Dr.T.PARIMALAM

@nandhaarts.org

Associate Professor and Head, PG and Research Department of Computer Science
Nandha Arts and Science College(Autonomous)

Dr.T.PARIMALAM


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https://researchid.co/parimalam

EDUCATION

M.C.A.,M..,

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Networks and Communications, Computer Science Applications, Artificial Intelligence
6

Scopus Publications

12

Scholar Citations

2

Scholar h-index

Scopus Publications

  • Adaptive and Stress-Responsive Wearable Self-Defense System with Concealed Multi-Modal Actuation for Women Safety
    S. Ramesh, A. Kannammal, R. Kishore, J. Jenshya, P. Ramya, T. Parimalam
    2026 2nd International Conference on Intelligent Systems for Communication Iot and Security Iciscois 2026, 2026
    Passive alarm systems are insufficient to guarantee women's safety in emergency situations and therefore require intelligent wearables to provide instant, multilayered self-protection. This paper introduces a wearable system that integrates physiological stress monitoring, adaptive defense cascading, and hidden actuation. Biosensors (heart rate, skin conductance, accelerometer) track stress and abnormal movement. Defense can be initiated manually or automatically, progressing through alarm at high volume, pepper spray, and low-voltage electroshock. Modules are inserted unobtrusively into clothing, with simultaneous GPS notifications transmitted through the KAAVALAN app. Prototype testing under attack-like conditions indicated greater than 90 % accuracy in detecting stress, actuation response time of one second or less, and high user acceptability. The system presented here illustrates a realizable route to smart, multi-layered wearables that integrate countermeasures with contemporaneous notifications, providing more protective coverage than alarm-only solutions while satisfying considerations of usability, safety, and compliance.
  • Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification
    Jitendra Kumar Chaudhary, T. Parimalam, Faisal Yousef Alghayadh, Ismail Keshta, Mukesh Soni, Sheshang Degadwala
    Next Generation Computing and Information Systems Proceedings of the 2nd International Conference on Next Generation Computing and Information Systems Icngcis 2023, 2025
    In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis.
  • Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach
    B. Karthikeyani, D. Yuvaraj, T. Parimalam, P. Parthasarathi
    2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
    Facial Emotional Recognition (FER) in healthcare can help to monitoring the patient by analyzing the emotional state of the patient through various emotional states. In medical industry it can help to trace the emotional state changes and diagnosis the pattern which are mapped respective to the patient's history. Digital twin technology is transforming healthcare systems and improving patient care, clinical operations, predictive analytics, and training and simulation by integrating virtual simulations and emerging analytic mechanism through ML and DL techniques. By allocating resources, monitoring workflow, and providing individualized patient care, these algorithms reduce healthcare operations and assist in determining an individual's risk. Since FER has lot of technical challenges like illumination, obstruction issues also high implementation cost. To solve these issues, the FER system will embed with the digital twin technology to provide a better result from real time sensing cameras also analyzing the result and guiding for a better treatment of an individual. We propose a system of end to end framework which produces best results in minimum amount of time with maximum accuracy. This real time implementation will help all the healthcare centers/industry to study an individual's health status as well as early prediction of diseases with effective treatment steps.
  • Efficient Clustering Techniques for Web Services Clustering
    T Parimalam, K Meenakshi Sundaram
    2017 IEEE International Conference on Computational Intelligence and Computing Research Iccic 2017, 2018
    Web services (WS) is called composite or compound when its execution involves interactions with other WS to utilize their features. The service providers published the web services through the internet as independent software components that are fulfilling the requirements of customer request. Clustering is more necessary for efficient web service discovery and web service composition processes. Clustering process groups the similar type of web services. In this paper, efficient clustering methods such as k-means clustering, Hierarchical agglomerative clustering and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering are introduced for web service clustering. The k-means clustering is a kind of partitional clustering where the web pages are divided into subsets with no hierarchy defined over them and the hierarchical agglomerative clustering is a type of hierarchical clustering where the web pages are arranged in tree structure in which leaves represents the data points and nodes denotes the clusters. BIRCH is an integrated hierarchical clustering algorithm uses the clustering features and cluster feature tree for general cluster description. Based on these clustering methods, web pages are clustered which are used for web service discovery and web service composition. The experiments are conducted on number of web services and the efficiency is evaluated in terms of accuracy, precision, recall and run time.
  • A multi metric optimized clustering for matching and ranking of web services
    Arpn Journal of Engineering and Applied Sciences, 2018
  • PSO-inspired BIRCH and improved bipartite graph for automatic web service composition
    International Journal of Applied Engineering Research, 2017

