Dr. S. Koteeswaran, B.Tech., M.E., Ph.D. currently working as Professor in the Department of Computer Science and Engineering (AI&ML), S.A. Engineering College, Chennai-600077, TamilNadu, India. He is having 15 years of teaching experience and published more than 50 research articles in various peer reviewed Journals. He is author for two text books and two edited books for Computer Science & Engineering Programme. His research interests include Artificial Intelligence, Machine Learning, Deep Learning, Big Data and Analytics and Internet of Things. He has presented several papers in conference proceedings. He is a reviewer for more than a dozen journals and also organized more than 25 various events such as National and International Conferences, Faculty Development Programs, Workshops, Seminars, National Level Paper Contests, Quiz programmes, 24 Hours IEEE Xtreme Programming Competition and 36 hours Hachathon. He is a Member of ACM, Member of IAEng, Global Member of ISOC.
EDUCATION
Ph.D. (Computer Science and Engineering)
Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology,
Chennai, 2013.
M.E. (Software Engineering)
Vel Tech Engineering College, Anna University, Chennai, 2009.
B.Tech. (Information Technology)
Amrita Institute of Technology and Science, Anna University, Chennai, 2006.
A Sophisticated Onscreen Smart Framework for Predicting Diabetes in Remote Healthcare Koteeswaran Seerangan, Premalatha Gunasekaran, Nithya Rekha Sivakumar, Resmi Ravi Nair, Malarvizhi Nandagopal, et al. Diagnostics, 2026 Background/Objectives: Diabetes is one of the most familiar and common diseases among people currently, and is a type of metabolic disease that is caused due to high levels of sugar in the blood for longer periods of time. If the disease is predicted at an earlier stage, the severity and risks associated with diabetes are significantly reduced, which helps to save the lifespan of people. In earlier investigations, various kinds of automated models based on artificial intelligence (AI) were developed for this purpose. However, key issues still revolve around the lack of robustness, dependability, and precise prediction. The motivation behind the proposed study is to design and develop an automated tool for the diagnosis of chronic disease with the use of novel AI methodology. Methods: For this purpose, a new detection framework is introduced, known as the Brass Optimized Learning-Based Diabetes Prediction (BOLD) model for remote healthcare applications. By using this kind of optimization-integrated deep learning technique, the overall performance and efficiency of the diabetes detection system are maximized. This framework preprocesses the input diabetes dataset before performing the data splitting, normalization, and cleaning activities. Next, the best attributes for improving the prognostic performance of the classifier are chosen using the Brassy Pelican Optimization (BPO) procedure. The Hunting Optimized Recurrent Neural Network—Long Short-Term Memory (RNN-LSTM) method is used to categorize the people into those who are diabetic and those who are not based on the chosen attributes. The approach employs a Deer Hunting Optimization (DHO) method to choose the hyperparameters needed to make an informed choice. A variety of parameters have been employed to confirm the results, which are evaluated for performance verification using the PIDD, Indonesia diabetic database, and kidney disease dataset. Results: The BOLD framework is successful to the extent that it has been able to achieve several metrics of comparably good results, such as an RMSE value of 0.015, a Cohen’s Kappa measure of 0.99, a precision of 0.991, a recall of 0.99, an accuracy equal to 0.996, and an AUC equal to 0.99. Conclusions: It is also remarkable that a very short time of 0.8 s was enough for it to deliver this kind of performance, making it a neat combination of both time and power efficiency.
Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, et al. Sensors, 2025 As cyber–physical systems are applied not only to crucial infrastructure but also to day-to-day technologies, from industrial control systems through to smart grids and medical devices, they have become very significant. Cyber–physical systems are a target for various security attacks, too; their growing complexity and digital networking necessitate robust cybersecurity solutions. Recent research indicates that deep learning can improve CPS security through intelligent threat detection and response. We still foresee limitations to scalability, data privacy, and handling the dynamic nature of CPS environments in existing approaches. We developed the CPS ShieldNet Fusion model as a comprehensive security framework for protecting CPS from ever-evolving cyber threats. We will present a model that integrates state-of-the-art methodologies in both federated learning and optimization paradigms through the combination of the Federated Residual Convolutional Network (FedRCNet) and the EEL-Levy Fusion Optimization (ELFO) methods. This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. This preserves data privacy through decentralized model training and improves complex security threat detection. We report the results of a rigorous evaluation of CICIoT-2023, Edge-IIoTset-2023, and UNSW-NB datasets containing the CPS ShieldNet Fusion model at the forefront in terms of accuracy and effectiveness against several threats in different CPS environments. Therefore, these results underline the potential of the proposed framework to improve CPS security by providing a robust and scalable solution to current problems and future threats.
A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems Malarvizhi Nandagopal, Koteeswaran Seerangan, Tamilmani Govindaraju, Neeba Eralil Abi, Balamurugan Balusamy, et al. Scientific Reports, 2024 In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared.
