His contribution to academic research is significant, with more than 35 refereed and indexed publications in reputed international journals such as Web of Science, ESCI, Scopus, Springer, and Elsevier’s Engineering Index (Compendex). He has authored three books, published two patents, and produced multiple works bearing ISBNs and DOIs, underlining his commitment to knowledge creation and innovation. As a prolific trainer, he has delivered over 179 workshops, faculty development programs, and seminars across domains including Information Technology, Human Resource Management, E-Business, and Soft Skills. His sessions are widely acknowledged for their practical insights and transformative impact. Since 2019, he has also been serving as an Empanelled Civil Instructor for Officer Cadets at the Officers Training Academy (OTA), Chennai, contributing to the shaping of military leadership development.
EDUCATION
Prof. Dr. S. Magesh earned his M.Tech with Honours in Computer Science and Engineering and his Ph.D. in Artificial Intelligence from Dr. M.G.R. Educational and Research Institute, Chennai, India. In addition to these achievements, he holds multifarious postgraduate degrees in Arts, Linguistics, Science, and Management, which reflect his wide-ranging multidisciplinary expertise. He is a distinguished technocrat, academician, publisher, global trainer, and life coach with a professional journey spanning over 28 years, seamlessly integrating academia and the corporate sector. Beginning his teaching career in 2000 as a Lecturer, he rose through the ranks of Assistant Professor, Associate Professor, Professor, and Head of the Department of Computer Science and Engineering, serving in several prestigious engineering institutions and universities across Tamil Nadu.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Management Information Systems, Multidisciplinary
Supply chain optimization-based drug polymer analysis using AI model for synthesis and characterization Lubin Balasubramanian, G. Balamurugan, Nagamany Abirami, U. Muruganantham, S. Magesh, R. Manikandan Applications of Artificial Intelligence in Pharmaceuticals, 2025 Research on drug creation and development is crucial for chemical scientists and pharmaceutical businesses. On the other hand, drug design and discovery are hampered by limited efficacy, off-target delivery, time consumption, and excessive cost. Using the available three-dimensional structures, molecular docking can be utilised to anticipate the strength of the binding of small-molecule binders and their chemical derivatives to a macromolecular target. In process of finding as well as developing new drugs, artificial intelligence as well as machine learning technologies are essential. This chapter proposes novel technique in drug polymer-based synthesis and characterisation analysis with supply chain optimisation using artificial intelligence with machine learning algorithm. Then, the supply chain optimisation is based on drug synthesis and structure analysis using particle adversarial reinforcement Markov binary optimisation model. The experimental analysis has been carried out for various drug-polymer structure datasets in terms of prediction accuracy, random precision, and Recall.
Metaverse security monitoring based on virtual environment analysis using machine learning techniques Sheryl Oliver, A. Chinnasamy, P. Varun, N. Manikandan, S. Magesh, R. Manikandan Navigating AI and the Metaverse in Scientific Research, 2025 A NextG Internet platform allows users to participate in many kinds of virtual events and communicate with avatars in a 3D virtual environment to perform many activities in this environment. Building an intrusion detection system is computationally challenging in the Metaverse because of its interactive nature as well as large number of user interactions that take place within virtual settings. This research proposes novel techniques in a Metaverse-based virtual environment in security monitoring using machine learning techniques. Here, security monitoring was carried out using reinforcement-federated regressive Gaussian neural networks. The metaverse virtual environment has been deployed, and its analysis is carried out using a cloud edge network with virtual software-defined infrastructure. Experimental analysis is carried out in terms of scalability, quality of service, latency, accuracy, and network integrity. The proposed model attained a scalability of 94%, a quality of service of 95%, an accuracy of 97%, a latency of 96%, and a network integrity of 93%.
Robotic Process Automation Streamlining Business Processes for Operational Excellence Magesh Sankar, Vijayalakshmi Sivaramakrishnan, Palamadai Subramanian Rajakumar, S. Geetha, R. Manikandan Artificial Intelligence Machine Learning and Iot for Smart Business Management, 2025 In the contemporary economic landscape, companies are required to comprehend complicated processes in detail, increase the effectiveness of resource utilization, and enhance the along without losing the overall performance capacity that has been built. Setting aside rigid systems, they do not scale well as they are unable to understand and control the changing workloads, which leads to crowded systems, overuse of electricity, and wastage of resources. This study suggests a sophisticated RPA operational management that seamlessly integrates advanced machine learning (ML) and reinforcement learning technology in operation to produce an ever-evolving set of intelligent practices. The development of configured task scheduling, dynamic resource provision, and system usage management are all targeted by the system to allow expansion and adaptability in working environments on a real-time basis. The model performance and prediction accuracy in complex environments are achieved by two new feature engineering techniques: adaptive particle swarm optimization (APSO) and opti-filter XGBoost. Moreover, latency is decreased and energy economy is improved by incorporating State-Action-Reward-State-Action (SARSA)-Tabu search-based reinforcement learning, which in turn enhances autonomous resource management operations. For control and monitoring purposes, the framework uses the autoregressive integrated moving average-whale optimization algorithm (ARIMA-WOA). This ensures efficient and accurate system supervision. The effectiveness of the proposed solution is demonstrated together with performance of quantitative metrics of interest, such as task execution time, resource consumption, and total throughput within specified threshold values. This research presents a practical solution that is scalable, intelligent, and flexible enough to implement rapid transformations in the organization, improve efficiency, and automate business processes.
Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning K. Rajkumar, Prassanna Jayachandran, Kannan Chakrapani, S. Magesh, R. Manikandan Automated Machine Learning and Industrial Applications, 2025 Science and technological advancements encourage ongoing improvements in consumer finance, but they also introduce some financial credit hazards. Financial credit risk is getting harder and harder to manage, especially with the ongoing expansion of Internet banking. This highlights the need for the creation and assessment of a unique, all-encompassing machine learning approach that incorporates the study of missing data and predictor evaluation into the distress prediction process. This research proposes a novel technique in financial risk prediction with banking monitoring for cyber security detection using federated learning and automated machine learning. Here, users’ banking is analyzed for cyber security detection using Gaussian Encoder Belief Network (GEBN). Financial credit risk analysis is conducted using metaheuristic autoregressive kernel principal swarm optimization model (MAPSO). The experimental analysis evaluates accuracy, mean average precision, recall, F-measure, and normalized error. Data from specific examples are used to analyze the model's performance. The findings imply that the suggested model-based data mining technique may extract hidden information from the data while optimizing input variables.
Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture Kaavya Kanagaraj, A. Sheryl Oliver, V.P. Kavitha, S. Magesh, R. Manikandan Automated Machine Learning and Industrial Applications, 2025 Body sensor network (BSN), a monitoring system utilized in a healthcare setting based on Internet of Things (IoT) technology, consists of wearable or implanted devices. Due to the limited battery capacity and energy supply for sensors in BSN, extending the service cycle of the network is a significant challenge. Increasing energy efficiency and energy collection is essential for the network to remain sustainable. Operators are looking to automate network diagnosis and management using machine learning (ML) to operate complex optical communication networks cost effectively. This research proposes a novel, sustainable, network-secure data transmission technique based on automated machine learning (AutoML). Here, the healthcare network monitoring uses a Reinforcement Bayesian Regressive Vector Machine. The secure data transmission uses quantum blockchain automated transfer machine graph learning. The experimental analysis of throughput, scalability, packet delivery ratio, and data integrity is carried out. New functionalities are needed to enable cognitive, autonomous management of optical network security to achieve these goals.
Incorporation of computer vision and metaverse analysis using UAV communications for healthcare applications Mukunth Madavan, Akshay Kumar R., Akshay Bhuvaneswari Ramakrishnan, Manikandan R., S. Magesh Ubiquitous Computing and Technological Innovation for Universal Healthcare, 2024 The integration of computer vision, unmanned aerial vehicles (UAVs), and metaverse analysis has potential to transform healthcare and offers solutions to geographical challenges. Emphasizing real-world applications, it details how computer vision aids in real-time patient monitoring and disease detection, while the metaverse enables immersive medical simulations and remote patient monitoring. Unmanned aerial vehicles help break the geographical barriers and give people access to healthcare services. The synergy between computer vision and metaverse analysis facilitates revolutionary data analysis and has multiple applications. Augmented reality (AR) and virtual reality (VR) tools enhance user engagement, enabling remote patient monitoring and medical simulations. The integration of metaverse analysis with UAVs introduces applications such as remote operation, telemedicine, propelling healthcare into a new era.
