Thanga Raj M

@kalasalingam.ac.in

Research Scholar, Electronics and Communication Engineering
Kalasalingam Academy of Research and Education

Thanga Raj M


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https://researchid.co/dr.thangaraj1127

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Engineering
5

Scopus Publications

24

Scholar Citations

3

Scholar h-index

Scopus Publications

  • DABiG: Breath pattern classification using the hybrid deep learning with optimal feature selection
    P Kaleeswari, R Ramalakshmi, T Arun Prasath, A Muthukumar, R Kottaimalai, M Thanga Raj
    Technology and Health Care, 2025
    Background A person's breathing pattern can be a reflection of their emotional and physical well-being because it shows the frequency, intensity, and rhythm of their breathing. Objective This research article presents a comprehensive approach to breathe pattern classification utilizing gyroscope and accelerometer readings obtained from individuals using two distinct sensors. The study encompasses the acquisition of six diverse breathing patterns, with a focus on data pre-processing through Min-Max normalization. Methods To select essential features from the normalized data, an innovative optimization algorithm, Adaptive Chimp Optimization (AdCO), is introduced. AdCO integrates an adaptive weighting strategy into the conventional Chimp optimization algorithm, enhancing convergence rates and enabling global optimal feature selection. Furthermore, the article introduces the application of the selected features in breath pattern classification using a hybrid deep learning mechanism, DABiG. DABiG leverages the Bidirectional Gated Recurrent Unit (BiGRU), a neural network architecture capable of processing sequential data bi-directionally. Results Spatial and temporal attention mechanisms are incorporated into DABiG to enhance its ability to focus on relevant spatial regions and time steps within the breath pattern data. Conclusion Spatial attention assigns weights to spatial regions, while temporal attention assigns weights to time steps, improving feature extraction and classification accuracy.
  • Correction to: Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121 (Journal of Ambient Intelligence and Humanized Computing, (2024), 15, 9, (3519-3531), 10.1007/s12652-024-04829-4)
    A. Muthukumar, M. Thanga Raj, R. Ramalakshmi, A. Meena, P. Kaleeswari
    Journal of Ambient Intelligence and Humanized Computing, 2024
  • Cloud-based Decentralized Smart Healthcare for Patient Monitoring on Deep Learning
    Sripriya Arunachalam, Shanthi H J, G Sivagurunathan, Shyamali Das, D. Anand, Thanga Raj M
    Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing Icaaic 2023, 2023
    Over the past few years, there has been a meteoric surge in the quantity of digital information available online for instantaneous sharing, persistent archiving, and inquiring. It has expanded the possibilities for using digital data that is both decentralised and ad hoc in order to make decisions quickly. At present, e-Healthcare is among the most sought-after sectors for EHR and telemedicine communication. Securing electronic health records (EHR) has become a topic of intense interest in recent years, with previous works employing a wide range of methods to better ensure the confidentiality and security of EHR at a reasonable price. There are a number of serious problems with the current research, including computational complexity, increased process time, information leakage, vulnerability to various assaults, scalability difficulty, etc. Clinical data analysis presents several difficulties, but disease prediction is one of the most significant ones. The suggested study aims to apply deep learning (DL) classification algorithms for disease prediction. A technique that utilises cloud computing, fog computing, and IoMT more recently has been presented for diagnosing illness. Fast DL classification analysis is performed in the fog layer. When compared to the alternative proposed model Bi-CNN, the healthcare model's efficiency in the Bi-LSTM simulation yields significantly better results: 97.31% of accuracy, 97.58% of recall, 96.90% of precision, 94.90% of F1-measure, 97.25% of specificity, and 94.80% of G-mean.
  • A Review on Machine Learning Models for Breathing Pattern Analysis of Soldiers
    Kaleeswari P, Ramalakshmi R, Arunprasath Thiyagarajan, Muthukumar Arunachalam, Kottaimalai Ramaraj, Thanga Raj M
    2023 International Conference on Energy Materials and Communication Engineering Icemce 2023, 2023
    Since 2001, the U.S. military has sent 2.7 million people to support missions in Afghanistan and Asia. The experience of land-based employees is increased by exposure to additional inhalational exposures and particulate matter from a variety of sources. For the purpose of preventing significant loss to the nation and to the individual military soldier, post-traumatic stress disorder (PTSD) must be identified. Breathing pattern analysis is a key method for detecting PTSD, and various studies have used machine learning techniques for this purpose. This survey examines multiple ML models to determine the military soldiers' breathing patterns in distinct works. This overview discusses several ML models and strategies used over the past few decades for conducting extensive research. Military personnel' breathing patterns are analyzed using a variety of datasets, statistical factors, and methodologies. The effectiveness of various algorithms is compared using qualitative as well as quantitative approaches. The potential future study areas with major challenges are discussed to reach a conclusion.
  • A Review on the Detection of Deep Fake and Propaganda Videos and Images-based Voice and Facial Manipulation using AI Techniques
    M Thanga Raj, Muthukumar Arunachalam, R Ramalakshmi, Kottaimalai Ramaraj, Meena Arunachalam, P Kaleeswari
    2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
    The videos or images based on deep fake video is the creation of artificial intelligence models namely deep and machine learning techniques to superimpose, replace, combine and merge images as well as clips of videos thereby generating a video as fake, which seems trustworthy. Without involving this consent, the explicit content is generated is Deep-fake videos. Recently, Artificial Intelligence (AI) model is widely addressing the exciting task of autonomous control, object detection, image processing and enlarge data analysis. During the past years, the videos, images and audio are the form of deep fakes. A deepfake determination desires an evolution of technology. A person’s face or voice is swapped with another one during deepfakes thereby providing higher accurate videos. A deepfake determination desires an evolution of technology. To detect face or voice manipulation, novel AI models namely ML and DL to solve the deepfake detection challenges. The main objective of this research is to provide existing studies related to the detection of deepfake video or images based on different techniques, benchmark dataset, and discussion and future directions.

