Paramasivam

@sonatech.ac.in

Associate Professor, Electronics and Communication Engineering
Sona College of Technology

Paramasivam


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https://researchid.co/itsmesivam
38

Scopus Publications

513

Scholar Citations

11

Scholar h-index

14

Scholar i10-index

Scopus Publications

  • A HIGH-EFFICIENCY SEVEN-LEVEL INVERTER WITH SELF-BALANCED SWITCHED-CAPACITOR TOPOLOGY VALIDATED THROUGH PLECS SIMULATION AND EXPERIMENTAL SETUP
    Muthan Eswaran Paramasivam, P. Darwin, Supriya Sahu, Venkata Satya Durga Manohar Sahu, Subash Ranjan Kabat, Aiswarya Rajalaxmi, Anton Amala Praveen, Bijaya Kumar Mohapatra, Bibhu Prasad Ganthia
    Journal of Mechanics of Continua and Mathematical Sciences, 2025
    This research introduces a novel seven-level switched-capacitor inverter (SCI) topology designed to achieve high efficiency and reduced component count. The proposed SCI utilizes a DC input source, consisting of only twelve switches and two capacitors, to generate a seven-level output voltage. This topology stands out for its ability to self-balance capacitor voltages, resulting in reduced voltage stress on the switches and minimizing the need for complex external components such as a backend H-bridge. The proposed SCI is its ability to deliver a threefold increase in output voltage relative to the input, effectively boosting voltage without additional step-up transformers. The article provides a comprehensive comparison with existing SCI topologies, demonstrating the superior benefits of the proposed design, such as fewer components, lower cost, and enhanced performance. Both simulation results and experimental outcomes validate the efficacy of the suggested SCI in various operating conditions, confirming its potential for practical applications in power conversion systems. The laboratory test setup for the seven-level MLI prototype further corroborates the functionality and robustness of the proposed design. Utilizing PLECS simulation software, the performance of twelve semiconductor switches (S1 to S12) was evaluated in terms of their power dissipation characteristics. This novel topology presents significant advancements in multilevel inverter technology, offering improved efficiency and reliability for a wide range of applications, including renewable energy integration and electrical power distribution systems.
  • VisionGrip: Revolutionizing Motor Functionality in Carpal Tunnel Syndrome and Radial Nerve Palsy Patients Through EOG-Controlled Robotic Claw
    G. NirmalaPriya, M. E. Paramasivam, S. Prema, B. Roopa
    Lecture Notes in Electrical Engineering, 2025
  • Comparative Analysis of SwinFace, Hybrid Transformer-Sequencer, and Deep CNNs for Real-Time Gender and Age Prediction: Enhancing Accuracy and Sensitivity
    N. Nalini, S. Manju, M E Paramasivam, Sultan Alshourah, Ginni Nijhawan, M Dinesh
    2025 8th International Conference on Circuit Power and Computing Technologies Iccpct 2025, 2025
    The proposed novel algorithm utilizes linear regression in real-time facial image analysis for predicting both gender and age. By leveraging features extracted from facial data, the model aims to provide accurate and instantaneous predictions. The emergence of social media platforms has made the automatic classification of gender and age increasingly relevant. Real-time frame processing is possible with OpenCV. The expected gender and age are provided as the output, and this frame is provided as the input. The capacity to autonomously ascertain age and gender from facial images because of its versatility in facial analysis applications. However, the current models are still behind the required accuracy level, which is required for these models to be used in real-world applications, because of the significant intraclass variance of face images (such as difference in lighting, position, scale, and occlusion). In this work, a deep learning framework uses an ensemble of residual and attentional convolutional networks to reliably determine the gender and age group of facial photographs. By employing an attention mechanism, this model can concentrate on the important and instructive aspects of the face, increasing its prediction accuracy to $90.15 \%$.
