kvelayut@gitam.edu
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Kathiresan Velayutham has extensive research experience in Machine Learning and Deep Learning related to various applications, Along with his collaborators from various universities, He used machine learning and deep learning techniques for outlier detection, he involved machine learning in the applications like financial transaction fraud detection and intrusion detection. He has published several articles on this topic.
Research Publications
- Kathiresan V, Karthik S, Prabakar D, Kavitha M S . (2022, January). Logistic Regression based machine Learning model for mutation classification in discovery of precision Medicine(Springer-scopus) . EAI/Springer Innovation in Communication and Computing – Translating Healthcare Through Intelligent Computational Methods.
- Kathiresan, V., Karthik, S., Divya, P., & Rajan, D. P. (2022, January). A Comparative Study of Diverse Intrusion Detection Methods using Machine Learning Techniques. In 2022 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-6). IEEE. (Scopus-Indexed)
- Kathiresan, V., & Karthik, S. (2021). Efficient Detection Using Soft Computing Approach of Modified Fuzzy C-Means Based Outlier Detection in Electronics Patient Records Systems. Journal of Medical Imaging and Health Informatics, 11(10), 2660-2666
- V. Kathiresan, Dr. N.A Vasanthi. Article: Outlier Detection On Financial Card Or Online Transaction Data Using Manhattan Distance Based Algorithm. International Journal of Contemporary Research in Computer Science and Technology (IJCRCST): 1100-1103,Volume 2 Issue 12 – December 2016.
- V. Kathiresan, Dr. N.A Vasanthi.Article: A Survey on Outlier Detection Techniques Useful for Financial Card Fraud Detection.International Journal of Innovations in Engineering and Technology (IJIET): 226-235,Volume 6 Issue 1 – October 2015.
Expertise
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Machine Learning and its applications
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Deep learning and its applications
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Computer Vision and its Applications
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Computer Programming
Authored Books
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Logistic Regression based machine Learning model for mutation classification in discovery of precision Medicine
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Machine Learning-Based DDoS Attack Detection Using Support Vector Machine