Image analysis and data processing for COVID-19 Ambeshwar Kumar, R. Manikandan, S. Magesh, Rizwan Patan, S. Ramesh, Deepak Gupta Data Science for Covid 19 Volume 1 Computational Perspectives, 2021
Community and Stakeholder Engagement in Human-AI Learning Ecosystems S Magesh, S Vijayalakshmi, V Nivedha, PS Rajakumar, R Manikandan Collaborative Paradigms of Generative AI and Human Intelligence for … , 2026 2026
Designing Inclusive Learning Experiences With Generative AI S Sriharshaa, AB Ramakrishnan, S Srijanani, R Manikandan, S Magesh Collaborative Paradigms of Generative AI and Human Intelligence for … , 2026 2026
Fog-IoT architecture with blockchain integrated model for electronic health records in healthcare systems S Magesh, S Varadhan, KAS Stephen, VBR Amalarajan, A Benita Advances in Fog Computing and the Internet of Things for Smart Healthcare … , 2026 2026
A smart internet of medical things healthcare framework using machine learning classifier in fog processing system S Magesh, A Vijayaraghavan, SI Kalilulah, D Anandan, P Malathi Advances in Fog Computing and the Internet of Things for Smart Healthcare … , 2026 2026
Deep Learning Algorithm Based Pregnant Women Heart Disease Prediction using Heart Beats SM P.S. Rajakumar International Journal of System of Systems Engineering, www.doi.org/10.1504 … , 2026 2026
Supply Chain Optimization-based Drug Polymer Analysis Using AI Model for Synthesis and Characterization L Balasubramanian, G Balamurugan, N Abirami, U Muruganantham, ... Applications of Artificial Intelligence in Pharmaceuticals, 195-214 , 2026 2026
Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions M Harishmaa, S Janani, KA Jayashree, JR Sherin, R Manikandan, ... Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex … , 2025 2025
Exploring precision agriculture: Employing Grad-CAM for deep neural network in cotton image detection and segmentation with XAI S Vidivelli, R Manikandan, S Magesh, J Cho, SV Easwaramoorthy AIP Conference Proceedings 3335 (1), 030009 , 2025 2025 Citations: 1
Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning K Rajkumar, P Jayachandran, K Chakrapani, S Magesh, R Manikandan Automated Machine Learning and Industrial Applications, 171-190 , 2025 2025
Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture K Kanagaraj, AS Oliver, VP Kavitha, S Magesh, R Manikandan Automated Machine Learning and Industrial Applications, 17-39 , 2025 2025 Citations: 1
Neuro Imaging-Based Alzheimer’s Disease Detection Using Generative Adversarial Model with Deep Learning Algorithm J Vijayaraj, B Satheesh Kumar, M Umapathy, R Manikandan, S Magesh Adversarial Deep Generative Techniques for Early Diagnosis of Neurological … , 2025 2025
Neuro Imaging-Based Alzheimer Disease Detection by Segmentation with Classification Using Machine Learning Algorithms S Oliver, N Manikandan, SV Shri Bharathi, R Jayaraj, S Magesh, ... Adversarial Deep Generative Techniques for Early Diagnosis of Neurological … , 2025 2025
Polymer Analysis Using Al Model for Synthesis G Balamurugan, N Abirami, U Muruganantham, S Magesh Applications of Artificial Intelligence in Pharmaceuticals, 195 , 2025 2025
IoT sensors for smart cities and business transactions to daily tasks for data analytics algorithms RR Prabhakaran, K Subramani, L Jabsheela, S Magesh, SH Charan AIP Conference Proceedings 3257 (1), 020161 , 2025 2025
Metaverse Security Monitoring Based on Virtual Environment Analysis Using Machine Learning Techniques S Oliver, A Chinnasamy, P Varun, N Manikandan, S Magesh, ... Navigating AI and the Metaverse in Scientific Research, 337-362 , 2025 2025
Digital Task Optimisation with Resource Allocation in Business Process Management Using Machine Learning Model KK Ravindran, TL Kumari, S Magesh, V Sivaramakrishnan, ... Intelligent Computing and Optimization for Sustainable Development, 55-72 , 2024 2024
UAV Communication for Various Learning Approaches in Metaverse Healthcare Analysis Using Cloud Computing AB Ramakrishnan, S Srijanani, M Madavan, R Manikandan, S Magesh Ubiquitous Computing and Technological Innovation for Universal Healthcare … , 2024 2024
Incorporation of Computer Vision and Metaverse Analysis Using UAV Communications for Healthcare Applications M Madavan, A Kumar, AB Ramakrishnan, S Magesh Ubiquitous Computing and Technological Innovation for Universal Healthcare … , 2024 2024 Citations: 2
Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals S Magesh, PS Rajakumar Measurement: Sensors 25, 100631 , 2023 2023 Citations: 29
