal of Advanced Computer Science and Applications, 2024
  • Maximizing Learning Outcomes through Fuzzy Inference System and Graph Theory Based on Learning Analytics
    J. Chandra Sekhar, Balajee J., Sanjiv R. Godla, Vuda Sreenivasa Rao, Yousef A. B. El-Ebiary, Chamandeep Kaur
    Journal of Advances in Information Technology, 2024
  • Convolutional Neural Networks in Malaria Diagnosis: A Study on Cell Image Classification
    Hritwik Ghosh, Irfan Sadiq Rahat, J V R Ravindra, Balajee J, Mohammad Aman Ullah Khan, J Somasekar
    Eai Endorsed Transactions on Pervasive Health and Technology, 2024
  • Unleashing the Power of a Novel Lightweight Lattice-based CP-ABE for Robust IoT Data Transmission
    Ad Hoc and Sensor Wireless Networks, 2024
  • An Effective Twitter Spam Detection Model using Multiple Hidden Layers Extreme Learning Machine
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Security Enhancement in Surveillance Cloud Using Machine Learning Techniques
    Lingamallu Raghu Kumar, C. Ashokkumar, Purnendu Shekhar Pandey, Sathish Kumar Kannaiah, Balajee J, M. I. Thariq Hussan
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • Pulmonary Chest Nodule Detection through Adaptive Reinforcement Learning Model (ARLM)
    J. Balajee, U.Kumaran B Haritha, Appakonda Srinadh Reddy, Kompalli Mounika, K Siva Kumar
    2023 2nd International Conference on Electrical Electronics Information and Communication Technologies Iceeict 2023, 2023
  • Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques
    Venkata Rao Yanamadni, J. Seetha, T. Sathish Kumar, Sathish Kumar Kannaiah, Balajee J, Madamanchi Brahmaiah
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
  • Deep convolutional neural network-based Henry gas solubility optimization for disease prediction in data from wireless sensor network
    Chandrashekhar Goswami, V. K. Senthil Ragavan, Janjhyam Venkata Naga Ramesh, J. Balajee, A. Ronald Doni, T. R. Saravanan, S. Siva Shankar
    Soft Computing, 2023
  • Optimizing Heterogeneity in IoT Infra Using Federated Learning and Blockchain-based Security Strategies
    Venkatesan Muthukumar, R. Sivakami, Vinoth Kumar Venkatesan, J. Balajee, T.R. Mahesh, E. Mohan, B. Swapna
    International Journal of Computers Communications and Control, 2023
  • Retraction Note: Drought Prediction and Analysis of Water level based on satellite images using deep convolutional neural network(International Journal of Speech Technology, (2021), 25, (615–623), 10.1007/s10772-021-09850-y)
    J. Balajee, M. A. Saleem Durai
    International Journal of Speech Technology, 2022
  • Massive Open Online Courses: A Tool for Intercontinental Collaboration in Archives and Records Management Education in Eswatini
    Vusi W. Tsabedze, D. Ranjith, T. Karthikeyan, Balajee Jeyakumar
    International Journal of E Collaboration, 2022
  • Selection of Routing Protocol-Based QoS Improvement for Mobile Ad Hoc Network
    V. Vinoth Kumar, R. Deepa, D. Ranjith, M. Balamurugan, J. M. Balajee
    Eai Springer Innovations in Communication and Computing, 2022
  • An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer
    JAIN (Deemed-to-be University), Mahesh T R, A. C. Kaladevi, Sona College of Technology, Balajee J M, Vellore Institute of Technology, V Vivek, JAIN (Deemed-to-be University), M. Prabu, VIT Bhopal University, V. Muthukumaran, REVA University
    International Journal of Integrated Engineering, 2022
  • Improving network security based on trust-aware routing protocols using long short-term memory-queuing segment-routing algorithms
    Muthukumaran V., V. Vinoth Kumar, Rose Bindu Joseph, Meram Munirathanam, Balajee Jeyakumar
    International Journal of Information Technology Project Management, 2021
  • Smart survey on recent trends in water level, drought and water quality analysis system
    J Balajee, M A Saleem Durai
    Journal of Physics Conference Series, 2021
  • Diabetes disease prediction using decision tree for feature selection
    Jayakumar Sadhasivam, V Muthukumaran, J Thimmia Raja, Rose Bindu Joseph, Meram Munirathanam, J M Balajee
    Journal of Physics Conference Series, 2021
  • Design and Evaluation of Wi-Fi Offloading Mechanism in Heterogeneous Networks
    Vinoth Kumar V., Ramamoorthy S., Dhilip Kumar V., Prabu M., Balajee J. M.
