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Rajamanogaran Murugan

Associate Professor, Department of Computer Science and Engineering, School of Computing, Chennai

manogaran248@gmail.com
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Bio Publications Qualification Skills Experience Projects Leadership Patent
Bio

Rajamanogaran Murugan currently serves as Associate Professor in the Department of Computer Science and Engineering, School of Computing, Chennai.
Innovative and research-driven Data Scientist with 7+ years of experience in Artificial Intelligence, Deep Learning, and Computer Vision. Specialized in architecting end-to-end machine learning solutions for real-time accident detection, biometric systems, medical imaging, and predictive analytics in healthcare and finance. Patent holder and published researcher with expertise in CNN, YOLO frameworks, LLMs, and federated learning.

Publications

Book Chapter

Year : 2025

Assessment of Various Segmentation Approaches to Poster Breast Cancer Using Thermal Images

Cite this Research Publication : P Kanimozhi, M Rajamanogaran, S Sathiya, ASSESSMENT OF VARIOUS SEGMENTATION APPROACHES TO POSTER BREAST CANCER USING THERMAL IMAGES, Future Trends in Internet of Things, Internet of Everything and its Applications, Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025, https://doi.org/10.58532/nbennurfticsw2

Publisher : Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd.

Conference Paper

Year : 2025

Real-Time Accident Event Detection Using CNN-YOLOv3 Fusion: Improving Accuracy for Enhanced Safety Measures

Cite this Research Publication : R. Elangovan, G. Karthikeyan, V. Ramalingam, M. Rajamanogaran, Real-Time Accident Event Detection Using CNN-YOLOv3 Fusion: Improving Accuracy for Enhanced Safety Measures, 2025 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), IEEE, 2025, https://doi.org/10.1109/amathe65477.2025.11081174

Publisher : IEEE

Year : 2024

FKNNYOLO: A Fusion Framework for Advanced Accident Event Detection and Enhanced Public Safety on Roads and Highways

Cite this Research Publication : M. Rajamanogaran, G. Karthikeyan, FKNNYOLO: A Fusion Framework for Advanced Accident Event Detection and Enhanced Public Safety on Roads and Highways, 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), IEEE, 2024, https://doi.org/10.1109/accai61061.2024.10602409

Publisher : IEEE

Year :

Effective minutiae extraction and template creation in fingerprints

Cite this Research Publication : E Karuppathal, R Kalpana, B Thilagavathy,S Baghavathi Priya and M R ajamanogaran,
Effective minutiae extraction and template creation in fingerprints, Journal of Physics: Conference Series,2318(2022) 012035,DOI10.1088/1742-6596/2318/1/012035[Scopus]

Publisher : IPO Science

Journal Article

Year : 2025

FRFYOLO -Fusion of Random Forest and YOLO Frame work for Real- Time Accident Event Detection

Cite this Research Publication : Rajamanogaran, FRFYOLO -Fusion of Random Forest and YOLO Frame work for Real- Time Accident Event Detection, [source], [publisher], 2025, [url]

Year : 2023

Comparison and Evaluation of Various CNN Models for Crack Detection in Structures

Cite this Research Publication : Rajamanogaran, Comparison and Evaluation of Various CNN Models for Crack Detection in Structures, [source], [publisher], 2023, [url]

Qualification
  • Ph.D. (Computer Science) – (under Thiruvalluvar University), Thesis Objective: Automating and optimizing accident identification for faster emergency response and traffic safety management
  • M.Sc. (Information Technology, Integrated) – Annamalai University, Chidambaram (2012–2017)
Skills
  • Languages: Python, C, SQL
  • Machine Learning: Regression, SVM, KNN, Random Forest, PCA, Clustering, Ensemble Methods
  • Deep Learning: CNN, RNN, LSTM, Transfer Learning (InceptionV3, VGG, MobileNet, YOLOv3), LLMs, TensorFlow, Keras
  • Computer Vision & NLP: OpenCV, Image Processing, Spacy, NLTK, Sentiment Analysis, Word2Vec
  • Cloud & Big Data: Azure ML Studio, Cosmos DB, PySpark, MongoDB, MySQL
  • Frameworks: Django, Flask
  • Tools: Git, Jupyter Notebook, Pandas, NumPy, Matplotlib, Scikit-learn
Experience
  • Data Scientist – Derisk Solutions (May 2018 – 2020)
    • Developed AI-driven solutions using Azure Cloud for real-time analytics and storage
    • Designed predictive models with deep learning for diverse client applications
    • Optimized neural architectures for faster inference in real-time systems
  • Python Developer – SVJ Technology (May 2017 – Mar 2018)
    • Built backend logic and web applications using Python frameworks
    • Collaborated on early-stage ML integrations and data pipelines
Projects
  • Healthcare AI: Lung cancer prediction using CNN/MobileNet; Breast cancer segmentation via thermal imaging
  • Computer Vision & Biometrics: Real-time face recognition system; Vehicle detection & traffic analysis using ensemble ML
  • NLP & Audio: Conversational AI agent; RNN-based speaker classification; CNN-based musical instrument recognition
  • Finance & Predictive Analytics: Django-based automated trading platform; LSTM-based water quality forecasting
Leadership

Leadership & Volunteering

  • Placement Secretary – Postgraduate Department, Annamalai University (2012–2017)
  • Event Coordinator – Visume Fest 2015 (World Record Event, Annamalai University)
  • Community Lead – AI Development Club & Open-Source Community
  • World Record Holder – Continuous single-stick Silambam rotation for 5 hours (2023, Arasan Academy, Tamil Nadu)
Patent
  • Real-Time Video-Based Vehicle Detection, Counting & Classification Using Hybrid ML Algorithm
    • Published July 2024 | Application No. 202441050589 A
    • Combines ensemble ML algorithms with HOG features for traffic density prediction and vehicle counting
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