RECENT SCHOLAR PUBLICATIONS

  • Adaptive and Stress-Responsive Wearable Self-Defense System with Concealed Multi-Modal Actuation for Women Safety
    S Ramesh, A Kannammal, R Kishore, J Jenshya, P Ramya, T Parimalam
    2026 Second International Conference on Intelligent Systems for … , 2026
    2026.0
  • Artificial Intelligence and Machine Learning in Sports
    RD T.Parimalam
    Emerging Trends in AI and Machine Learning (ETAIML-2k25) , 2025
    2025.0
  • A novel Approach for Ovarian Cancer classification using Hybrid convolutional Neural Network with Probablistic classifier
    DTPP Thenmozhi
    Intelligent automation & Next Gen Innovations , 2025
    2025.0
  • Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach
    DTP B.Karthikeyani, D.Yuvaraj
    IEEE International Conference on Advances in Information Technology (ICAIT-24) , 2024
    2024.0
  • Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification
    S Chaudhary, J.K., Parimalam, T., Alghayadh, F.Y., Keshta, I., Soni, M ...
    Next Generation Computing and Information Systems - Proceedings of the 2nd … , 2023
    2023.0
    Citations: 1
  • AN IMPROVED BIG SERVICE COMPOSITION BASED ON SERVICE MATCHING AND SERVICE STITCHING
    DPNN Dr.T.Parimalam,Dr.D.Rajakumari
    Madhya Bharti 82 (17), 97-103 , 2022
    2022.0
  • Analysis of Crop Yield Prediction’s Precision value using Deep Neural Networks with environmental change impact and uncertainty handling
    DTP Dr.M.Saranya*
    International Journal of Creative Research Thoughts 10 (2), 71-76 , 2022
    2022.0
    Citations: 1
  • ROLE OF ICT TOOLS IN EDUCATION DURING COVID PROS AND CONS
    SK Dr. T. PARIMALAM, Dr. D. RAJAKUMARI
    ANVESAK 51 (No.2(VIII)), 90-94 , 2021
    2021.0
  • The internet of Smart Clothing: A Review on Application of IoT in Manufacturing Smart Textile and Clothing
    P Ramya, T Parimalam, D Rajakumari, S Karthika
    Design Engineering, 569-579 , 2021
    2021.0
  • Application of IOT in Automation
    DT Parimalam
    International conference on DataScience and Information Ecosystem'21 , 2021
    2021.0
  • The internet of Smart Clothing: A Review on Application of IoT in Manufacturing Smart Textile and Clothing
    SK Dr. P. Ramya,Dr. T. Parimalam, Dr. D. Rajakumari
    Design Engineering, 563-579 , 2021
    2021.0
  • An Automatic Service Composition using Clustering Techniques
    DKMST Parimalam
    CiiT International Journal of Networking and Communication Engineering, 12 … , 2020
    2020.0
  • An Optimal Composition Plan Selection Using Multi Objective Particle Swarm Optimization
    T Parimalam, DK Meenakshi Sundaram
    International Journal of Computer Engineering and Technology 10 (1) , 2019
    2019.0
  • SECURE AWARE AND PRIVACY PRESERVING TECHNIQUES FOR BIG SERVICE COMPOSITION
    TPDKM Sundaram
    International Journal of Advances in Science Engineering and Technology 6 (4 … , 2018
    2018.0
  • Efficient clustering techniques for web services clustering
    T Parimalam, KM Sundaram
    2017 ieee international conference on computational intelligence and … , 2017
    2017.0
    Citations: 6
  • PSO-inspired BIRCH and Improved Bipartite Graph for Automatic Web Service Composition
    KM Sundaram, T Parimalam
    International Journal of Applied Engineering Research 12 (8), 1765-1771 , 2017
    2017.0
    Citations: 1
  • Detecting Duplicate Records-A Case Study
    T Parimalam, R Deepa, RN Devi, PY Devi
    International Journal of Scientific Research in Science, Engineering and … , 2015
    2015.0
    Citations: 2
  • A multi metric optimized clustering for matching and ranking of web services
    KM Sundaram, T Parimalam
    2006.0
    Citations: 1
  • Artificial Intelligence for Smart Cities and Urban Optimization
    T Parimalam, MK Hemalatha, MS Umamaheswari, MAS Priya
  • Applications of IOT in Automation
    M Santhiya
    Two Day International Conference on Data Science and Information Ecosystem … , 0