    International Journal of E Collaboration, 2021
  • Low power area efficient adaptive FIR filter for hearing aids using distributed arithmetic architecture
    P. V. Praveen Sundar, D. Ranjith, T. Karthikeyan, V. Vinoth Kumar, Balajee Jeyakumar
    International Journal of Speech Technology, 2020
  • A quantum approach in LiFi security using quantum key distribution
    International Journal of Advanced Science and Technology, 2020
  • Personalized content extraction and text classification using effective web scraping techniques
    Karthikeyan T., Karthik Sekaran, Ranjith D., Vinoth Kumar V., Balajee J M
    International Journal of Web Portals, 2019
  • Case Studies in Amalgamation of Deep Learning and Big Data
    Balajee Jeyakumar, M.A. Saleem Durai, Daphne Lopez
    Deep Learning and Neural Networks Concepts Methodologies Tools and Applications, 2019
  • Comparison of machine learning algorithms to build optimized Network intrusion detection system
    H Parveen Sultana, Nirvishi Shrivastava, Dhanapal Durai Dominic, N Nalini, J. M Balajee
    Journal of Computational and Theoretical Nanoscience, 2019
  • Machine learning based classification of cervical cancer using K-Nearest neighbour, Random Forest and Multilayer Perceptron algorithms
    Shakila Basheer, S Mariyam Aysha Bivi, S Jayakumar, Arpit Rathore, Balajee Jeyakumar
    Journal of Computational and Theoretical Nanoscience, 2019
  • Analyzing financial data and mutual funds recommendation by using big data analytics
    Nithya Sampath, Jayakumar Sadhasivam, R Raj Kumar, M Sathish Kumar, Balajee Jeyakumar, P. V PraveenSundar
    Journal of Computational and Theoretical Nanoscience, 2019
  • An analysis on Barrier Coverage in Wireless Sensor networks
    Shakila Basheer, Rincy Merlin Mathew, D Ranjith, M Sathish Kumar, P. V Praveen Sundar, J. M Balajee
    Journal of Computational and Theoretical Nanoscience, 2019
  • Action recongnition in video survillance using HIPI and map reducing model
    International Journal of Mechanical Engineering and Technology, 2017
  • HOCS: Host oscommunication service layer
    International Journal of Civil Engineering and Technology, 2017
  • Review of gaming and its evolution over networks
    International Journal of Civil Engineering and Technology, 2017
  • Content based video retrieval and analysis using image processing: A review
    International Journal of Pharmacy and Technology, 2016
  • In premises of cloud computing and models
    International Journal of Pharmacy and Technology, 2016
  • Superior content-based video retrieval system according to query image
    International Journal of Applied Engineering Research, 2015
  • Computational approach for particle size measurement of silver nanoparticle from electron microscopic image
    International Journal of Pharmacy and Pharmaceutical Sciences, 2013

RECENT SCHOLAR PUBLICATIONS

  • Load-Aware Fog-Based Workflow Scheduling for Reliable IIOT Connectivity in Industry 4.0
    S Anakal, K Shravani, J Balajee, H Kunder, MVHB Murthy, RK Tata, ...
    IEEE Communications Standards Magazine , 2026
    2026
  • Adaptive Deep Defense: Reinforcing Network Intrusion Detection with Hybrid Transformer–LSTM Models
    SS Kumar, J Balajee, J Lande, A Bhansali, R Lotus
    2026 International Conference on ICT and Photonics (ICTP) 1, 1-6 , 2026
    2026
  • Secure AI-Driven Framework for Predicting Drug Toxicity Using Computational Modeling
    R Kumar, J Balajee, J Lande, H Garg, M Tamilarasi, P Rajarajeswari, ...
