KOTEESWARAN S

@saec.ac.in

Professor, Department of Computer Science and Engineering (AI and ML)
S.A. Engineering College (Autonomous)

KOTEESWARAN S


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https://researchid.co/koteeswaran
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.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Computer Science Applications, Software
63

Scopus Publications

545

Scholar Citations

14

Scholar h-index

17

Scholar i10-index

Scopus Publications

  • A Sophisticated Onscreen Smart Framework for Predicting Diabetes in Remote Healthcare
    Koteeswaran Seerangan, Premalatha Gunasekaran, Nithya Rekha Sivakumar, Resmi Ravi Nair, Malarvizhi Nandagopal, Neeba Eralil Abi, Nalini Manogaran
    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.
  • Manual and Automated Web-Based Diagnosis and Interpretation of Mammograms of Breast Cancer and Robust Analysis
    Nalini Manogaran, Suresh Annamalai, Koteeswaran Seerangan, Balamurugan Balusamy, Parimala Veluvali, Sushama Tanwar
    Communications in Computer and Information Science, 2026
  • An efficient patient’s response predicting system using multi-scale dilated ensemble network framework with optimization strategy
    Nalini Manogaran, Nirupama Panabakam, Durai Selvaraj, Koteeswaran Seerangan, Firoz Khan, Shitharth Selvarajan
    Scientific Reports, 2025
    The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful long-term health impacts would considerably enhance individual treatment plans. Due to the continuous exposure to radiation, cardiovascular disease and pulmonary fibrosis might occur. For forecasting the response of patients to chemotherapy, the Convolutional Neural Networks (CNN) technique is widely used. With the help of radiotherapy, cancer diseases are diagnosed, but some patients suffer from side effects. The toxicity of radiotherapy and chemotherapy should be estimated. For validating the patient's improvement in treatments, a patient response prediction system is essential. In this paper, a Deep Learning (DL) based patient response prediction system is developed to effectively predict the response of patients, predict prognosis and inform the treatment plans in the early stage. The necessary data for the response prediction are collected manually. The collected data are then processed through the feature selection segment. The Repeated Exploration and Exploitation-based Coati Optimization Algorithm (REE-COA) is employed to select the features. The selected weight features are input into the prediction process. Here, the prediction is performed by Multi-scale Dilated Ensemble Network (MDEN), where we integrated Long-Short term Memory (LSTM), Recurrent Neural Network (RNN) and One-dimensional Convolutional Neural Networks (1DCNN). The final prediction scores are averaged to develop an effective MDEN-based model to predict the patient's response. The proposed MDEN-based patient's response prediction scheme is 0.79%, 2.98%, 2.21% and 1.40% finer than RAN, RNN, LSTM and 1DCNN, respectively. Hence, the proposed system minimizes error rates and enhances accuracy using a weight optimization technique.
  • IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks
    Suma Christal Mary Sundararajan, Yamini Bhavani Shankar, Sinthia Panneer Selvam, Nalini Manogaran, Koteeswaran Seerangan, Deepa Natesan, Shitharth Selvarajan
    Scientific Reports, 2025
    The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of providing efficient solutions through communication technologies. IoT systems are used for enabling connection between various devices based on the ability to gather and collect information. Furthermore, IoT systems are designed to address the environment and the automation industry. The threats associated with aquaponics farming are managed through an IoT-based smart water monitoring framework, which has become increasingly relevant in recent days. Therefore, this approach is crucial for achieving a remarkable improvement in order to increase the productivity rate and yield. The quality of water directly affects the rate of growth, efficiency of feed, and the overall health rate of the fish, plants, and bacteria. Insufficient knowledge about species selection poses a significant challenge in aquaponics farming, as it heavily relies on the water quality parameters. To address the challenges of conventional models, we have developed an effective IoT-based water quality prediction model, more specifically designed for aquaponic fish ponds. The data needed to perform the developed water quality prediction model will be acquired from "a simple dataset of aquaponic fish pond IoT" database. After that, these data are forwarded to the feature extraction