TamilThendral

I am a faculty member at Bannari Amman Institute of Technology, Sathyamangalam, with over 6 years of teaching experience. I hold a Ph.D. in Information and Communication Engineering from Anna University, Chennai. My research interests include Medical Image Processing, Machine Learning, Deep Learning, and Generative AI. I have published four Scopus-indexed papers, one SCI paper, four IEEE conference papers, and one book chapter.

EDUCATION

I have completed my Bachelor of Engineering (B.E.) in Computer Science and Engineering in April 2011 from Shri Andal Alagar College of Engineering, Chengalpattu. Subsequently, I obtained my Master of Engineering (M.E.) in Computer Science and Engineering in May 2016 from the same institution. Further, I was awarded my Doctor of Philosophy ( in Information and Communication Engineering in February 2022 from Anna University, Chennai.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering

Scopus Publications

Synchronization of Markovian jump neural networks for sampled data control systems with additive delay components: Analysis of image encryption technique
Marimuthu Tamil Thendral, Thiruvannamalai Radhakrishnan Ganesh Babu, Arunachalam Chandrasekar, Yang Cao
Mathematical Methods in the Applied Sciences, 2026
In an modern world, image encryption played an vital role to prevent our data from illegal abuser entrée. Based on this, in this paper, the Markovian jump neural networks for synchronization of sampled‐data control systems with two additive delay components are used on the looped functional method, and its direct application is applied in image encryption. Meanwhile, and denotes the information states with tuning parameter and a few slack variable which is introduced in the derived result. Furthermore, the sampled‐data controller is intended both the present and delayed state information, to enroll the control performance and flexibility. Finally by using the new technique, the several examples are highlighted in the numerical section, and also, the effectiveness of an image encryption is studied.
Analysis of Brain Health Using Machine Learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective
Arunkumar Thirunagalingam, M. S. Usha, S. Geetha, A Dhanamathi, S. Dhivya, M.Tamil Thendral
1st International Conference on Advances in Computer Science Electrical Electronics and Communication Technologies Ce2ct 2025, 2025
The creation and discovery of pharmaceuticals may be considered the most important translational science activity that improves human invulnerability and happiness. In the pharmaceutical sector, strategies to reduce costs and speed up the development of new drugs have sparked a rigorous and fascinating brainstorming session. The use of classified big data in conjunction with remarkably improved computer power and cloud storage has enabled the application of artificial intelligence (AI), particularly the deep-learning (DL) component, in all domains.In healthy individuals, ML evaluation of neuroimaging data may accurately predict ordered age; illness and mental impairment have been linked to departures from normal brain maturation. Convolutional neural networks (CNN), a DL-based predictive display technique, were applied to both pre-handled and raw T1-weighted X-ray data in order to further evaluate the eligibility of “brain-predicted age” as a biomarker of individual contrasts in the brain ageing process. Brain-predicted age is a very accurate, remarkably stable, and genetically valid trait that exhibits assurance as a biomarker of brain maturation. Additionally, age may be accurately predicted from raw T1-MRI data, which greatly reduces the amount of time needed to compute fresh data and accelerates the process of delivering immediate data on brain health in clinical settings.
Real-Time Autonomous Vehicle Automation With 5G-Based Edge Computing and Artificial Intelligence
Geethanjali D, Meena Rani N, Prasanna Kumar Lakineni, Veeraiah Maddu, Abhilash S Nath, Tamil Thendral M
Journal of Machine and Computing, 2025
Autonomous Vehicles (AV) are revolutionizing transportation, but real-time decision-making remains a challenge due to End-To-End Delay (EED introduced by Cloud Computing (CC) based processing. A 5G-enabled Edge Computing Model (5G-EECM) is proposed to address this problem by processing time-sensitive tasks at the network edge, closer to the AV, reducing EED and improving responsiveness. The architecture uses Machine Learning (ML) for Obstacle Detection (OD) and Reinforcement Learning (RL) for navigation, dynamically switching between Edge Computing (EC) EC and CC based on task demands. The study tested the system using a user-friendly AV on a controlled track, revealing increased response times, reduced average EED, reduced energy consumption, and improved OD accuracy. The results demonstrate that 5G-EECM significantly boosts AV systems' real-time safety and efficiency, making it reliable and scalable for next-generation AV systems.
Object Detection and Identification for Visually Impaired
Lourdes Santhosh S, Jegathiswaran R, M. Tamil Thendral
Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024 Icnwc 2024, 2024