    2026 International Conference on ICT and Photonics (ICTP) 1, 1-6 , 2026
    2026
  • 6G-Enabled Edge-Enhanced Functional Chain Scheduling for Intelligent Medical Emergency Communication in Smart Healthcare
    H Byeon, DN Reddy, D Nimma, J Balajee, GSN Rao, A Smerat, M Soni
    IEEE Communications Standards Magazine , 2026
    2026
  • Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare
    J Balajee, K Kathiravan, G Sangar
    Machine Learning in Healthcare: Data-Driven Decisions, Predictive Modelling … , 2025
    2025
  • Isolation Forest-Driven Anomaly Detection Framework for UAV-Based Power Line Inspection in the Cloud
    P Kaushik, B Jeyakumar, MVS Devi, D Kumar, V Kushwaha, K Israni
    2025 5th International Conference on Advancement in Electronics … , 2025
    2025
  • Leveraging Advanced Data Analytics for Improved Extreme Weather Event Assessment and Mitigation
    P Kaushik, B Jeyakumar, M Khamar, R Arif, V Kumar, P Sharma
    2025 5th International Conference on Advancement in Electronics … , 2025
    2025
  • Method and System for preventing Identity Spoofing using Artificial intelligence Driven Pattern Recognition
    B Jeyakumar
    US Patent 20,250,285,471 , 2025
    2025
  • AI-BASED SYSTEM FOR IOT-BASED ADAPTIVE MANUFACTURING LINE OPTIMIZATION AND METHOD THEREOF
    DBJ al
    IN Patent 49/2,025 , 2025
    2025
  • Empowering Farmers through Sustainable Agriculture
    BJ G. Harika, G Kavya Sree, K.S. Sadiya, Ravi Kumar K V
    International Research Journal of Innovations in Engineering and Technology … , 2025
    2025
  • Bio-Thermal Hybrid Storage (BTHS): Transforming Waste into Watts
    BJ Sai Lokeshwar A, Ravi Kumar K V
    International Research Journal of Innovations in Engineering and Technology … , 2025
    2025
  • Decentralizing Health: The Future of Blockchain in Health Care
    TV A. Thriveni, J. Balajee
    Using Blockchain Technology in Healthcare Settings Empowering Patients with … , 2025
    2025
    Citations: 1
  • A novel optimization-based blockchain technology using health care data for enhancing security and privacy in the medical system
    SG K. Kavita, Raghavendra Kulkarni, A. Hanumat Prasad, Balajee Jeyakumar ...
    Journal of Discrete Mathematical Sciences and Cryptography 27 (8), 2587-2598 , 2024
    2024
  • An Innovative Secure and Privacy-Preserving Federated Learning-Based Hybrid Deep Learning Model for Intrusion Detection in Internet-Enabled Wireless Sensor Networks
    SR Jeyakumar, MZU Rahman, DK Sinha, PR Kumar, V Vimal, KU Singh, ...
    IEEE Transactions on Consumer Electronics 71 (1), 273-280 , 2024
    2024
    Citations: 28
  • Hybridized deep learning goniometry for improved precision in Ehlers-Danlos Syndrome (EDS) evaluation
    T Kudithi, J Balajee, R Sivakami, TR Mahesh, E Mohan, S Guluwadi
    BMC Medical Informatics and Decision Making 24 (1), 196 , 2024
    2024
    Citations: 4
  • Artificial Intelligence techniques for large-scale image retrieval: addressing efficiency and scalability in visual search
    VH A. Thriveni, J. Balajee
    Futuristic Trends in Artificial Intelligence 3, 217-254 , 2024
    2024
  • Unveiling the algorithms: How explainable AI reshapes healthcare
    RV A. Thriveni, J. Balajee
    Explainable Artificial Intelligence in Healthcare Systems 1, 101-117 , 2024
    2024
  • Employing a Hybrid Convolutional Neural Network and Extreme Learning Machine for Precision Liver Disease Forecasting.
    AA Deshmukh, RVV Krishna, R Salman, S Sandhiya, J Balajee, D Pilli
    International Journal of Advanced Computer Science & Applications 15 (2) , 2024
    2024
    Citations: 2
  • Smart Transportation Systems Machine Learning Application in WSN-Based Digital Twins
    M Ayyavaraiah, B Jeyakumar, S Chidambaranathan, S Subramaniam, ...
    Harnessing AI and Digital Twin Technologies in Businesses, 356-366 , 2024
    2024
    Citations: 7
  • Rule Based Mamdani Fuzzy Inference System to Analyze Efficacy of COVID19 Vaccines
    P Mittal, SP Abirami, P Ramya, J Balajee, E Muniyandy
    EAI Endorsed Transactions on Pervasive Health and Technology 10 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Personalized Content Extraction and Text Classification Using Effective Web Scraping Techniques
    T Karthikeyan, K Sekaran, D Ranjith, V Vinoth kumar, JM Balajee
    International Journal of Web Portals (IJWP) 11 (2), 41-52 , 2019
    2019
    Citations: 155
  • Low power area efficient adaptive FIR filter for hearing aids using distributed arithmetic architecture
    PV Praveen Sundar, D Ranjith, T Karthikeyan, V Vinoth Kumar, ...