phase. The weighted features, DBN (Deep Belief Network) features, and the original features are achieved in the feature extraction stage. The weighted features are obtained using the Revamped Fitness-based Mother Optimization Algorithm (RF-MOA). Subsequently, these extracted features are fed into the Multi-Scale feature fusion-based Convolutional Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network for predicting the water quality. Thus, the water quality predicted data is obtained. The proposed model integrates GRU networks with a convolutional autoencoder to improve water quality prediction by capturing trends and managing temporal dependencies. It enhances accuracy by analysing key parameters and employing techniques to reduce overfitting. The effectiveness of the proposed system is evaluated in comparison to the traditional models using some evaluation measures.
  • AI Innovations for Improving the Food Industry
    AI Innovations for Improving the Food Industry, 2025
  • 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, Sanmugasundaram Ravichandran, Koteeswaran Seerangan
    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.
  • Integrating meta-heuristic with named data networking for secure edge computing in IoT enabled healthcare monitoring system
    Nalini Manogaran, Malarvizhi Nandagopal, Neeba Eralil Abi, Koteeswaran Seerangan, Balamurugan Balusamy, Shitharth Selvarajan
    Scientific Reports, 2024
    The advancement in technology, with the "Internet of Things (IoT) is continuing a crucial task to accomplish distance medical care observation, where the effective and secure healthcare information retrieval is complex. However, the IoT systems have restricted resources hence it is complex to attain effective and secure healthcare information acquisition. The idea of smart healthcare has developed in diverse regions, where small-scale implementations of medical facilities are evaluated. In the IoT-aided medical devices, the security of the IoT systems and related information is highly essential on the other hand, the edge computing is a significant framework that rectifies their processing and computational issues. The edge computing is inexpensive, and it is a powerful framework to offer low latency information assistance by enhancing the computation and the transmission speed of the IoT systems in the medical sectors. The main intention of this work is to design a secure framework for Edge computing in IoT-enabled healthcare systems using heuristic-based authentication and "Named Data Networking (NDN)". There are three layers in the proposed model. In the first layer, many IoT devices are connected together, and using the cluster head formation, the patients are transmitting their data to the edge cloud layer. The edge cloud layer is responsible for storage and computing resources for rapidly caching and providing medical data. Hence, the patient layer is a new heuristic-based sanitization algorithm called Revised Position of Cat Swarm Optimization (RPCSO) with NDN for hiding the sensitive data that should not be leaked to unauthorized users. This authentication procedure is adopted as a multi-objective function key generation procedure considering constraints like hiding failure rate, information preservation rate, and degree of modification. Further, the data from the edge cloud layer is transferred to the user layer, where the optimal key generation with NDN-based restoration is adopted, thus achieving efficient and secure medical data retrieval. The framework is evaluated quantitatively on diverse healthcare datasets from University of California (UCI) and Kaggle repository and experimental analysis shows the superior performance of the proposed model in terms of latency and cost when compared to existing solutions. The proposed model performs the comparative analysis of the existing algorithms such as Cat Swarm Optimization (CSO), Osprey Optimization Algorithm (OOA), Mexican Axolotl Optimization (MAO), Single candidate optimizer (SCO). Similarly, the cryptography tasks like "Rivest-Shamir-Adleman (RSA), Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Data sanitization and Restoration (DSR) are applied and compared with the RPCSO in the proposed work. The results of the proposed model is compared on the basis of the best, worst, mean, median and standard deviation. The proposed RPCSO outperforms all other models with values of 0.018069361, 0.50564046, 0.112643119, 0.018069361, 0.156968355 and 0.283597992, 0.467442652, 0.32920734, 0.328581887, 0.063687386 for both dataset 1 and dataset 2 respectively.
  • 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, Shitharth Selvarajan
    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.
