Dr.M.Kandan

@srmist.edu.in

Assistant Professor and Department of Computing Technologies
SRM Institute of Science and Technology, Deemed to be University, Kattankulathur, Chengalpattu, Tamil Nadu, India

Dr.M.Kandan


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https://researchid.co/drkandan

EDUCATION

Ph.D (IT)- Anna University, Chennai, July -2018
M.Tech (IT)- Sathyabama University, Chennai, April 2009
B.E (CSE)- Mailam Engineering College, Thindivanam, Affiliated to Madras University, Chennai , April -2004

RESEARCH INTERESTS

Cloud Computing, Distributed Computing, Machine Learning and Deep Learning
17

Scopus Publications

84

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Privacy and security challenges of explainable artificial intelligence in healthcare
    M. Kandan, M. Mutharasu, S. Ramesh, G. Nagarajan, Amit Kumar Tyagi
    Explainable AI in Clinical Practice Methods Applications and Implementation, 2026
  • RETRACTED: Privacy-preserving fuzzy commitment schemes for secure IoT device authentication
    M. Kandan, A. Durai Murugan, Gandikota Ramu, Gandikota Ramu, R.K. Gnanamurthy, Dibyahash Bordoloi, Swati Rawat, Murugesan, Pulicherla Siva Prasad
    Journal of Intelligent and Fuzzy Systems, 2025
    Privacy-Preserving Fuzzy Commitment Schemes (PPFCS) have emerged as a promising solution for secure Internet of Things (IoT) device authentication, addressing the critical need for privacy and security in the rapidly growing IoT ecosystem. This paper presents a novel PPFCS-based authentication mech anism that protects sensitive user data and ensures secure communication between IoT devices. The proposed scheme leverages error-correcting codes (ECC) and cryptographic hash functions to achieve reliable and efficient authentication. The PPFCS framework allows IoT devices to authenticate themselves without revealing their true identity, preventing unauthorized access and preserving users’ privacy. Furthermore, our PPFCS-based authentication mechanism is resilient against various attacks, such as replay, man-in-the-middle, and brute-force attacks, by incorporating secure random nonce generation and timely key updates. We provide extensive experimental results and comparative analysis, demonstrating that the proposed PPFCS significantly outperforms existing authentication schemes in terms of security, privacy, and computational efficiency. As a result, the PPFCS offers a viable and effective solution for ensuring secure and privacy-preserving IoT device authentication, mitigating the risks associated with unauthorized access and potential data breaches in the IoT ecosystem.
  • Quantum Neural Networks: An Overview
    Priyanga Subbiah, M. Kandan, N. Krishnaraj, Shaji. K.A. Theodore
    Quantum Computing the Future of Information Processing, 2025
    Quantum neural networks (QNNs) represent a cutting-edge approach to machine learning and artificial intelligence, harnessing the principles of quantum mechanics to process and analyze data in fundamentally new ways. Unlike classical neural networks, which operate on classical bits and are limited by the laws of classical physics, QNNs use quantum bits (qubits) and exploit quantum phenomena such as superposition and entanglement to achieve unprecedented computational power and efficiency. This chapter explores the principles and applications of QNNs, including their potential for solving complex optimisation problems, pattern recognition tasks, and quantum simulation tasks. We discuss various QNN architectures, training algorithms, and applications, highlighting their advantages and challenges compared to classical neural networks. Additionally, we explore the potential impact of QNNs on fields such as quantum computing, quantum communication, and quantum sensing, as well as their implications for society and the economy. Overall, this chapter provides insights into the emerging field of quantum neural networks and sets the stage for further research and innovation in this exciting area.
  • Qubit-Based Applications for Next Generation Society
    M. Kandan, Priyanga Subbiah, N. Krishnaraj, Shaji. K.A. Theodore
    Quantum Computing the Future of Information Processing, 2025