    International Journal of Speech Technology 23 (2), 287-296 , 2020
    2020
    Citations: 103
  • Improving network security based on trust-aware routing protocols using long short-term memory-queuing segment-routing algorithms
    V Muthukumaran, VV Kumar, RB Joseph, M Munirathanam, B Jeyakumar
    International Journal of Information Technology Project Management (IJITPM … , 2021
    2021
    Citations: 41
  • Design and Evaluation of Wi-Fi Offloading Mechanism in Heterogeneous Networks
    JM Vinoth Kumar, V. , Ramamoorthy, S. , Dhilip Kumar, V. , Prabu, M. , Balajee
    International Journal of e-Collaboration (IJeC) 17 (1), 62-70 , 2021
    2021
    Citations: 41
  • Data Wrangling and Data Leakage in Machine Learning for Healthcare
    SNSGB J M
    International Journal of Emerging Technologies and Innovative Research 5 (8 … , 2018
    2018
    Citations: 38
  • A Quantum Approach in LiFi Security using Quantum Key Distribution
    BJM Vinoth Kumar V, Karthikeyan T, Praveen Sundar P V, Magesh G
    International Journal of Advanced Science and Technology 29 (6s), 2345-2354 , 2020
    2020
    Citations: 33
  • Comparison of machine learning algorithms to build optimized network intrusion detection system
    H Parveen Sultana, N Shrivastava, DD Dominic, N Nalini, JM Balajee
    Journal of Computational and Theoretical Nanoscience 16 (5-6), 2541-2549 , 2019
    2019
    Citations: 30
  • An Innovative Secure and Privacy-Preserving Federated Learning-Based Hybrid Deep Learning Model for Intrusion Detection in Internet-Enabled Wireless Sensor Networks
    SR Jeyakumar, MZU Rahman, DK Sinha, PR Kumar, V Vimal, KU Singh, ...
    IEEE Transactions on Consumer Electronics 71 (1), 273-280 , 2024
    2024
    Citations: 28
  • An efficient ensemble method using K-fold cross validation for the early detection of benign and malignant breast cancer
    TR Mahesh, AC Kaladevi, JM Balajee, V Vivek, M Prabu, ...
    International Journal of Integrated Engineering 14 (7), 204-216 , 2022
    2022
    Citations: 27
  • Superior content-based video retrieval system according to query image
    S Kamalakannan, G., Balajee, J., Srinivasa Raghavan
    International Journal of Applied Engineering Research 10 (3), 7951-7957 , 2015
    2015
    Citations: 24
  • In Premises of Cloud Computing and Models
    BJ Ranjith D, Kumar C
    International Journal Of Pharmacy & Technology 8 (3), 4685-4695 , 2016
    2016
    Citations: 21
  • ACTION RECONGNITION IN VIDEO SURVILLANCE USING HIPI AND MAP REDUCING MODEL
    BJANDBP USHAPREETHI P
    International Journal of Mechanical Engineering & Technology (IJMET) 8 (11 … , 2017
    2017
    Citations: 17
  • Optimizing heterogeneity in IoT infra using federated learning and blockchain-based security strategies
    V Muthukumar, R Sivakami, VK Venkatesan, J Balajee, TR Mahesh, ...
    International Journal of Computers Communications & ControL 18 (6) , 2023
    2023
    Citations: 16
  • Case studies in amalgamation of deep learning and big data
    B Jeyakumar, MAS Durai, D Lopez
    HCI Challenges and Privacy Preservation in Big Data Security, 159-174 , 2018
    2018
    Citations: 13
  • Diabetes disease prediction using decision tree for feature selection
    J Sadhasivam, V Muthukumaran, J Thimmia Raja, RB Joseph, ...
    Journal of Physics: Conference Series 1964 (6), 062116 , 2021
    2021
    Citations: 12
  • Detection of MRI Medical MRI Images of Brain Tumors Using Deep Learning & Secure the Transfer of Medical Images Using Blockchain.
    TP Rao, MN Rao, U Arul, J Balajee, SH Hasan
    Journal of Algebraic Statistics 13 (3) , 2022
    2022
    Citations: 10
  • Machine learning based classification of cervical cancer using k-nearest neighbour, random forest and multilayer perceptron algorithms
    S Basheer, S Mariyam Aysha Bivi, S Jayakumar, A Rathore, B Jeyakumar
    Journal of Computational and Theoretical Nanoscience 16 (5-6), 2523-2527 , 2019
    2019
    Citations: 10
  • An analysis on barrier coverage in wireless sensor networks
    S Basheer, RM Mathew, D Ranjith, M Sathish Kumar, S Praveen, ...
    Journal of Computational and Theoretical Nanoscience 16 (5-6), 2599-2603 , 2019
    2019
    Citations: 10
  • Content based video retrieval and analysis using image processing: A review
    SRS Janarthanan Y, Balajee J.M
    International Journal of Pharmacy and Technology 8 (4), 5042-5048 , 2016
    2016
    Citations: 10
  • Computational approach for particle size measurement of silver nanoparticle from electron microscopic image
    A Rajeshwari, A., Prathna, T.C., Balajee, J., Mandal, A.B., Mukherjee
    International Journal of Pharmacy and Pharmaceutical Sciences 5 (SUPPL.2 … , 2013
    2013
    Citations: 10