  • Implementing heuristic-based multiscale depth-wise separable adaptive temporal convolutional network for ambient air quality prediction using real time data
    Raj Anand Sundaramoorthy, Antony Dennis Ananth, Koteeswaran Seerangan, Malarvizhi Nandagopal, Balamurugan Balusamy, Shitharth Selvarajan
    Scientific Reports, 2024
    In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)-a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)-is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets.
  • A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning
    Koteeswaran Seerangan, Malarvizhi Nandagopal, Tamilmani Govindaraju, Nalini Manogaran, Balamurugan Balusamy, Shitharth Selvarajan
    Scientific Reports, 2024
    In the air-to-ground transmissions, the lifespan of the network is based on the "unmanned aerial vehicle's (UAV)" life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate's minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the "Internet of Things (IoT)" systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.
  • ERABiLNet: enhanced residual attention with bidirectional long short-term memory
    Koteeswaran Seerangan, Malarvizhi Nandagopal, Resmi R. Nair, Sakthivel Periyasamy, Rutvij H. Jhaveri, Balamurugan Balusamy, Shitharth Selvarajan
    Scientific Reports, 2024
  • Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network
    Woothukadu Thirumaran Chembian, Krishna Murthi Sankar, Seerangan Koteeswaran, Kandasamy Thinakaran, Periyannan Raman
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
  • Detecting the Possession of Harmful Weapons by Humans Through Surveillance System
    Nalini Manogaran, Suresh Annamalai, Malarvizhi Nandagopal, Koteeswaran Seerangan, Balamurugan Balusamy, Francesco Benedetto
    Ssrg International Journal of Electronics and Communication Engineering, 2024
  • Leather Image Quality Classification and Defect Detection System using Mask Region-based Convolution Neural Network Model
    Azween Bin Abdullah, Malathy Jawahar, Nalini Manogaran, Geetha Subbiah, Koteeswaran Seeranagan, Balamurugan Balusamy, Abhishek Chengam Saravanan
    International Journal of Advanced Computer Science and Applications, 2024
  • 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
  • A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu
    S. Koteeswaran, R. Suganya, Chellammal Surianarayanan, E. A. Neeba, A. Suresh, Pethuru Raj Chelliah, Seyed M. Buhari
    Soft Computing, 2023
  • Prediction of heart conditions by consensus K-nearest neighbor algorithm and convolution neural network
    Saiyed Faiayaz Waris, S. Koteeswaran
    International Journal of Modeling Simulation and Scientific Computing, 2022
  • An Investigation on Disease Diagnosis and Prediction by Using Modified KMean clustering and Combined CNN and ELM Classification Techniques
    Saiyed Faiayaz Waris, S. Koteeswaran
    International Journal of Communication Networks and Information Security, 2022
  • Streaming Analytics: Concepts, architectures, platforms, use cases and applications
    Streaming Analytics Concepts Architectures Platforms Use Cases and Applications, 2022
  • Cloud Resource Scheduling Optimal Hypervisor (CRSOH) for Dynamic Cloud Computing Environment
    N. Malarvizhi, G. Soniya Priyatharsini, S. Koteeswaran
    Wireless Personal Communications, 2020
  • Swarm-based clustering algorithm for efficient web blog and data classification
    E. A. Neeba, S. Koteeswaran, N. Malarvizhi
    Journal of Supercomputing, 2020
  • Sentiment polarity classification using conjure of genetic algorithm and differential evolution methods for optimized feature selection
    Jeevanandam Jotheeswaran, S. Koteeswaran
    Recent Advances in Computer Science and Communications, 2020
  • Bacterial foraging information swarm optimizer for detecting affective and informative content in medical blogs
    E. A. Neeba, S. Koteeswaran
    Cluster Computing, 2019
  • 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, 2019
  • 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
  • Identification and classification of best spreader in the domain of interest over the social networks
    A. N. Arularasan, A. Suresh, Koteeswaran Seerangan
    Cluster Computing, 2019
  • An integrated approach for network traffic analysis using unsupervised clustering and supervised classification
    Kothandapani Chokkanathan, S. Koteeswaran
    International Journal of Internet Technology and Secured Transactions, 2019
  • Dismemberment of metaphors with grid scratch via kernel k-Means
    S Ravikumar, K. Antony Kumar, S Koteeswaran
    Journal of Computational and Theoretical Nanoscience, 2018