Study on Book Recommendation System V K Kavitha, S Koteeswaran Proceedings of the Accthpa 2023 Conference on Advanced Computing and Communication Technologies for High Performance Applications, 2023
Streaming Analytics: Concepts, architectures, platforms, use cases and applications Streaming Analytics Concepts Architectures Platforms Use Cases and Applications, 2022
A pragmatic approach on the internet of things for smart applications International Journal of Recent Technology and Engineering, 2019
A lightweight security scheme for IoT based medical applications International Journal of Innovative Technology and Exploring Engineering, 2019
Environmental monitoring and assessment by applying iot for reducing pollution caused by vehicles International Journal of Engineering and Advanced Technology, 2019
An effective novel IOT framework for water irrigation system in smart precision agriculture International Journal of Innovative Technology and Exploring Engineering, 2019
Emprical study of iot solution for the security threats in real life scenario: State of the art International Journal of Engineering and Technology Uae, 2018
An intelligent recursive feature reduction methods for efficient classification of medical blogs International Journal of Engineering and Technology Uae, 2018
Message 2017 IEEE International Conference on Smart Technologies and Management for Computing Communication Controls Energy and Materials Icstm 2017 Proceedings, 2017
Medical blog classification using hybrid feature selection mechanisms Research Journal of Biotechnology, 2017
An efficient patient’s response predicting system using multi-scale dilated ensemble network framework with optimization strategy N Manogaran, N Panabakam, D Selvaraj, K Seerangan, F Khan, ... Scientific Reports 15 (1), 15713 , 2025 2025 Citations: 1
IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks SCM Sundararajan, YB Shankar, SP Selvam, N Manogaran, ... Scientific Reports 15 (1), 1925 , 2025 2025 Citations: 27
A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning K Seerangan, M Nandagopal, T Govindaraju, N Manogaran, B Balusamy, ... Scientific Reports 14 (1), 22188 , 2024 2024 Citations: 21
Integrating meta-heuristic with named data networking for secure edge computing in IoT enabled healthcare monitoring system N Manogaran, M Nandagopal, NE Abi, K Seerangan, B Balusamy, ... Scientific Reports 14 (1), 21532 , 2024 2024 Citations: 12
ERABiLNet: enhanced residual attention with bidirectional long short-term memory K Seerangan, M Nandagopal, RR Nair, S Periyasamy, RH Jhaveri, ... Scientific Reports 14 (1), 20622 , 2024 2024 Citations: 3
Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data RA Sundaramoorthy, AD Ananth, K Seerangan, M Nandagopal, ... Scientific Reports 14 (1), 18437 , 2024 2024 Citations: 9
A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems M Nandagopal, K Seerangan, T Govindaraju, NE Abi, B Balusamy, ... Scientific Reports 14 (1), 10280 , 2024 2024 Citations: 21
Leather Image Quality Classification and Defect Detection System using Mask Region-based Convolution Neural Network Model. AB Abdullah, M Jawahar, N Manogaran, G Subbiah, K Seeranagan, ... International Journal of Advanced Computer Science & Applications 15 (4) , 2024 2024 Citations: 6
Detecting the Possession of Harmful Weapons by Humans Through Surveillance System N Manogaran, S Annamalai, M Nandagopal, K Seerangan, B Balusamy, ... SSRG INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING … , 2024 2024
RETRACTED ARTICLE: A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu S Koteeswaran, R Suganya, C Surianarayanan, EA Neeba, A Suresh, ... Soft Computing, 1-1 , 2023 2023 Citations: 1
Streaming Analytics: Concepts, Architectures, Platforms, Use Cases and Applications P Raj, C Surianarayanan, K Seerangan, G Ghinea IET , 2022 2022 Citations: 1
Prediction of heart conditions by consensus K -nearest neighbor algorithm and convolution neural network SF Waris, S Koteeswaran International Journal of Modeling, Simulation, and Scientific Computing 13 … , 2022 2022 Citations: 2
Coronary Heart Artery Problem Detection and Evaluation employing Deep Neural Network Waris, S.F. and Koteeswaran, S. NeuroQuantology 20 (08), 271-280 , 2022 2022
An Investigation on Disease Diagnosis and Prediction by Using Modified KMean clustering and Combined CNN and ELM Classification Techniques SF Waris, S Koteeswaran International Journal of Communication Networks and Information Security 14 … , 2022 2022 Citations: 2
WITHDRAWN: Heart disease early prediction using a novel machine learning method called improved K-means neighbor classifier in python SF Waris, S Koteeswaran Materials today: proceedings , 2021 2021 Citations: 42
Closed-Loop Irrigation Decision Support System: An Internet of Things Based Closed Loop Irrigation Decision Support System for Precision Agriculture Using Machine Learning P Suresh, S Koteeswaran, RH Aswathy Journal of Computational and Theoretical Nanoscience 18 (3), 942-948 , 2021 2021 Citations: 2
