Kishore Balasubramanian has more than 23 years of academic experience in imparting Engineering Education. He had his Bachelor’s Degree in Electronics & Instrumentation from Bharathiar University, India, Master’s Degree in Applied Electronics from Anna University, India and Ph.D (Information and Communication Engineering) from Anna University, India. His research interests include Medical Image Processing and Computer Vision. He is an active as a reviewer in many SCI and Scopus indexed journals, conferences and editor in several scientific international journals. He has authored three books in the field of Analog Electronics and has published papers in international and national journals. He has received grants from CSIR, DRDO (Government Funding agencies) for conducting Faculty Development Programmes, Workshops and Conferences. He is a member of ISTE, IRED and IAENG.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Multidisciplinary
Unveiling the Invisible: Powering Security Threat Detection in WSN With AI K. P. Uvarajan, Kishore Balasubramanian, C. Gowri Shankar Concurrency and Computation Practice and Experience, 2025 Security in wireless sensor networks (WSNs) is of paramount importance due to their pervasive deployment in critical infrastructure and sensitive environments. Despite their ubiquitous nature, WSNs are vulnerable to various security threats, ranging from unauthorized access to data manipulation and network disruption. In response to these challenges, this paper proposes a novel approach leveraging the Base Stacked Long Short‐Term Memory with Attention Models and AdaBoost Ensemble (BSLAM‐AE) architecture to enhance security in WSNs. The proposed model is designed to address the unique characteristics and challenges of WSNs, combining deep learning and ensemble learning techniques to detect and mitigate security threats effectively. The BSLAM‐AE model incorporates stacked LSTM networks with attention mechanisms, enabling the analysis of time‐series data and the detection of subtle anomalies or security breaches. In addition, an AdaBoost ensemble‐learning component iteratively trains a set of models to improve predictive accuracy and robustness. Implemented in the PyCharm integrated development environment, experimental results demonstrate the efficacy of the proposed model, achieving an impressive accuracy of 98% in detecting security threats in WSNs. Overall, the BSLAM‐AE model represents a significant advancement in WSN security, offering a comprehensive and efficient solution for detecting and mitigating security threats. By leveraging deep learning and ensemble learning techniques, the proposed model provides enhanced security and reliability, thereby safeguarding WSNs against potential attacks and ensuring the integrity and availability of critical data and infrastructure.
Exploring Deep Learning Methods for Accurate Bridge Crack Detection: A Comparative Study Gayathri Devi Krishnamoorthy, Kishore B, Vinothini V R, Kanmani Ruby E. D, Sakthisudhan K, Thilagavathi K 2025 3rd International Conference on Advancements in Electrical Electronics Communication Computing and Automation Icaeca 2025, 2025 Bridges serve as an essential part in infrastructure and must undergo consistent inspection and maintenance to ensure safety and structural soundness. One common issue that bridges can face is the development of cracks, which can compromise their stability and safety. This study proposed a crack detection system with the Kaggle dataset of bridge images with and without cracks and trained using three architecture SqueezeNet, GoogLeNet and Alex-Net architecture. The images were pre-processed to improve their quality and to standardize their dimensions. A transfer learning approach was applied to a pre-trained network to classify cracked and non-cracked images. Its performance was evaluated using metrics including accuracy, precision, recall, and F1-score. The experimental findings indicated that the AlexNet-based system achieved remarkable crack detection accuracy exceeding 99% in bridge images. Furthermore, it showcased high precision, recall, and F1-score, emphasizing its capability to effectively identify cracks while minimizing both false positives and false negatives.
Classification of white blood cells based on modified U-Net and SVM Kishore Balasubramanian, K. Gayathri Devi, K. Ramya Concurrency and Computation Practice and Experience, 2023 Summary Manual investigation of blood cell count is sometimes erroneous due to interoperability error, fatigue error, requiring expert skill and time consuming too. In particular, investigation of white blood cell (WBC) gains importance in identifying diseases like leukemia, leukopenia, etc. WBC does not possess regular structure because they move throughout the blood stream and hence analyzing WBC and its types for structure and shape is quite challenging. To aid in hematology, this work provides classification of WBC classification based on modified U‐Net and support vector machines (SVM). A modified U‐Net architecture is developed to segment WBC followed by feature extraction and classification by radial basis function‐support vector machine (RBF‐SVM). Experiments indicated that the modified U Net segmentation can detect the WBC nucleus with a dice similarity coefficient of 0.972. The proposed U‐Net‐SVM can recognize WBCs in Raabin‐WBC, LISC, and BCCD datasets with an accuracy of 99.45%, 98.62%, and 98.81%, respectively. Further investigation on leukemia dataset, ALL‐IDB2, revealed an accuracy of 99.42% with 100% sensitivity and specificity. The proposed model can be used to investigate WBCs and hence provide a great support to the hematologists in analyzing the blood smear for various disease identifications.