  • Energy-Efficient Cloud Computing Through Reinforcement Learning-Based Workload Scheduling
    Ashwini R Malipatil, M E Paramasivam, Dilfuza Gulyamova, Aanandha Saravanan, Janjhyam Venkata Naga Ramesh, Elangovan Muniyandy, Refka Ghodhbani
    International Journal of Advanced Computer Science and Applications, 2025
    —The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (DRL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes Deep Q-Networks (DQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as Round Robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures.
  • Revolutionizing Road Safety: AI-Powered Road Defect Detection
    M E Paramasivam, Sutharshana Perumal, Hariharan Pathmanaban
    2024 3rd International Conference on Power Electronics and Iot Applications in Renewable Energy and Its Control Parc 2024, 2024
    The “Revolutionizing Road Safety: AI-Powered Road Defect Detection for Safer Roads” project aims to revolutionize road safety and infrastructure management by equipping patrolling vehicles with Line Scanner Cameras. These cameras enable real-time identification of road defects. This initiative addresses the labor-intensive and error-prone nature of manual defect detection in critical infrastructure. Natural disasters further compound this issue, necessitating extensive inspections for structural integrity. The integration of image processing techniques and machine learning methods offers a powerful solution, allowing for the analysis of captured images to discern potential defects. A comprehensive review of ten meticulously selected research articles spanning the past decade highlights One of the most encouraging automated methods for identifying cracks, emphasizing the potential of this AI-powered system to streamline road maintenance and repair efforts while bolstering road safety in worldwide.
  • Oral Cancer Detection Using Convolutional Neural Network
    M. E. Paramasivam, B. S. Sriganesh, S Sureshkrishna
    4th International Conference on Innovative Practices in Technology and Management 2024 Iciptm 2024, 2024
    Oral cancer poses a global health concern, affecting the mouth, throat, face, and oral glands. Histopathology images play a crucial role in diagnosing and predicting anomalies, yet human error remains a challenge in physical examinations. Deep learning algorithms offer advancements, aiding medical professionals by enhancing the accuracy of oral cancer identification from histopathology pictures. This study modifies three Convolutional Neural Network (CNN) architectures, including two based on DENSENET-121, to discern photos containing both oral cancer and healthy cells. The experiment focuses on two classes: normal and malignant cells, with a global incidence rate of 7 for malignancy, a prevalent form of head and neck cancer. Traditional oral squamous cell carcinoma (OSCC) diagnosis relies on time-consuming histological analysis, prone to human interpretation variations. Utilizing artificial intelligence techniques improves diagnostic accuracy, expediting precise diagnoses. This research aims to employ hybrid methodologies, leveraging fused characteristics to optimize early OSCC detection, addressing a critical need in global healthcare.
  • Smart Question Paper Generator Using Oracle Apex Framework
    M.E. Paramasivam, P. M. Dinesh, R. S. Sabeenian, P. William, R. Balamurugan, Manjunathan Alagarsamy
    4th International Conference on Innovative Practices in Technology and Management 2024 Iciptm 2024, 2024