MOST CITED SCHOLAR PUBLICATIONS

  • Efficient clustering techniques for web services clustering
    T Parimalam, KM Sundaram
    2017 ieee international conference on computational intelligence and … , 2017
    2017.0
    Citations: 6
  • Detecting Duplicate Records-A Case Study
    T Parimalam, R Deepa, RN Devi, PY Devi
    International Journal of Scientific Research in Science, Engineering and … , 2015
    2015.0
    Citations: 2
  • Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification
    S Chaudhary, J.K., Parimalam, T., Alghayadh, F.Y., Keshta, I., Soni, M ...
    Next Generation Computing and Information Systems - Proceedings of the 2nd … , 2023
    2023.0
    Citations: 1
  • Analysis of Crop Yield Prediction’s Precision value using Deep Neural Networks with environmental change impact and uncertainty handling
    DTP Dr.M.Saranya*
    International Journal of Creative Research Thoughts 10 (2), 71-76 , 2022
    2022.0
    Citations: 1
  • PSO-inspired BIRCH and Improved Bipartite Graph for Automatic Web Service Composition
    KM Sundaram, T Parimalam
    International Journal of Applied Engineering Research 12 (8), 1765-1771 , 2017
    2017.0
    Citations: 1
  • A multi metric optimized clustering for matching and ranking of web services
    KM Sundaram, T Parimalam
    2006.0
    Citations: 1
  • Adaptive and Stress-Responsive Wearable Self-Defense System with Concealed Multi-Modal Actuation for Women Safety
    S Ramesh, A Kannammal, R Kishore, J Jenshya, P Ramya, T Parimalam
    2026 Second International Conference on Intelligent Systems for … , 2026
    2026.0
  • Artificial Intelligence and Machine Learning in Sports
    RD T.Parimalam
    Emerging Trends in AI and Machine Learning (ETAIML-2k25) , 2025
    2025.0
  • A novel Approach for Ovarian Cancer classification using Hybrid convolutional Neural Network with Probablistic classifier
    DTPP Thenmozhi
    Intelligent automation & Next Gen Innovations , 2025
    2025.0
  • Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach
    DTP B.Karthikeyani, D.Yuvaraj
    IEEE International Conference on Advances in Information Technology (ICAIT-24) , 2024
    2024.0
  • AN IMPROVED BIG SERVICE COMPOSITION BASED ON SERVICE MATCHING AND SERVICE STITCHING
    DPNN Dr.T.Parimalam,Dr.D.Rajakumari
    Madhya Bharti 82 (17), 97-103 , 2022
    2022.0
  • ROLE OF ICT TOOLS IN EDUCATION DURING COVID PROS AND CONS
    SK Dr. T. PARIMALAM, Dr. D. RAJAKUMARI
    ANVESAK 51 (No.2(VIII)), 90-94 , 2021
    2021.0
  • The internet of Smart Clothing: A Review on Application of IoT in Manufacturing Smart Textile and Clothing
    P Ramya, T Parimalam, D Rajakumari, S Karthika
    Design Engineering, 569-579 , 2021
    2021.0
  • Application of IOT in Automation
    DT Parimalam
    International conference on DataScience and Information Ecosystem'21 , 2021
    2021.0
  • The internet of Smart Clothing: A Review on Application of IoT in Manufacturing Smart Textile and Clothing
    SK Dr. P. Ramya,Dr. T. Parimalam, Dr. D. Rajakumari
    Design Engineering, 563-579 , 2021
    2021.0
  • An Automatic Service Composition using Clustering Techniques
    DKMST Parimalam
    CiiT International Journal of Networking and Communication Engineering, 12 … , 2020
    2020.0
  • An Optimal Composition Plan Selection Using Multi Objective Particle Swarm Optimization
    T Parimalam, DK Meenakshi Sundaram
    International Journal of Computer Engineering and Technology 10 (1) , 2019
    2019.0
  • SECURE AWARE AND PRIVACY PRESERVING TECHNIQUES FOR BIG SERVICE COMPOSITION
    TPDKM Sundaram
    International Journal of Advances in Science Engineering and Technology 6 (4 … , 2018
    2018.0
  • Artificial Intelligence for Smart Cities and Urban Optimization
    T Parimalam, MK Hemalatha, MS Umamaheswari, MAS Priya
  • Applications of IOT in Automation
    M Santhiya
    Two Day International Conference on Data Science and Information Ecosystem … , 0