Work Experience
Worked as an Assistant Professor in Kings Engineering College, Chennai. From May
2012 to June 2015.
Worked as an Associate Professor in Kings Engineering College, Chennai. From July
2015 to December 2022.
Educational Qualification
Ph.D in CSE (Computer Science & Engineering) from Vels University, Tamilnadu. 2013.
8.2 % CGPA.
M.E in CSE (Computer Science & Engineering) from Madras University, Chennai,
Tamilnadu – 2003 – 79.33% – First Class.
B.E in CSE (Computer Science & Engineering) from Madras University, Tamilnadu – 1998
– 67.8% – First Class.
International Journals Published:
1. “RACHSU Algorithm based Handwritten Tamil Script Recognition”, International Journal of
Computer Science Information Security, Vol 08, No 7, pp. 56-61.
2. “Recognition and Conversion of Handwritten Tamil Characters”, International Journal of
Research and Reviews in Computer Science, Vol 01, No 4, pp. 158-163.
3. “Character Recognition using RCS with Neural Network”, International Journal of Computer
Science Issues, Vol 07, No 5, pp. 289-295.
4. “Handwritten Tamil Character Recognition and Conversion using Neural Network”,
International Journal of Computer Science and Engineering, Vol 02, No 7, pp. 2261-2267.
5. “Handwritten Tamil Character Recognition using RCS Algorithm”, International Journal of
Computer Applications, Vol 08, No 8, pp. 21-25.
6. “Tamil Handwritten Character Recognition using Back Propagation Network”, International
Journal of Artificial Intelligent Systems and Machine Learning, Vol 01, No 01, pp. 01-07.
7. “Handwritten Character Recognition using Fuzzy Neural Network”, International Journal of
Advanced Research in Computer Science”, Vol 01, No 01.
8. “Tamil Handwritten Character Recognition using Kohonons Self organizing map ”,
International Journal of Computer Science and Network Security, Vol 09, No 2, pp. 156-162.
9. “Handwritten Tamil Character Recognition using Support vector machine”, International
Journal of Computer and Network Security, Vol 01, No 3, pp. 29-36.
10. Background subtraction based on Threshold detection using modified K-Means Algorithms”,
IEEE Pattern Recognition, Informatics and Mobile Engineering, pp. 379-382.