    The Smart Question Paper Generator using Oracle APEX is a software application designed to streamline the process of generating question papers. It uses Oracle Application Express (APEX) as a development platform to create an intuitive and userfriendly interface that allows educators and examiners to easily create and generate question papers. The system utilizes a database of questions that can be sorted by topic, difficulty level, and type of question. With this information, the system can automatically generate a customized question paper based on the criteria specified by the user. The application saves time and effort, reduces errors, and ensures consistency in the quality of the question paper. Overall, the Smart Question Paper Generator using Oracle APEX is a powerful tool that simplifies the question paper generation process and increases the efficiency of educators and examiners.
  • Deep Learning-Based Prediction of Knee Innovative Practices
    P. M. Dinesh, Kanith Kumar Govindaraj, Nithi Manivannan, M. E. Paramasivam, P. William, Manjunathan Alagarsamy
    4th International Conference on Innovative Practices in Technology and Management 2024 Iciptm 2024, 2024
    This project presents a systematic framework employing advanced deep learning techniques for predicting knee osteoarthritis. Leveraging state-of-the-art models in image recognition, joint segmentation, and pathological analysis, our approach aims to streamline the diagnosis process. The integration of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) enhances the accuracy and efficiency of predicting knee osteoarthritis. This study represents a significant advancement in medical diagnostics, offering a valuable tool for healthcare professionals to predict and manage knee osteoarthritis, contributing to improved patient care and management.
  • Square root for perfect square numbers using Vedic mathematics
    Sabeenian Royappan Savarimuthu, Kalaiselvi Cinnu Muthuraji, Paramasivam Muthan Eswaran
    Aip Conference Proceedings, 2023
  • Daily report
    Hemashree Ramesh, Jayakrishnan Kaliyappan, Paramasivam Muthan Eswaran
    Aip Conference Proceedings, 2023
  • Noise Level Notifier
    Dinesh P.M, Yogesh Bala B, Manoj Kumar S, Sabeenian R.S, Paramasivam M.E, Manjunathan A
    E3s Web of Conferences, 2023
  • Smart Wearable Gadget for Miners Using IOT
    Sabeenian R.S, Kanishka K, Kavi Priya R, Dinesh P.M, Paramasivam M.E, Manjunathan A
    E3s Web of Conferences, 2023
  • IOT Based Real Time River Water Quality Monitoring and Control System
    Dinesh P.M, Shree Sapnaa K, Kiranisha A.J, Sabeenian R.S, Paramasivam M.E, Manjunathan A
    E3s Web of Conferences, 2023
  • Identification of Phishing Attacks using Machine Learning Algorithm
    Dinesh P.M, Mukesh M, Navaneethan B, Sabeenian R.S, Paramasivam M.E, Manjunathan A
    E3s Web of Conferences, 2023
  • IOT Based Smart Farming Application
    Dinesh P.M, Sabeenian R.S, Lokeshvar R.G, Paramasivam M.E, Thanish S, Manjunathan A
    E3s Web of Conferences, 2023
  • High-density salt & pepper noise removal using machine learning
    R. S. Sabeenian, M. E. Paramasivam, J. Akilandeswari, P. Iyyanar, A. Naveenkumar, A. Manjunathan
    Aip Conference Proceedings, 2023
  • RISC V Based Fault Diagnosis Architecture for Skinny Family of Block Ciphers
    R S Sabeenian, R Somes, A S Sri Surya, P M Dinesh, M E Paramasivam, Manjunathan Alagarsamy
    1st International Conference on Emerging Research in Computational Science Icercs 2023 Proceedings, 2023
  • Deep learning-based massive MIMO precoder under heavily noisy channel with flexible rate and power adaptation
    Suraya Mubeen, M. E. Paramasivam, D. Pradeep, S. Narendran, Zainulabedin Hasan Mohammed, Nalla Siva Kumar
    Soft Computing, 2023
  • Performing the classification of pulsation cardiac beats automatically by using CNN with various dimensions of kernels
    Manjunathan Alagarsamy, Joseph Michael Jerard Vedam, Nithyadevi Shanmugam, Paramasivam Muthan Eswaran, Gomathy Sankaraiyer, Kannadhasan Suriyan
    International Journal of Reconfigurable and Embedded Systems, 2022
  • Color-to-Grayscale Conversion for Images with Non-uniform Chromatic Distribution Using Multiple Regression
    M. E. Paramasivam, R. S. Sabeenian, P. M. Dinesh, R. Anand, Eldho Paul