A survey on heart disease early prediction methodologies S Waris, S Koteeswaran Turkish Journal of Computer and Mathematics Education Vol 12 (9), 2023-2037 , 2021 2021 Citations: 4
Early Prediction of Heart Conditions by K-Means Consensus Clustering and Convolution Neural Network SF Waris, S Koteeswaran Annals of the Romanian Society for Cell Biology 25 (3), 6623-6640 , 2021 2021
Sentiment polarity classification using conjure of genetic algorithm and differential evolution methods for optimized feature selection J Jotheeswaran, S Koteeswaran Recent Advances in Computer Science and Communications (Formerly: Recent … , 2020 2020 Citations: 4
Cloud resource scheduling optimal hypervisor (CRSOH) for dynamic cloud computing environment N Malarvizhi, GS Priyatharsini, S Koteeswaran Wireless Personal Communications 115 (1), 27-42 , 2020 2020 Citations: 14
MOST CITED SCHOLAR PUBLICATIONS
Implementation of cloud based Electronic Health Record (EHR) for Indian healthcare needs R Kavitha, E Kannan, S Kotteswaran Indian Journal of Science and Technology 9 (3), 1-5 , 2016 2016.0 Citations: 43
WITHDRAWN: Heart disease early prediction using a novel machine learning method called improved K-means neighbor classifier in python SF Waris, S Koteeswaran Materials today: proceedings , 2021 2021.0 Citations: 42
Identification and classification of best spreader in the domain of interest over the social networks AN Arularasan, A Suresh, K Seerangan Cluster Computing 22, 4035-4045 , 2019 2019.0 Citations: 41
Data mining application on aviation accident data for predicting topmost causes for accidents S Koteeswaran, N Malarvizhi, E Kannan, S Sasikala, S Geetha Cluster computing 22 (Suppl 5), 11379-11399 , 2019 2019.0 Citations: 31
Decision tree based feature selection and multilayer perceptron for sentiment analysis J Jotheeswaran, S Koteeswaran Journal of Engineering and Applied Sciences 10 (14), 5883-5894 , 2015 2015.0 Citations: 28
IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks SCM Sundararajan, YB Shankar, SP Selvam, N Manogaran, ... Scientific Reports 15 (1), 1925 , 2025 2025.0 Citations: 27
A review on clustering and outlier analysis techniques in datamining S Koteeswaran, P Visu, J Janet American journal of applied sciences 9 (2), 254 , 2012 2012.0 Citations: 26
Feature selection using random forest method for sentiment analysis J Jotheeswaran, S Koteeswaran Indian Journal of Science and Technology 9 (3), 1-7 , 2016 2016.0 Citations: 22
A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning K Seerangan, M Nandagopal, T Govindaraju, N Manogaran, B Balusamy, ... Scientific Reports 14 (1), 22188 , 2024 2024.0 Citations: 21
A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems M Nandagopal, K Seerangan, T Govindaraju, NE Abi, B Balusamy, ... Scientific Reports 14 (1), 10280 , 2024 2024.0 Citations: 21
Artificial bee colony based energy aware and energy efficient routing protocol P Visu, S Koteeswaran, J Janet Journal of Computer Science 8 (2), 227 , 2012 2012.0 Citations: 17
An effective novel IOT framework for water irrigation system in smart precision agriculture P Suresh, S Koteeswaran International Journal of Innovative Technology and Exploring Engineering 8 … , 2019 2019.0 Citations: 16
Cloud resource scheduling optimal hypervisor (CRSOH) for dynamic cloud computing environment N Malarvizhi, GS Priyatharsini, S Koteeswaran Wireless Personal Communications 115 (1), 27-42 , 2020 2020.0 Citations: 14
Deep learning-based decision-making with WoT for smart city development S Vimal, V Jeyabalaraja, P Subbulakshmi, A Suresh, M Kaliappan, ... Smart innovation of web of things, 51-62 , 2020 2020.0 Citations: 14
Sentiment analysis: A survey of current research and techniques DSK Jeevanandam Jotheeswaran International Journal of Innovative Research in Computer sand Communication … , 2015 2015.0 Citations: 14
Smart Eye Testing, Advances in Intelligent Systems and Computing, 2021, ISCDA 2020, 1312 AISC S Hrushikesava Raju, LR Burra, SF Waris, S Kavitha, S Dorababu Citations: 13
Integrating meta-heuristic with named data networking for secure edge computing in IoT enabled healthcare monitoring system N Manogaran, M Nandagopal, NE Abi, K Seerangan, B Balusamy, ... Scientific Reports 14 (1), 21532 , 2024 2024.0 Citations: 12
Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data RA Sundaramoorthy, AD Ananth, K Seerangan, M Nandagopal, ... Scientific Reports 14 (1), 18437 , 2024 2024.0 Citations: 9
Swarm-based clustering algorithm for efficient web blog and data classification EA Neeba, S Koteeswaran, N Malarvizhi The Journal of Supercomputing 76 (6), 3949-3962 , 2020 2020.0 Citations: 9
Optimal energy management in wireless adhoc network using Artificial Bee Colony based routing protocol P Visu, J Janet, E Kannan, S Koteeswaran European Journal of Scientific Research 74 (2), 301-307 , 2012 2012.0 Citations: 9