Arivoli Sundaramurthy

@ksriet.ac.in

Assistant Professor / Biomedical Engineering
K S R Institute for Engineering and Technology

Arivoli Sundaramurthy


               Download Compact PDF  
https://researchid.co/arivolisbme

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Engineering, Electrical and Electronic Engineering, Control and Systems Engineering
14

Scopus Publications

Scopus Publications

  • High impedance fault discrimination in microgrid power system using stacking ensemble approach
    Arangarajan Vinayagam, Raman Mohandas, Meyyappan Chindamani, Bhadravathi Gavirangapa Sujatha, Soumya Mishra, et al.
    International Journal of Applied Power Engineering, 2026
    High impedance (HI) faults in microgrid (MG) power systems are non-linear, intermittent, and have low fault current magnitudes, making them challenging to detect by typical protective systems. Consequently, it is imperative to implement a sophisticated protection system that is dependent on the precision of fault detection. In this study, a stacking ensemble classifier (SEC) is proposed to discriminate HI fault from other transients within a photovoltaic (PV) generated MG power system. The MG model is simulated with the introduction of faults and transients. The features of data set from event signals are generated using the discrete wavelet transform (DWT) technique. The dataset is used to train the individual classifiers (Naïve Bayes (NB), decision tree J48 (DTJ), and K-nearest neighbors (KNN)) at initial and meta learner in the final stage of SEC. The SEC outperforms other classification methods with respect to accuracy of classification, rate of success in detecting HI fault, and performance measures. The outcomes of the classification study conducted under standard test conditions (STC) of solar PV and the noisy environment of event signals clearly demonstrate that the SEC is more dependable and performs better than the individual base classification approaches.
  • Enhanced maximum power point estimation algorithm using quantum particle swarm optimization for solar photovoltaic micro inverter systems
    Ganesh Moorthy Jagadeesan, Arivoli Sundaramurthy, M. Vijayakumar, Birhanu Belete
    Energy Reports, 2025
  • IoT-Based Predictive Maintenance in Smart Electric Vehicles for Enhanced Reliability and Sustainability
    Arivoli Sundaramurthy, Bavithra Karunanithi, Karthikeyan B
    2025 2nd International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems Itech Secom 2025, 2025
  • Artificial Intelligence Enables Intelligent Biosensor for Microbial Analysis for Lung Health
    Sudharshan Putha, Swaroop Reddy Gayam, Praveen Thuniki, Arivoli Sundaramurthy, Sagar Dhanraj Pande, et al.
    Microorganisms for Sustainability, 2025
  • A Bio-Inspired Maximum Power Point Tracking Strategy Utilizing the Flower Pollination Algorithm for Improved Photovoltaic System Efficiency
    Sivakumar J, K. Balasubramanian, S. Kamachi, B. Thamizhkani, Arivoli Sundaramurthy, et al.
    International Conference on Electrical Energy Systems Icees, 2025
  • RETRACTION:Certain performance analysis of Islanded microgrid systems stability through biologically activated engineering optimization technique
    Karthikeyan Ramasamy, Arivoli Sundaramurthy, Chitra Vaithiyalingam
    Journal of Intelligent and Fuzzy Systems, 2024
    The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model’s particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable.
  • Enhancing Smart Grid Stability: Data-Driven Predictive Modeling in Distribution Systems
    Arivoli Sundaramurthy, Karthikeyan Ramasamy, Durgadevi Velusamy, Chitra Vaithiyalingam
    International Journal of Electrical and Electronics Research, 2024
    The system's ability to retain the equilibrium state during regular and under disturbance decides the power system stability. The power system stability is highly affected by continuous load variation, voltage variation, frequency variation, power flow variation, topology and the work environment. Hence the stability analysis is made to ensure the acceptable equilibrium state throughout the operation of the power system while meeting the demand. As there has been numerous inclusion of renewable energy sources into the electric network, there occurs challenge to maintain the equilibrium level of this decentralized supply with temporary needs. So to establish this kind of scenario, a Decentralized smart grid control (DSGC) is developed. In DSGC, demand is evaluated with supply through price information and the customers are allowed to decide on usage based on Pricing. The optimal hyperparameter tuning through grid search optimization for DSGC stability prediction is presented in this paper. The local frequency provides the details on equilibrium/power balance, to match supply with demand. Using an ensemble grid search optimization approach, we examine the power grid performance on dynamic stability. Our findings imply that DSGC stability is best predicted by ensemble gradient boost machine grid search with best R2 index performance and accuracy of 93.92%.
  • IOT Based-Smart CAP