    Eai Springer Innovations in Communication and Computing, 2022
  • GLCM Feature-Based Texture Image Classification Using Support Vector Machine
    R. Anand, T. Shanthi, R. S. Sabeenian, M. E. Paramasivam, K. Manju
    Eai Springer Innovations in Communication and Computing, 2022
  • Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people
    J. Akilandeswari, G. Jothi, A. Naveenkumar, R. S. Sabeenian, P. Iyyanar, M. E. Paramasivam
    Multimedia Tools and Applications, 2022
  • Deep Learning Algorithm for Identification of Ear Disease
    K. Manju, M. E. Paramasivam, S. Nagarjun, A. Mokesh, A. Abishek, K. Meialagan
    Lecture Notes in Networks and Systems, 2022
  • Robust fabric defects inspection system using deep learning architecture
    T. Shanthi, M. E. Paramasivam, C. Prakash, K. Manju, Eldho Paul, R. Anand, P. M. Dinesh, R. S. Sabeenian, D. Raja
    Journal of Testing and Evaluation, 2022
  • Voice support system using deep learning approaches for unilateral vocal cord paralyzed patients
    Chocko Valliappa, R. S. Sabeenian, M. E. Paramasivam, Eldho Paul, K. Manju, R. V. Pragadeesh
    International Journal of Noncommunicable Diseases, 2021
  • Detecting pulmonary embolism using deep neural networks
    J. Akilandeswaria, G. Jothib, A. Naveenkumara, R.S. Sabeenianc, P. Iyyanara, M.E Paramasivamc
    International Journal of Performability Engineering, 2021
  • An Investigation on Indoor Navigation Systems
    J. Akilandeswari, A. Naveenkumar, R. S. Sabeenian, P. Iyyanar, M. E. Paramasivam, G. Jothi
    Advances in Intelligent Systems and Computing, 2021
  • Efficient gold tree child items classification system using deep learning
    Dr. Sabeenian R.S.
    Journal of Advanced Research in Dynamical and Control Systems, 2020
  • A comparative study of feature detection techniques for navigation of visually impaired person in an indoor environment
    Akilandeswari Jeyapal, Jothi Ganesan, Sabeenian Royappan Savarimuthu, Iyyanar Perumal, Paramasivam Muthan Eswaran, Lakshmanan Subramanian, Naveenkumar Anbalagan
    Journal of Computational and Theoretical Nanoscience, 2020
  • Palm-leaf manuscript character recognition and classification using convolutional neural networks
    R. S. Sabeenian, M. E. Paramasivam, R. Anand, P. M. Dinesh
    Lecture Notes in Networks and Systems, 2019
  • Image contrast enhancement using particle swarm optimization
    Journal of Advanced Research in Dynamical and Control Systems, 2019
  • Classification of handwritten Tamil characters in palm leaf manuscripts using SVM based smart zoning strategies
    R. S. Sabeenian, M. E. Paramasivam, P. M. Dinesh, R. Adarsh, Gokul Ravi Kumar
    ACM International Conference Proceeding Series, 2017
  • Contrast based color plane selection for binarization of historical document images
    M. E. Paramasivam, R. S. Sabeenian
    Lecture Notes in Electrical Engineering, 2017
  • Appraisal of localized binarization methods on Tamil palm-leaf manuscripts
    Sabeenian R S, Paramasivam M E, Dinesh P M
    Proceedings of the 2016 IEEE International Conference on Wireless Communications Signal Processing and Networking Wispnet 2016, 2016
  • Face recognition using Gray Level Weight Matrix (GLWM)
    R. S. Sabeenian, M. E. Paramasivam, P. M. Dinesh
    Communications in Computer and Information Science, 2011
  • Handloom Silk Fabric Defect Detection Using First Order Statistical Features on a NIOS II Processor
    M. E. Paramasivam, R. S. Sabeenian
    Communications in Computer and Information Science, 2010
  • Defect detection and identification in textile fabrics using multi resolution combined statistical and spatial frequency method
    R.S. Sabeenian, M.E. Paramasivam
    2010 IEEE 2nd International Advance Computing Conference Iacc 2010, 2010
  • An efficient bit reduction binary multiplication algorithm using vedic methods
    M.E. Paramasivam, R.S. Sabeenian
    2010 IEEE 2nd International Advance Computing Conference Iacc 2010, 2010