Smart Multi Verification Based Security System Uma Maheswari K, Kishore BALASUBRAMANİAN, Dhanu Aravinth K, Karthik V, Padmanaban V K El Cezeri Journal of Science and Engineering, 2023 In this technologically evolving era, security plays a significant role in preventing different assets and crimes. This inconsistency developed an innovative idea to improve the level and solve the existing problems. This paper proposes a well-suited multilayer security system for homes, bank lockers, and more locations we can use it. Traditionally, password and biometric double-layer security systems use everywhere, but this embedded solution combines RFID, OTP, and fingerprint identification in sequence. Essential modules are connected and controlled through a microcontroller with a GSM module. Every operation done by the system pushes to the IoT cloud, and the mobile application shows the status of every action. The right authorized access can let the magnetic switch open, and every unauthorized access turns on the alarm with appropriate message notifications. The Proposed system is more effective and reliable due to multistage security, and it is not easy to crack with the combination of all three stages. The whole system's workings indicate by led flashes. This implementation has shown better results and a higher performance rate than existing methods
Preliminary investigations on automatic segmentation methods for detection and volume calculation of brain tumor from MR images Biomedical Research India, 2016
Cloud-Integrated Hybrid RAG and ML Framework for Healthcare, Finance, and CRM KS Dr. Ganesh Gupta , Dr. Bhargava R , Abeg Kumar Jaiswal , Mr. A. V ... International Journal of Drug Delivery Technology 16 (42s), 304-312 , 2026 2026
Retinal image-based cardiovascular risk prediction using AI-CRS: a multi-modal deep learning framework C Mariswari, K Balasubramanian International Ophthalmology 46 (1), 192 , 2026 2026
AQ-PQC: Adaptive Lightweight Hybrid Post-Quantum Key Exchange Protocol for Resource-Constrained IoT Devices with Formal Security Proofs DKB Ms. T Cowsalya, Ms. Jeni Narayanan L A,Ms. N. Logeshwari Kronika Journal 26 (4), 189-206 , 2026 2026
Optimized deep learning with Grad-CAM for automated cardamom classification: A multispectral imaging approach for real-time mobile deployment R Jose, K Balasubramanian Computers and Electrical Engineering 132, 110992 , 2026 2026
Professional Skills-I TS P Shenbagarajan, Kishore Balasubramanian, T sekar 9789373592701 S Chand Publications 1, 309 , 2026 2026
Quantum-enhanced data fusion framework for early detection and intervention of goodpasture syndrome (GPS) B Senthilnayaki, S Karthik, MSS Sasikumar, P Koppula, S Dhanasekar, ... International Journal of Information Technology, 1-8 , 2026 2026
AI, IoT, and 6G Convergence for Smart Sustainable Cities and Clean Energy Systems: Innovations in Intelligent Infrastructure and Urban Sustainability K Balasubramanian RAD Publishers 1, 383 , 2025 2025
Blockchain-Enabled IoT framework with Energy-Efficient machine learning for scalable and secure smart cities VRG Nallagattla, A Rai, S Thangam, GJS Deol, ASK Ratnam, JN Rao, ... Sustainable Computing: Informatics and Systems, 101212 , 2025 2025 Citations: 5
BESS-Enabled Smart Grid Environments: A Comprehensive Framework for Cyber Threat Classification, Cybersecurity, and Operational Resilience PP Gopinath, K Balasubramanian, RDA Raj, A Pallakonda, ... Technologies 13 (9), 423 , 2025 2025 Citations: 5
Exploring Deep Learning Methods for Accurate Bridge Crack Detection: A Comparative Study GD Krishnamoorthy, K B, V V R, KR E. D, S K, T K 3rd International Conference on Advancements in Electrical, Electronics … , 2025 2025
Exploring Gene Signature and Expression Trends: A Bibliometric Study Utilizing Biblioshiny and VOSviewer with Deep Learning and Convolutional Neural Networks R Jose, K Balasubramanian 2025 6th International Conference on Control, Communication and Computing … , 2025 2025
Unveiling the Invisible: Powering Security Threat Detection in WSN With AI CGS K. P. Uvarajan, Kishore Balasubramanian Concurrency and Computation: Practice and Experience 37 (9), e70049 , 2025 2025
Machine Learning Based Security Device for Cloud Computing K Balasubramanian IN Patent Design No: 440605-001 , 2025 2025
Deep Reinforcement Learning Applications in Autonomous Systems and Decision-Making KB Jeyarani R ,K. Nanthitha ,Dr Sweta Kumari ISBN10: 9349552981 | ISBN13: 9789349552982 , 2025 2025
Real Time Implementation of GHNN-PI Based ACM Controlled Self-Tuning FS-SNIBB Converter J Nagarajan, M Balasubramonian, K Balasubramanian Journal of Electrical Engineering & Technology 20 (3), 1347–1361 , 2025 2025
Multivariate technique for the prediction and classification of brain tumor using deep shallow network GD Krishnamoorthy, K Balasubramanian Applied Soft Computing 164, 111962 , 2024 2024 Citations: 9