  • Internet of things (IoT): A study on key elements, protocols, application, research challenges, and fog computing
    P. Suresh, S. Koteeswaran, N. Malarvizhi, R. H. Aswathy
    Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, 2018
  • A study on flow based classification models using machine learning techniques
    K. Chokkanathan, S. Koteeswaran
    International Journal of Intelligent Systems Technologies and Applications, 2018
  • Conclave of Internet of Things for smart applications: A concise review
    International Journal of Engineering and Technology Uae, 2018
  • A study on machine learning: Elements, characteristics and algorithms
    International Journal of Engineering and Technology Uae, 2018
  • Privacy protection and perfect classification nature of C4.5 algorithm
    K Chokkanathan, S Koteeswaran
    International Journal of Engineering and Technology Uae, 2018
  • 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
  • Mining medical opinions using hybrid genetic algorithm - Neural network
    Jeevanandam Jotheeswaran, S. Koteeswaran
    Journal of Medical Imaging and Health Informatics, 2016
  • Feature selection using swarms in parallel for classifying affective and informative content
    Iioab Journal, 2016
  • A weighted semantic feature expansion using hyponymy tree for feature integration in sentiment analysis
    Jeevanandam Jotheeswaran, S. Koteeswaran
    Proceedings of the 2015 International Conference on Green Computing and Internet of Things Icgciot 2015, 2016
  • Classification of information hubs based on the interest to maximize viral marketing in social networks
    Asian Journal of Information Technology, 2016
  • A study on influential evaluation of information hubs in social networks
    A. N. Arularasan, S. Koteeswaran
    Indian Journal of Science and Technology, 2016
  • Feature selection using random forestmethod for sentiment analysis
    Jeevanandam Jotheeswaran, S. Koteeswaran
    Indian Journal of Science and Technology, 2016
  • Artificial bee colony with map reducing technique for solving resource problems in clouds
    K. Silambarasan, S. Ambareesh, S. Koteeswaran
    Indian Journal of Science and Technology, 2016
  • Implementation of cloud based electronic health record (EHR) for Indian healthcare needs
    R. Kavitha, E. Kannan, S. Kotteswaran
    Indian Journal of Science and Technology, 2016
  • Decision tree based feature selection and multilayer perceptron for sentiment analysis
    Arpn Journal of Engineering and Applied Sciences, 2015
  • Enhancing JS- MR based data visualisation using YARN
    S. Koteeswaran, P. Visu, E. Kannan
    Indian Journal of Science and Technology, 2015
  • Virtual mining model for classifying text using unsupervised learning
    Koteeswaran
    American Journal of Applied Sciences, 2014
  • Region specific election routing protocol for wireless sensor networks
    Indian Journal of Science and Technology, 2014
  • HADOOP+big data: Analytics using series queue with blocking model
    S. Koteeswaran, P. Visu, K. Silambarasan, R. Vimal Karthick
    Research Journal of Applied Sciences Engineering and Technology, 2014
  • Expertised string mining in outsized databases and hefty files
    K. Geetha Rani, Shobhanjaly P. Nair, P. Visu, S. Koteeswaran
    Research Journal of Applied Sciences Engineering and Technology, 2014
  • Application of a quantitative algorithm for green computing
    International Journal of Applied Engineering Research, 2014
  • Analysis of bilateral intelligence (ABI) for textual pattern learning
    S. Koteeswara, E. Kannan
    Information Technology Journal, 2013
  • Significant term list based metadata conceptual mining model for effective text clustering
    T.
    Journal of Computer Science, 2012
  • Optimal energy management in wireless adhoc network using Artificial Bee Colony based routing protocol
    European Journal of Scientific Research, 2012
  • Terrorist intrusion monitoring system using outlier analysis based search knight algorithm
    European Journal of Scientific Research, 2012
  • Artificial Bee Colony based Energy Aware and Energy Efficient Routing Protocol
    F.
    Journal of Computer Science, 2012
  • A review on clustering and outlier analysis techniques in datamining
    Fabiane
    American Journal of Applied Sciences, 2012
  • Simulation of broadcasting algorithm using neighbor information in mobile ad hoc networks
    N. Punnagai, K. Ayarpadi, C. Leena, S. Koteeswaran
    Iet Conference Publications, 2011
  • Security in multicast mobile ad-hoc networks
    P. Visu, W.T. Chembian, S. Koteeswaran
    2009 1st International Conference on Advanced Computing Icac 2009, 2009

RECENT SCHOLAR PUBLICATIONS

  • 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