RECENT SCHOLAR PUBLICATIONS

  • A programmable ALU with quantum dot cellular automata: A significantly improved fast speed multiplexer design
    G Narendra, G Srikanth, E Aparna, S Mishra, ME Paramasivam
    Emerging Technologies In Sustainable Innovation, Management and Development … , 2025
    2025
  • Analysis of a quick and adaptable IOT-based approach for roadside e-vehicle charging
    S Manikyala, KB Kumar, P Navitha, AS Reddy, ME Paramasivam, ...
    Emerging Technologies In Sustainable Innovation, Management and Development … , 2025
    2025
  • Preface: International Conference on Green Computing for Communication Technologies (ICGCCT-2024)
    RS Sabeenian, ME Paramasivam, PM Dinesh
    AIP Conference Proceedings 3279 (1), 010001 , 2025
    2025
  • VisionGrip: Revolutionizing Motor Functionality in Carpal Tunnel Syndrome and Radial Nerve Palsy Patients Through EOG-Controlled Robotic Claw
    G NirmalaPriya, ME Paramasivam, S Prema, B Roopa
    International Conference on Microelectronics, Electromagnetics and … , 2024
    2024
  • Revolutionizing road safety: AI-powered road defect detection
    ME Paramasivam, S Perumal, H Pathmanaban
    2024 3rd International conference on Power Electronics and IoT Applications … , 2024
    2024
    Citations: 6
  • Deep Learning-Based Prediction of Knee Innovative Practices
    PM Dinesh, KK Govindaraj, N Manivannan, ME Paramasivam, P William, ...
    2024 4th International Conference on Innovative Practices in Technology and … , 2024
    2024
    Citations: 5
  • Smart Question Paper Generator Using Oracle Apex Framework
    ME Paramasivam, PM Dinesh, RS Sabeenian, P William, R Balamurugan, ...
    2024 4th International Conference on Innovative Practices in Technology and … , 2024
    2024
    Citations: 5
  • Oral cancer detection using convolutional neural network
    ME Paramasivam, BS Sriganesh, S Sureshkrishna
    2024 4th International Conference on Innovative Practices in Technology and … , 2024
    2024
    Citations: 6
  • RISC V Based Fault Diagnosis Architecture for Skinny Family of Block Ciphers
    RS Sabeenian, R Somes, ASS Surya, PM Dinesh, ME Paramasivam, ...
    2023 International Conference on Emerging Research in Computational Science … , 2023
    2023
  • Preface: International Conference on Green Computing for Communication Technologies (ICGCCT-2022)
    RS Sabeenian, ME Paramasivam, R Anand, E Paul
    AIP Conference Proceedings 2857 (1), 010001 , 2023
    2023
  • IoT based smart farming application
    D PM, S RS, L RG, P ME, T S, M A
    E3S Web of Conferences 399, 04012 , 2023
    2023
    Citations: 12
  • RETRACTED ARTICLE: Deep learning-based massive MIMO precoder under heavily noisy channel with flexible rate and power adaptation: S. Mubeen et al.
    S Mubeen, ME Paramasivam, D Pradeep, S Narendran, ZH Mohammed, ...
    Soft Computing, 1-1 , 2023
    2023
    Citations: 18
  • Noise Level Notifier
    D PM, YB B, MK S, S RS, P ME, M A
    E3S Web of Conferences 399, 04011 , 2023
    2023
    Citations: 2
  • High-density salt & pepper noise removal using machine learning
    RS Sabeenian, ME Paramasivam, J Akilandeswari, P Iyyanar, ...
    AIP Conference Proceedings 2690 (1), 020049 , 2023
    2023
    Citations: 5
  • Square root for perfect square numbers using Vedic mathematics
    SR Savarimuthu, KC Muthuraji, PM Eswaran
    GREEN COMPUTING FOR COMMUNICATION TECHNOLOGIES 2857 (1) , 2023
    2023
    Citations: 7
  • IOT based real time river water quality monitoring and control system
    PM Dinesh, SK Shree, AJ Kiranisha, RS Sabeenian, ME Paramasivam, ...
    E3S Web of Conferences 399 , 2023
    2023
    Citations: 9
  • Smart wearable gadget for miners using IoT
    RS Sabeenian, K Kanishka, PR Kavi, PM Dinesh, ME Paramasivam, ...
    E3S Web of Conferences 399 , 2023
    2023
    Citations: 7
  • Identification of phishing attacks using machine learning algorithm
    PM Dinesh, M Mukesh, B Navaneethan, RS Sabeenian, ...
    E3S web of conferences 399, 04010 , 2023
    2023
    Citations: 25
  • Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people
    J Akilandeswari, G Jothi, A Naveenkumar, RS Sabeenian, P Iyyanar, ...
    Multimedia Tools and Applications 81 (3), 3483-3514 , 2022
    2022
    Citations: 50
  • GLCM feature-based texture image classification using support vector machine
    R Anand, T Shanthi, RS Sabeenian, ME Paramasivam, K Manju
    3rd EAI international conference on big data innovation for sustainable … , 2022
    2022
    Citations: 12