RECENT SCHOLAR PUBLICATIONS

  • Aescin-Cellulose Nanocrystals for the Enhancement of Oxidative Damage, Mitochondrial Dysfunction, and Anti-migratory Potential in Cancer Cells
    R Seth, A Mathur, M Raj, A Meena
    Journal of Polymers and the Environment 34 (4), 93 , 2026
    2026
  • An explorative analysis of T cell activating drugs
    SS Salins, A Muthukumar, RM Thanga, P Kaleeswari
    A Study on Next-Generation Materials and Devices, 341-346 , 2025
    2025
  • Deep learning approaches for classification of abnormal respiratory signals
    P Kaleeswari, R Ramalakshmi, A Muthukumar, RM Thanga
    A Study on Next-Generation Materials and Devices, 284-288 , 2025
    2025
  • DABiG: breath pattern classification using the hybrid deep learning with optimal feature selection
    P Kaleeswari, R Ramalakshmi, T Arun Prasath, A Muthukumar, ...
    Technology and Health Care 33 (4), 1612-1625 , 2025
    2025
    Citations: 4
  • Robust Detection of Deepfake Images in Blockchain Systems Using Differential Privacy and Secure Multi-Party Computation
    MT Raj, A Muthukumar, M Arunachalam
    Sustainable Materials and Technologies in VLSI and Information Processing … , 2025
    2025
  • Adaptive zk-SNARKs: Cutting edge defense against image manipulation
    MT Raj, M Arunachalam
    Advances in Electrical and Computer Technologies, 562-569 , 2025
    2025
  • SIMPD net: A novel dataset for south Indian medicinal plants in natural conditions
    M Arunachalam, T Gopu, S Nathan, K Uma, V Srivatsav, M Thangaraj
    2024 IEEE Silchar Subsection Conference (SILCON 2024), 1-6 , 2024
    2024
    Citations: 2
  • Correction to: Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121
    A Muthukumar, M Thanga Raj, R Ramalakshmi, A Meena, P Kaleeswari
    Journal of Ambient Intelligence and Humanized Computing 15 (11), 3847-3847 , 2024
    2024
  • Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121
    A Muthukumar, MT Raj, R Ramalakshmi, A Meena, P Kaleeswari
    Journal of Ambient Intelligence and Humanized Computing 15 (9), 3519-3531 , 2024
    2024
    Citations: 8
  • A Review on Machine Learning Models for Breathing Pattern Analysis of Soldiers
    P Kaleeswari, R Ramalakshmi, M Thanga Raj
    2023 International Conference on Energy, Materials and Communication … , 2023
    2023
    Citations: 1
  • A review on the detection of deep fake and propaganda videos and images-based voice and facial manipulation using AI techniques
    MT Raj, M Arunachalam, R Ramalakshmi, K Ramaraj, M Arunachalam, ...
    2023 2nd International Conference on Automation, Computing and Renewable … , 2023
    2023
    Citations: 6
  • Cloud-based decentralized smart healthcare for patient monitoring on deep learning
    S Arunachalam, HJ Shanthi, G Sivagurunathan, S Das, D Anand
    2023 2nd International Conference on Applied Artificial Intelligence and … , 2023