    Bavithra K, Arivoli S, Akshayakannaa S, Dharshini S, Gokul M, et al.
    International Conference on Smart Systems for Electrical Electronics Communication and Computer Engineering Icsseec 2024 Proceedings, 2024
    The World Health Organization has stated that the number of human beings with visible impairments worldwide is 285 million. Of these, 9 million human beings are blind.1 Vision, being one of the vital human senses performs the maximum crucial position in human environmental perception. The biggest challenge for a person with vision and hearing impairment is to navigate around the world. A way to this hassle has been proposed. Smart Cap targets to assist human beings with such disabilities and assist them conquer regular challenges. The architecture of the proposed system consists of an Arduino UNO, an ultrasonic sensor to locate obstacles, and a vibration motor to alert deaf-blind human beings of obstacles. An advanced technology called talkie library which is a speech synthesizer is used to help the blind by giving voice commands. In case of emergency, the person's guardian may be alerted through the WiFi module.
  • Ethical Dimensions and Future Prospects of Artificial Intelligence in Decision Making Systems for Oncology: A Comprehensive Analysis and Reference Scheme
    Arivoli Sundaramurthy, Chitra Vaithiyalingam
    Proceedings 1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems Itech Secom 2023, 2023
    Artificial Intelligence (AI) has become a prevalent force in diverse medical domains, including image diagnostics, pathological categorization, treatment plan selection, and prognosis analysis. The collaboration between human and computer interactions has notably matured in the context of image-assisted cancer diagnosis. However, the ethical considerations associated with the incorporation of AI into clinical decision-making processes remain inadequately addressed. Consequently, the AI-driven Clinical Decision making System has not fully embraced interactions between humans and computers, particularly in the domain of image-supported diagnostic systems. This paper comprehensively reviews the global applications of the Clinical Decision Support System (CDSS) and delineates the fundamental principles guiding the incorporation of AI into CDSS. It scrutinizes the challenges faced by AI in oncology decision-making, shedding light on the existing ethical gaps. By presenting a thorough overview of the current landscape, this paper serves as a attribute framework for the future deployment of Artificial Intelligence in the field of oncology decision-making. As AI continues to progress, acknowledging and resolving ethical considerations becomes crucial for unlocking its full potential in enhancing clinical decision-making processes.
  • Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier
    Karthikeyan Ramasamy, Arivoli Sundaramurthy, Durgadevi Velusamy
    Automatika, 2023
    Smart Grid is an intelligent power grid with a bidirectional flow of electricity and information, that applies intelligent techniques to operate the grid autonomously near the stability limit. An intelligent technique is developed to identify and predict the abnormalities due to changes in customer behaviour and the unexpected disruption in the grid. A cost-sensitive stacked ensemble classifier (CS-SEC) is proposed for predicting the operations in smart grid that combines four cost-sensitive base classifiers, namely Extreme gradient boosting, Naive Bayes, Nu-support vector machine and Random forest at level-1 and the support vector machine as the meta classifier in level-2. The meta classifier uses the probability of prediction of the first-level classifiers with stratified 5-fold cross-validation to predict the decentralized smart grid stability. The proposed stacked ensemble classifier achieved an accuracy of 98.6% with specificity, recall and precision of 98.34%, 99.0% and 99.06%, respectively. Extensive experimental evaluation and results show that the proposed CS-SEC provides an accurate prediction of grid stability compared with other state-of-the-art models. The results reveal the robustness and competency of the proposed CS-SECs with optimized parameters.
  • Implementation of Suitable Control Strategy for Improved Dynamics in Hybrid Grid Connected System
    S. Arivoli, R. Karthikeyan, V. Chitra
    2021 International Conference on Computer Communication and Informatics Iccci 2021, 2021
  • Classification of human emotion using EP
    S. Arivoli, Vasanth Raj P.T., Harishkumar Harishkumar
    2020 International Conference on Computer Communication and Informatics Iccci 2020, 2020
  • Analysis of Double space time diversity-CDMA system for frequency-selective channel
    S. Arivoli, P.T. Vasanth Raj, A. Vijayaraj, Gowrishankar Senthilkumar
    2020 International Conference on Computer Communication and Informatics Iccci 2020, 2020
  • Zigbee based monitoring and control of melter process in sugar industry
    V. Saravanan, S. Arivoli, K. Valarmathi
    2014 International Conference on Electronics and Communication Systems Icecs 2014, 2014