MOST CITED SCHOLAR PUBLICATIONS

  • An efficient bit reduction binary multiplication algorithm using vedic methods
    ME Paramasivam, RS Sabeenian
    2010 IEEE 2nd International Advance Computing Conference (IACC), 25-28 , 2010
    2010
    Citations: 76
  • Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people
    J Akilandeswari, G Jothi, A Naveenkumar, RS Sabeenian, P Iyyanar, ...
    Multimedia Tools and Applications 81 (3), 3483-3514 , 2022
    2022
    Citations: 50
  • Palm-leaf manuscript character recognition and classification using convolutional neural networks
    RS Sabeenian, ME Paramasivam, R Anand, PM Dinesh
    Computing and Network Sustainability: Proceedings of IRSCNS 2018, 397-404 , 2019
    2019
    Citations: 47
  • Detecting pulmonary embolism using deep neural networks
    J Akilandeswaria, G Jothib, A Naveenkumara, RS Sabeenianc, ...
    International Journal of Performability Engineering 17 (3), 322 , 2021
    2021
    Citations: 42
  • Defect detection and identification in textile fabrics using multi resolution combined statistical and spatial frequency method
    RS Sabeenian, ME Paramasivam
    2010 IEEE 2nd International Advance Computing Conference (IACC), 162-166 , 2010
    2010
    Citations: 27
  • Identification of phishing attacks using machine learning algorithm
    PM Dinesh, M Mukesh, B Navaneethan, RS Sabeenian, ...
    E3S web of conferences 399, 04010 , 2023
    2023
    Citations: 25
  • Computer vision based defect detection and identification in handloom silk fabrics
    RS Sabeenian, ME Paramasivam, PM Dinesh
    International Journal of Computer Applications 42 (17), 41-48 , 2012
    2012
    Citations: 25
  • RETRACTED ARTICLE: Deep learning-based massive MIMO precoder under heavily noisy channel with flexible rate and power adaptation: S. Mubeen et al.
    S Mubeen, ME Paramasivam, D Pradeep, S Narendran, ZH Mohammed, ...
    Soft Computing, 1-1 , 2023
    2023
    Citations: 18
  • Detection and location of defects in handloom cottage silk fabrics using MRMRFM & MRCSF
    RS Sabeenian, ME Paramasivam, PM Dinesh
    International Journal of Technology and Engineering System 2 (2), 172-176 , 2011
    2011
    Citations: 17
  • IoT based smart farming application
    D PM, S RS, L RG, P ME, T S, M A
    E3S Web of Conferences 399, 04012 , 2023
    2023
    Citations: 12
  • GLCM feature-based texture image classification using support vector machine
    R Anand, T Shanthi, RS Sabeenian, ME Paramasivam, K Manju
    3rd EAI international conference on big data innovation for sustainable … , 2022
    2022
    Citations: 12
  • Deep learning algorithm for identification of ear disease
    K Manju, ME Paramasivam, S Nagarjun, A Mokesh, A Abishek, ...
    Proceedings of International Conference on Data Science and Applications … , 2021
    2021
    Citations: 11
  • Fabric Defect Detection in Handlooms Cottage Silk Industries using Image Processing Techniques
    S R S, P M E, D P M
    International Journal of Computer Applications 58 (11), 21-29 , 2012
    2012
    Citations: 11
  • Appraisal of localized binarization methods on Tamil palm-leaf manuscripts
    S R S, P M E, D P M
    Wireless Communications, Signal Processing and Networking (WiSPNET … , 2016
    2016
    Citations: 10
  • IOT based real time river water quality monitoring and control system
    PM Dinesh, SK Shree, AJ Kiranisha, RS Sabeenian, ME Paramasivam, ...
    E3S Web of Conferences 399 , 2023
    2023
    Citations: 9
  • A Comparative Study of Feature Detection Techniques for Navigation of Visually Impaired Person in an Indoor Environment
    A Jeyapal, J Ganesan, SR Savarimuthu, I Perumal, PM Eswaran, ...
    Journal of Computational and Theoretical Nanoscience 17 (1), 21-26 , 2020
    2020
    Citations: 8
  • Computing and network sustainability
    RS Sabeenian, ME Paramasivam, R Anand, PM Dinesh, ...
    Springer, Singapore , 2019
    2019
    Citations: 8
  • Image Contrast Enhancement Using Particle Swarm Optimization
    E Paul, R An, PV Karthick, ME Paramasivam
    Journal of Advanced Research in Dynamic and Control Systems 11 (04–Special … , 2019
    2019
    Citations: 8
  • Square root for perfect square numbers using Vedic mathematics
    SR Savarimuthu, KC Muthuraji, PM Eswaran
    GREEN COMPUTING FOR COMMUNICATION TECHNOLOGIES 2857 (1) , 2023
    2023
    Citations: 7
  • Smart wearable gadget for miners using IoT
    RS Sabeenian, K Kanishka, PR Kavi, PM Dinesh, ME Paramasivam, ...
    E3S Web of Conferences 399 , 2023
    2023
    Citations: 7