    A rapidly developing frontier in computer science, the potential applications of quantum computing hold great promise for revolutionary advances in many domains. Quantum computing, which follows the rules of quantum physics, may one day solve problems that traditional computers have failed miserably at. Several of the many uses of quantum computing are covered in this chapter. Optimisation, encryption, medicine development, materials science, and machine learning are only a few of these uses. By making use of quantum phenomena like entanglement and superposition, quantum algorithms may drastically reduce the time it takes to do computations in these domains. This has the potential to greatly improve how problems in the actual world are solved. In addition, the chapter delves into the current state of quantum computing technology, showcasing advancements in algorithm design, hardware development, and error correction. It concludes by examining the societal impacts of quantum computing and the consequences it may have in domains such as cybersecurity, healthcare, and finance. Generally speaking, this chapter provides an overview of the groundbreaking possibilities of quantum computing applications and establishes a framework for future study and development in this intriguing field.
  • Smart security system using hybrid system with IoT and machine learning: A security system human-based detection
    Kandan. M., J. Jayaganesh, Prithu Sarkar, Karimulla Syed, Kalangi Balasubramanyam, Mohit Tiwari
    Interdisciplinary Approaches to AI Internet of Everything and Machine Learning, 2024
    Smart security systems (SSS) in houses are growing increasingly prevalent as technological advances become more prevalent in everyday life. Lighting, temperature, security cameras, and gadgets are all controlled by a SSS in houses. The widespread adoption of Internet of Things (IoT) technologies in smart houses has altered the environment of connected devices, online activity, and data transfers in household broadband connections and the web overall. Current cyber assaults and hazards against IoT-based SSS in houses, on the other hand, have shown widespread hazards and dangers in IoT devices that encompass the layer of data connection protocols to service providers.
  • The impact of cloud computing on organisational agility and competitive advantage in management information systems
    M. Kandan, D. Srinivasarao, M. Kalyan Chakravarthi, S. Gnana Prasanna, David Raul Hurtado Tiza, Mohit Tiwari
    Interdisciplinary Approaches to AI Internet of Everything and Machine Learning, 2024
    In the context of management information systems (MIS), this study investigates the transformational impact of cloud computing on organizational agility and competitive advantage. This research gathered, processed, alongside analysed data from various sources by utilizing the capabilities of Google Colab. We revealed insights into the significant impacts of cloud adoption on MIS performance using text mining, sentiment analysis, statistical testing, including machine learning. Scalability, quick deployment, cost-effectiveness, and global reach are highlighted as important factors in organizational agility. In addition, the foundation of competitive advantage is access to cutting-edge technology, improved collaboration, data-driven insights, and quicker innovation cycles. This report highlights how important cloud technology is to the transformation of contemporary businesses.
  • Blood Donation System
    Chakravarthula Krishna Desik, Tadiboyina Durga Prasad, M. Kandan
    Aip Conference Proceedings, 2024
  • Enhanced Object Detection with Convolutional Neural Networks for Vehicle Detection
    Vijayvargiya Akshat, Saraswat Kush, M. Kandan
    Aip Conference Proceedings, 2024
  • Air quality forecasting-driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach
    M. Kandan, K. Jayasakthi Velmurugan, P. Gururama Senthilvel, N. Sathish Kumar
    Transactions on Emerging Telecommunications Technologies, 2024
    In recent times, air quality prediction is turned out to be one of the important research topics among research communities to prevent lives from negative health impacts. Random fluctuations of PM2.5 level brought about by frequent variations in meteorological factors create difficulties air pollution management. Forecasting the quality of air using time series data serves as a defense mechanism against threatening hazards by providing immense support to take preventive measures. Besides, handling dynamic real time workloads, forecasted by the prediction model requires appropriate computing resources to distribute workloads based on demands. To achieve this goal, this paper proposes a