Feature analysis and classification of maize crop diseases employing AlexNet-inception network K Balasubramanian Multimedia Tools and Applications 83 (9), 26971-26999 , 2024 2024 Citations: 23
A Comprehensive Analysis on Early Forecasting and Pest Monitoring in Crops KBKN Senthilkumar .C, Gayathri Devi.K Indian Journal of Natural Sciences 14 (82), 68416-68422 , 2024 2024
Futuristic Trends in Computing Technologies and Data Sciences K Balasubramanian ISBN: 9789362524878 , 2024 2024
Survey on the Techniques for Classification and Identification of Brain Tumour Types from MRI Images Using Deep Learning Algorithms DK Gayathri, K Balasubramanian Recent Advances in Computer Science and Communications 16 (9), 11-26 , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Review on Application of Drones for Crop Health Monitoring and Spraying Pesticides and Fertilizer KB Gayathri Devi, N.Sowmiya, K.Yasoda,K.Muthulakshmi Journal of Critical Reviews 7 (6), 667-672 , 2020 2020 Citations: 118
Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method K Balasubramanian CMC-Computers, Materials & Continua 72 (1), 519–534 , 2022 2022 Citations: 77
Social Network User Profiling With Multilayer Semantic Modeling Using Ego Network GB Tamil Selvi P., Balasubramaniam, Kishore., Vidhya S.,, Jayapandian N ... International Journal of Information Technology and Web Engineering 17 (1), 1-14 , 2022 2022 Citations: 72
Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis K Balasubramanian, NP Ananthamoorthy Neural Computing and Applications 33 (13), 7649-7660 , 2021 2021 Citations: 45
Design and Implementation of IoT Integrated Monitoring and Control System of Renewable Energy in Smart Grid for Sustainable Computing Network NPG Bhavani, R Kumar, BS Panigrahi, K Balasubramanian, ... Sustainable Computing: Informatics and Systems 35 (100769) , 2022 2022 Citations: 43
An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm K Balasubramanian, NP Ananthamoorthy, K Ramya Neural Computing and Applications 34 (18), 16089-16101 , 2022 2022 Citations: 40
Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network K Balasubramanian Electronics 11 (11), 1763 , 2022 2022 Citations: 39
Correlation-based feature selection using bio-inspired algorithms and optimized KELM classifier for glaucoma diagnosis K Balasubramanian, NP Ananthamoorthy Applied Soft Computing 128 (C), 109432 , 2022 2022 Citations: 37
Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals GDK Kishore Balasubramanian, K. Ramya Cognitive Neurodynamics , 2022 2022 Citations: 32
Glaucoma classification based on intra-class and extra-class discriminative correlation and consensus ensemble classifier B Kishore, NP Ananthamoorthy Genomics 112 (5), 3089-3096 , 2020 2020 Citations: 27
Feature analysis and classification of maize crop diseases employing AlexNet-inception network K Balasubramanian Multimedia Tools and Applications 83 (9), 26971-26999 , 2024 2024 Citations: 23
Machine Learning and Deep Learning Techniques for Medical Science LAN K.Gayathri Devi, Kishore Balasubramanian CRC Press 1, 412 , 2022 2022 Citations: 20
Estimation of the effects of normal tissue sparing using equivalent uniform dose-based optimization K Senthilkumar, KJM Das, K Balasubramanian, AC Deka, BR Patil Journal of Medical Physics 41 (2), 123-128 , 2016 2016 Citations: 16
Improved swarm optimization of deep features for glaucoma classification using SEGSO and VGGNet KGD Kishore Balasubramanian, K Ramya Biomedical Signal Processing and Control 77 (103845) , 2022 2022 Citations: 15
Automatic Health Care Waste Segregation and Disposal System Kishore Journal of Xidian University 14 (5), 5281-90 , 2020 2020 Citations: 15
Accurate prediction and classification of corn leaf disease using adaptive moment estimation optimizer in deep learning networks K Gayathri Devi, K Balasubramanian, C Senthilkumar, K Ramya Journal of Electrical Engineering & Technology 18 (1), 637-649 , 2023 2023 Citations: 14
Classification of white blood cells based on modified U-Net and SVM KR Kishore Balasubramanian, K. Gayathri Devi Concurrency and Computation: Practice and Experience, e7862 , 2023 2023 Citations: 12
Energy management system using binary particle swarm optimization technique GD Krishnamoorthy, K Balasubramanian, S Govindaraj, PG Ayyavu, ... 5TH INTERNATIONAL CONFERENCE ON INNOVATIVE DESIGN, ANALYSIS & DEVELOPMENT … , 2023 2023 Citations: 10
A hybrid deep learning for patient activity recognition (PAR): Real time body wearable sensor network from healthcare monitoring system (HMS) K Balasubramanian, AV Prabu, MF Shaik, RA Naik, SK Suguna Journal of Intelligent & Fuzzy Systems 44 (1), 195 – 211 , 2023 2023 Citations: 10
Automatic Diagnosis and Classification of Glaucoma Using Hybrid Features and k -Nearest Neighbor ANP Kishore Balasubramanian Journal of Medical Imaging and Health Informatics 8 (8), 1598–1606 , 2018 2018 Citations: 10