Visual impairment takes a major toll in a person’s life for them to act independently without others help. This paper introduces an assistive technology that makes use of the YOLO (You Only Look Once) algorithm integrated into wearable spectacles along with a camera linked to a Raspberry Pi processor. The system aims to perform real-time object detection and identification, transforming visual data into audio feedback for users. The hardware setup involves a camera connected to a Raspberry Pi for data processing captured using the camera. An earphone is connected to the Raspberry Pi to guide and navigate the user. The YOLO algorithm, specifically optimized for real-time usage on the Raspberry Pi, distinguishes objects captured within the camera's view. Consequently, the system translates this visual data into understandable audio information transmitted to users via connected earphones by using the trained data. This paper introduces a methodology, execution, and outcomes of the proposed system. It offers a thorough insight into the hardware configuration, the algorithmic framework employed for object detection, and the integration methodology for generating audio-based feedback. Evaluations of the system's performance is calculated by its efficacy, swiftness, and precision in real-world situations, highlighting its potential to aid visually impaired individuals in understanding unfamiliar environments.
Fake Currency Notes Detection using Supervised Learning Methods
K. Selvakumar, K.R Premlatha, M. Tamil Thendral, L Sai Ramesh
Proceedings 3rd International Conference on Advances in Computing Communication and Applied Informatics Accai 2024, 2024
This paper discusses the story of to identify the type of money that if a sample is given money fraud. Traditional techniques are different as well methods available for the detection of counterfeit money that on its color, widths, along with unique currency identification number on it. Today, the time of present-day of advance computational age-progressed computation techniques, different AI calculations have been created for picture handling that gives close to $100 \\%$ precision of phony money. Strategies for obtaining and acknowledgment over calculations incorporate associations, for example, shading, shape, paper width, picture sifting note. This paper proposes a fake money acknowledgment technique utilizing K-Nearest Neighbors were trailed by picture handling and further refinement of boundaries. KNN has a high precision of little informational collections making it alluring utilized for PC discovery work. The banknote picture properties dataset has been made with the development of computational and numerical methodologies, which results in the right information and data in regards to the substances and elements identified with the money. Information handling and information Extraction is finished by utilizing AI and picture calculations handling to get the end-product and exactness.
Implementing a Graph Convolutional Network (GCN) model for the early detection of Alzheimer's disease using a single neurodegeneration indicator
P.Joy Kiruba, K. Sowmiya, Thiagarajan Kittappa, P. Balasubramanian, Vanitha Innocent Rani, M. TamilThendral
3rd International Conference on Advances in Computing Communication and Materials Icaccm 2024, 2024
This research presents a diagnosis model of single biomarker of Alzheimer’s diseases using graph convolutional network (GCN). Alzheimer’s disease is quite a challenging disease to diagnose early and accurately due to its complexity and progression nature. Traditional approaches used troponin and various other enzymes as well as clinical procedures such as coronary imaging – techniques that are expensive and require time. This approach makes diagnosis less complex because it utilizes a single measure of neurodegeneration combined with GCNs, which rely on structural analysis of the brain’s neural network. The preclinical research entertaining requires assembling and standardizing a sizable amount of data to make certain that the chosen biomarker generates coherent signals of Alzheimer’s disease. To this end, the GCN model is designed to be able to discern between healthy and diseased states by fine-tuning graph convolutions to capture such nuances. Empirical results of key performance indicators show that using our model yields better results in diagnostic outcomes than conventional methods. This innovative approach not only brings down the diagnostic costs, but also helps diagnose Alzheimer’s faster and more accurately thus helping a patient’s condition, and quality of life.
Fake Product Detection Using Blockchain Using QR Code
J Kevin Timothy, R Lokesh, M. Tamil Thendral, P Meena, A Gnanasudharsan, S R Rithika, S Sreevarshan
Iq Cchess 2023 2023 IEEE International Conference on Quantum Technologies Communications Computing Hardware and Embedded Systems Security, 2023
Issues such as service duplication, inadequate departmental coordination, and a lack of standardization often plague supply chain management due to a lack of transparency. Counterfeiting has become increasingly prevalent, making it extremely difficult to visually identify counterfeit products. Counterfeiters cause significant challenges for legitimate firms, yet far too many people have no idea of the entire amount of counterfeit items’ influence on brands. Various approaches have been developed in the past to address the challenge of product counterfeiting. The most popular methods are using RFID tags, Artificial Intelligence, QR Code based systems, etc. Each of them had a few disadvantages such as the QR Code can be copied from a genuine product and placed on a fake product, artificial intelligence uses CNN and machine learning which needs heavy computational power and so on. The idea of this paper is to improve detection of fake products by linking product SN with customer ID in the seller’s system. This is achieved with Blockchain technology which ensures the identification and traceability of real products through QR Code. Blockchain based systems, makes everything decentralized. One of its main advantages is that the recorded data is difficult to change without the consent of all parties concerned which makes the data extremely secure and protected from all vulnerabilities.