    2023
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121
    A Muthukumar, MT Raj, R Ramalakshmi, A Meena, P Kaleeswari
    Journal of Ambient Intelligence and Humanized Computing 15 (9), 3519-3531 , 2024
    2024
    Citations: 8
  • A review on the detection of deep fake and propaganda videos and images-based voice and facial manipulation using AI techniques
    MT Raj, M Arunachalam, R Ramalakshmi, K Ramaraj, M Arunachalam, ...
    2023 2nd International Conference on Automation, Computing and Renewable … , 2023
    2023
    Citations: 6
  • DABiG: breath pattern classification using the hybrid deep learning with optimal feature selection
    P Kaleeswari, R Ramalakshmi, T Arun Prasath, A Muthukumar, ...
    Technology and Health Care 33 (4), 1612-1625 , 2025
    2025
    Citations: 4
  • Cloud-based decentralized smart healthcare for patient monitoring on deep learning
    S Arunachalam, HJ Shanthi, G Sivagurunathan, S Das, D Anand
    2023 2nd International Conference on Applied Artificial Intelligence and … , 2023
    2023
    Citations: 3
  • SIMPD net: A novel dataset for south Indian medicinal plants in natural conditions
    M Arunachalam, T Gopu, S Nathan, K Uma, V Srivatsav, M Thangaraj
    2024 IEEE Silchar Subsection Conference (SILCON 2024), 1-6 , 2024
    2024
    Citations: 2
  • A Review on Machine Learning Models for Breathing Pattern Analysis of Soldiers
    P Kaleeswari, R Ramalakshmi, M Thanga Raj
    2023 International Conference on Energy, Materials and Communication … , 2023
    2023
    Citations: 1
  • Aescin-Cellulose Nanocrystals for the Enhancement of Oxidative Damage, Mitochondrial Dysfunction, and Anti-migratory Potential in Cancer Cells
    R Seth, A Mathur, M Raj, A Meena
    Journal of Polymers and the Environment 34 (4), 93 , 2026
    2026
  • An explorative analysis of T cell activating drugs
    SS Salins, A Muthukumar, RM Thanga, P Kaleeswari
    A Study on Next-Generation Materials and Devices, 341-346 , 2025
    2025
  • Deep learning approaches for classification of abnormal respiratory signals
    P Kaleeswari, R Ramalakshmi, A Muthukumar, RM Thanga
    A Study on Next-Generation Materials and Devices, 284-288 , 2025
    2025
  • Robust Detection of Deepfake Images in Blockchain Systems Using Differential Privacy and Secure Multi-Party Computation
    MT Raj, A Muthukumar, M Arunachalam
    Sustainable Materials and Technologies in VLSI and Information Processing … , 2025
    2025
  • Adaptive zk-SNARKs: Cutting edge defense against image manipulation
    MT Raj, M Arunachalam
    Advances in Electrical and Computer Technologies, 562-569 , 2025
    2025
  • Correction to: Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121
    A Muthukumar, M Thanga Raj, R Ramalakshmi, A Meena, P Kaleeswari
    Journal of Ambient Intelligence and Humanized Computing 15 (11), 3847-3847 , 2024
    2024