new Air Quality Prediction‐enabled Resource Allocation scheme for cloud‐based software services, which offers dynamic adjustment of resources based on workload demands with high energy efficiency. The proposed system is a two phase system that executes both air quality prediction and resource allocation processes consecutively. A new weighted average ensemble classifier is designed by combining support vector machine (SVM), artificial neural network (ANN), and gradient boosting machine (GBM) techniques to measure PM2.5 level on time series information of Beijing PM2.5 dataset. The system then dynamically allocates appropriate computing resources using crossover particle swarm optimization (CPSO) algorithm based on the forecasted results of PM2.5 level. This system has the potential to contribute significantly to reducing energy consumption and improving air quality in cities worldwide. The experimental results conducted to determine the efficiency of the proposed system in terms of different metrics proves that it achieves greater performance with less error functions for PM2.5 level prediction as well as minimizes energy consumption for resource allocation when compared with existing methods.
  • Air Quality Prediction in Smart Cities Using Regression Techniques
    Ritik Gupta, Kushal Khandal, M. Kandan
    Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024 Icnwc 2024, 2024
    Air pollution in smart cities presents a significant risk to public health and general welfare. Precise air quality forecasting is essential for successful pollution reduction and long-term urban growth. This study performs a comprehensive comparative analysis of air quality prediction utilizing four regression techniques: Random Forest regression, Linear regression, Decision Tree regression, and XGBoost regression. The study seeks to determine the best effective model by evaluating criteria such as R2 measurements and Mean Absolute Error.The results demonstrate that the XGBoost regression method surpasses competing algorithms in terms of efficiency and accuracy. Moreover, the use of cloud computing technologies has greatly enhanced the implementation speed of these tactics. Utilizing distributed computing resources allows for real-time air quality forecasts, facilitating quick decision-making and proactive measures to combat air pollution incidents.This work enhances air quality prediction methods in smart cities by highlighting the effectiveness of the XGBoost regression algorithm. The study highlights the crucial need of utilizing advanced regression techniques and cloud computing to improve the precision and effectiveness of air quality forecasts, aiding in proactive efforts to address air pollution and create healthier urban settings.
  • Digital Twin Applications in Healthcare Facilities Management
    M. Kandan, P. Naveen, G. Nagarajan, S. Janagiraman
    Artificial Intelligence Enabled Blockchain Technology and Digital Twin for Smart Hospitals, 2024
  • Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm for Cloud Computing
    Kandan Kandan, , , , , , M. Mutharasu, Siva Satya Sreedhar. P., S. Thenappan, G. Nagarajan
    Journal of Intelligent Systems and Internet of Things, 2024
  • Enhancing Earthquake Prediction through Hybrid Deep Learning Approach
    M. Kandan, Swagata Sarkar, Meenakshi Sharma, V. Rajeswari, P. Poonkuzhali, Srimathi S
    2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024
  • Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment
    M. Kandan, Anbazhagan Krishnamurthy, S. Arun Mozhi Selvi, Mohamed Yacin Sikkandar, Mohamed Abdelkader Aboamer, T. Tamilvizhi
    Journal of Supercomputing, 2022
  • Implementation of Crop Yield Forecasting System based on Climatic and Agricultural Parameters
    M. Kandan, Garapati Sravani Niharika, Mallula Jhansi Lakshmi, Kallakuri Manikanta, Korlepara Bhavith
    Proceedings 2021 IEEE International Conference on Intelligent Systems Smart and Green Technologies Icissgt 2021, 2021
  • Smart Voting System using Face Detection and Recognition Algorithms
    M. Kandan, Koppula Durga Devi, Kasani Durga Navya Sri, Nunna Ramya, Nunna Krishna Vamsi
    Proceedings 2021 IEEE International Conference on Intelligent Systems Smart and Green Technologies Icissgt 2021, 2021
  • AIS-DAG: Artificial immune system for directed acyclic graphs model based fair resource allocation for heterogeneous cloud computing
    Asian Journal of Information Technology, 2016