Fetal heart disease detection via deep reg network based on ultrasound images S Magesh, PS RajaKumar J. Appl. Eng. Technol. Sci 5, 439-450 , 2023 2023 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
& Rajesh, M.(2021) R Kumar, F Al-Turjman, L Anand, A Kumar, S Magesh, K Vengatesan Genomic sequence analysis of lung infections using artificial intelligence … , 0 Citations: 105
Pervasive computing in the context of COVID-19 prediction with AI-based algorithms S Magesh, VR Niveditha, PS Rajakumar, L Natrayan International Journal of Pervasive Computing and Communications 16 (5), 477-487 , 2020 2020.0 Citations: 97
Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals S Magesh, PS Rajakumar Measurement: Sensors 25, 100631 , 2023 2023.0 Citations: 29
Automated irrigation system based on soil moisture using arduino A Kumar, S Magesh International Journal of Pure and Applied Mathematics 116 (21), 319-323 , 2017 2017.0 Citations: 27
Taylor Based Grey Wolf Optimization Algorithm (TGWOA) For Energy Aware Secure Routing Protocol R Rahim, S Murugan, S Priya, S Magesh, R Manikandan International Journal of Computer Networks and Applications (IJCNA) 7 (4 … , 2020 2020.0 Citations: 26
Genomic sequence analysis of lung infections using artificial intelligence technique R Kumar, F Al-Turjman, L Anand, A Kumar, S Magesh, K Vengatesan, ... Interdisciplinary Sciences: Computational Life Sciences 13 (2), 192-200 , 2021 2021.0 Citations: 22
Fine tuning smart manufacturing enterprise systems: a perspective of internet of things-based service-oriented architecture SM Nagarajan, V Muthukumaran, IS Beschi, S Magesh Handbook of Research on Innovations and Applications of AI, IoT, and … , 2021 2021.0 Citations: 16
Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors K KR, I M, N VR, S Magesh, G Magesh, S Marappan International Journal of Pervasive Computing and Communications 18 (4), 365-375 , 2022 2022.0 Citations: 13
Hypervisor based Mitigation Technique for Keylogger Spyware Attacks C Santwana, KS Aditya, S Magesh International Journal of Computer science and information technologies 5 (2 … , 2014 2014.0 Citations: 10
Emerging 5g iot smart system based on edge-to-cloud computing platform VR Niveditha, D Usha, PS Rajakumar, B Dwarakanath International Journal of e-Collaboration (IJeC) 17 (4), 122-131 , 2021 2021.0 Citations: 9
Concepts and Contributions of Edge Computing in Internet of Things (IoT): A Survey S. Magesh, J. Indumathi , Radha RamMohan. S, Niveditha V. R, P. Shanmuga Prabha International Journal of Computer Networks and Applications (IJCNA) 7 (5 … , 2020 2020.0 Citations: 8
Knowledge discovery from consumer behavior in electronic home appliances market in Chennai by using data mining techniques S Vijayalakshmi, V Mahalakshmi, S Magesh African Journal of Business Management 7 (34), 3332 , 2013 2013.0 Citations: 8
Fetal heart disease detection via deep reg network based on ultrasound images S Magesh, PS RajaKumar J. Appl. Eng. Technol. Sci 5, 439-450 , 2023 2023.0 Citations: 7
Pervasive computational model and wearable devices for prediction of respiratory symptoms in progression of COVID-19 J Dhanapal, B Narayanamurthy, V Shanmugam, A Gangadharan, ... International Journal of Pervasive Computing and Communications 16 (4), 371-381 , 2020 2020.0 Citations: 7
Hash tag based topic modelling techniques for twitter by tweet aggregation strategy K Nimala, S Magesh, R Thamizh Arasan J Adv Res Dyn Control Syst 10, 571-578 , 2018 2018.0 Citations: 6
Study on consumer buying behaviour towards selective electronic home appliances in Hyderabad city S Vijayalakshmi, V Mahalakshmi, S Magesh International Journal of Logistics & Supply Chain Management Perspectives 2 … , 2013 2013.0 Citations: 6
Image analysis and data processing for COVID-19 DG AmbeshwarKumar, R.Manikandan,S.Magesh,RizwanPatan,S.Ramesh Data Science for COVID-19 1, 413-427 , 2021 2021.0 Citations: 5
Evaluation of investor awareness on techniques used in stock trading before their investment B Vidhya, S Magesh International Journal of Engineering & Technology 7 (3.12), 98-107 , 2018 2018.0 Citations: 5
A Survey On Health Care Data Using Data Mining Techniques V Rogeith, S Magesh Int. J. Pure Appl. Math 117 (16), 665-672 , 2017 2017.0 Citations: 4
An Approach to Corporate Governance by an Individual’s Self Consciousness and Integrated Advancement S Magesh, S Srinivasalu, S Venkata Guru Prasad Scope International Journal of Science, Humanities, Management and … , 2015 2015.0 Citations: 4