RECENT SCHOLAR PUBLICATIONS

Synchronization of Markovian jump neural networks for sampled data control systems with additive delay components: Analysis of image encryption technique
M Tamil Thendral, TR Ganesh Babu, A Chandrasekar, Y Cao
Mathematical methods in the applied sciences 49 (3), 1879-1895 , 2026
2026
Citations: 105
Real-Time Autonomous Vehicle Automation With 5G-Based Edge Computing and Artificial Intelligence
ASNTTM "Geethanjali D, Meena Rani N, Prasanna Kumar Lakineni, Veeraiah Maddu
Journal of Machine and Computing 5 (2) , 2025
2025
Analysis of Brain Health Using Machine Learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective
TT Arun Kumar, Usha, Geetha, Dhanamathi, Dhivya
2025 First International Conference on Advances in Computer Science … , 2025
2025
Implementing a Graph Convolutional Network (GCN) model for the early detection of Alzheimer's disease using a single neurodegeneration indicator
PJ Kiruba, K Sowmiya, T Kittappa, P Balasubramanian, VI Rani, ...
2024 International Conference on Advances in Computing, Communication and … , 2024
2024
Fake currency notes detection using supervised learning methods
K Selvakumar, KR Premlatha, MT Thendral, LS Ramesh
2024 International Conference on Advances in Computing, Communication and … , 2024
2024
Citations: 1
Object Detection and Identification for Visually Impaired
R Jegathiswaran, MT Thendral
2024 2nd International Conference on Networking and Communications (ICNWC), 1-6 , 2024
2024
Citations: 6
Object Detection and Identification for Visually Impaired
L Santhosh S, J R, MT Thendral
2024 2nd International Conference on Networking and Communications (ICNWC) , 2024
2024
Fake Product Detection Using Blockchain Using QR Code
JK Timothy, R Lokesh, MT Thendral, P Meena, A Gnanasudharsan, ...
2023 International Conference on Quantum Technologies, Communications … , 2023
2023
Citations: 4
Cyber Forensics
DRR Dr.M.Tamil Thendral, Dr.Sunanda Das
2023
Magnetic Resonance Image from Children’s Brain by Evaluating IQ Estimator Using Kernel Support Vector Regression
TRB M Tamil Thendral
Journal of Medical Imaging and Health Informatics 11 (5), 1431-1443 , 2021
2021
Detection of Lung Cancer Tumorusing Fuzzy Local Information C-Means Clustering
TR Ganesh Nabu, M Tamil Thendral, K Vidhya
Int. J. Pure Appl. Math 118 (17), 389-400 , 2018
2018
Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

Synchronization of Markovian jump neural networks for sampled data control systems with additive delay components: Analysis of image encryption technique
M Tamil Thendral, TR Ganesh Babu, A Chandrasekar, Y Cao
Mathematical methods in the applied sciences 49 (3), 1879-1895 , 2026
2026
Citations: 105
Object Detection and Identification for Visually Impaired
R Jegathiswaran, MT Thendral
2024 2nd International Conference on Networking and Communications (ICNWC), 1-6 , 2024
2024
Citations: 6
Fake Product Detection Using Blockchain Using QR Code
JK Timothy, R Lokesh, MT Thendral, P Meena, A Gnanasudharsan, ...
2023 International Conference on Quantum Technologies, Communications … , 2023
2023
Citations: 4
Fake currency notes detection using supervised learning methods
K Selvakumar, KR Premlatha, MT Thendral, LS Ramesh
2024 International Conference on Advances in Computing, Communication and … , 2024
2024
Citations: 1
Detection of Lung Cancer Tumorusing Fuzzy Local Information C-Means Clustering
TR Ganesh Nabu, M Tamil Thendral, K Vidhya
Int. J. Pure Appl. Math 118 (17), 389-400 , 2018
2018
Citations: 1
Real-Time Autonomous Vehicle Automation With 5G-Based Edge Computing and Artificial Intelligence
ASNTTM "Geethanjali D, Meena Rani N, Prasanna Kumar Lakineni, Veeraiah Maddu
Journal of Machine and Computing 5 (2) , 2025
2025
Analysis of Brain Health Using Machine Learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective
TT Arun Kumar, Usha, Geetha, Dhanamathi, Dhivya
2025 First International Conference on Advances in Computer Science … , 2025
2025
Implementing a Graph Convolutional Network (GCN) model for the early detection of Alzheimer's disease using a single neurodegeneration indicator
PJ Kiruba, K Sowmiya, T Kittappa, P Balasubramanian, VI Rani, ...
2024 International Conference on Advances in Computing, Communication and … , 2024
2024
Object Detection and Identification for Visually Impaired
L Santhosh S, J R, MT Thendral
2024 2nd International Conference on Networking and Communications (ICNWC) , 2024
2024
Cyber Forensics
DRR Dr.M.Tamil Thendral, Dr.Sunanda Das
2023
Magnetic Resonance Image from Children’s Brain by Evaluating IQ Estimator Using Kernel Support Vector Regression
TRB M Tamil Thendral
Journal of Medical Imaging and Health Informatics 11 (5), 1431-1443 , 2021
2021