RECENT SCHOLAR PUBLICATIONS

  • Privacy and security challenges of explainable artificial intelligence in healthcare
    M Kandan, M Mutharasu, S Ramesh, G Nagarajan, AK Tyagi
    Explainable AI in Clinical Practice, 411-426 , 2026
    2026
  • RETRACTED: Privacy-preserving fuzzy commitment schemes for secure IoT device authentication
    M Kandan, A Durai Murugan, G Ramu, G Ramu, RK Gnanamurthy, ...
    Journal of Intelligent & Fuzzy Systems 49 (1_suppl), 345-353 , 2025
    2025
    Citations: 1
  • Digital twin applications in healthcare facilities management
    M Kandan, P Naveen, G Nagarajan, S Janagiraman
    Artificial Intelligence‐Enabled Blockchain Technology and Digital Twin for … , 2024
    2024
    Citations: 3
  • Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach
    M Kandan, K Jayasakthi Velmurugan, P Gururama Senthilvel, ...
    Transactions on Emerging Telecommunications Technologies 35 (2), e4898 , 2024
    2024
    Citations: 6
  • Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment
    M Kandan, A Krishnamurthy, SAM Selvi, MY Sikkandar, MA Aboamer, ...
    The Journal of Supercomputing 78 (7), 10176-10190 , 2022
    2022
    Citations: 45
  • Implementation of Crop Yield Forecasting System based on Climatic and Agricultural Parameters
    M Kandan, GS Niharika, MJ Lakshmi, K Manikanta, K Bhavith
    2021 IEEE International Conference on Intelligent Systems, Smart and Green … , 2021
    2021
    Citations: 6
  • Smart voting system using face detection and recognition algorithms
    M Kandan, KD Devi, KDN Sri, N Ramya, NK Vamsi
    2021 IEEE International Conference on Intelligent Systems, Smart and Green … , 2021
    2021
    Citations: 14
  • Optimum Resource Allocation Techniques for Enhancing Quality of Service Parameters in Cloud Environment
    M Kandan
    Springer, 831-839 , 2020
    2020
    Citations: 3
  • QRA:a Multi-Stage Framework for Improving Qos in Resource Allocation
    DRM M.Kandan
    Journal of Advanced Research in Dynamical and Control System 9 (5), 131-141 , 2017
    2017
  • AIS-DAG: Artificial Immune System for Directed Acyclic Graphs Model based Fair Resource Allocation for Heterogeneous Cloud Computing
    M Kandan
    Asian Journal of Information Technology 15 (19), 3673-3686 , 2016
    2016
  • Multi Agent Based Dynamic Resource Allocation In Cloud Environment for Improving Quality of Service
    DRM M.Kandan
    Australian Journal of Basic and Applied Science 9 (27), 340-347 , 2015
    2015
  • A framework for effective resource allocation in a distributed cloud environment
    M Kandan, R Manimegalai
    Int J Appl Eng Res 10 (87), 493-498 , 2015
    2015
    Citations: 3
  • Strategies for resource allocation in cloud computing: a review
    M Kandan, R Manimegalai
    Int J Appl Eng Res 10 (76), 1-76 , 2015
    2015
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment
    M Kandan, A Krishnamurthy, SAM Selvi, MY Sikkandar, MA Aboamer, ...
    The Journal of Supercomputing 78 (7), 10176-10190 , 2022
    2022
    Citations: 45
  • Smart voting system using face detection and recognition algorithms
    M Kandan, KD Devi, KDN Sri, N Ramya, NK Vamsi
    2021 IEEE International Conference on Intelligent Systems, Smart and Green … , 2021
    2021
    Citations: 14
  • Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach
    M Kandan, K Jayasakthi Velmurugan, P Gururama Senthilvel, ...
    Transactions on Emerging Telecommunications Technologies 35 (2), e4898 , 2024
    2024
    Citations: 6
  • Implementation of Crop Yield Forecasting System based on Climatic and Agricultural Parameters
    M Kandan, GS Niharika, MJ Lakshmi, K Manikanta, K Bhavith
    2021 IEEE International Conference on Intelligent Systems, Smart and Green … , 2021
    2021
    Citations: 6
  • Digital twin applications in healthcare facilities management
    M Kandan, P Naveen, G Nagarajan, S Janagiraman
    Artificial Intelligence‐Enabled Blockchain Technology and Digital Twin for … , 2024
    2024
    Citations: 3
  • Optimum Resource Allocation Techniques for Enhancing Quality of Service Parameters in Cloud Environment
    M Kandan
    Springer, 831-839 , 2020
    2020
    Citations: 3
  • A framework for effective resource allocation in a distributed cloud environment
    M Kandan, R Manimegalai
    Int J Appl Eng Res 10 (87), 493-498 , 2015
    2015
    Citations: 3
  • Strategies for resource allocation in cloud computing: a review
    M Kandan, R Manimegalai
    Int J Appl Eng Res 10 (76), 1-76 , 2015
    2015
    Citations: 3
  • RETRACTED: Privacy-preserving fuzzy commitment schemes for secure IoT device authentication
    M Kandan, A Durai Murugan, G Ramu, G Ramu, RK Gnanamurthy, ...
    Journal of Intelligent & Fuzzy Systems 49 (1_suppl), 345-353 , 2025
    2025
    Citations: 1
  • Privacy and security challenges of explainable artificial intelligence in healthcare
    M Kandan, M Mutharasu, S Ramesh, G Nagarajan, AK Tyagi
    Explainable AI in Clinical Practice, 411-426 , 2026
    2026
  • QRA:a Multi-Stage Framework for Improving Qos in Resource Allocation
    DRM M.Kandan
    Journal of Advanced Research in Dynamical and Control System 9 (5), 131-141 , 2017
    2017
  • AIS-DAG: Artificial Immune System for Directed Acyclic Graphs Model based Fair Resource Allocation for Heterogeneous Cloud Computing
    M Kandan
    Asian Journal of Information Technology 15 (19), 3673-3686 , 2016
    2016
  • Multi Agent Based Dynamic Resource Allocation In Cloud Environment for Improving Quality of Service
    DRM M.Kandan
    Australian Journal of Basic and Applied Science 9 (27), 340-347 , 2015
    2015

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

1. M.Kandan et al., “Internet of Things, Cloud, AI, and ML based 24*7 Drinking Water Supply Netwoks -Technological- Challenge for India”, Intellectual Property India, Indian Publication, 25th Feb 2022.