Qualification: 
M.Tech, BE
Email: 
d_radha@blr.amrita.edu

Radha D. currently serves as Assistant Professor at Department of Computer Science, Amrita School of Engineering, Bengaluru. She is currently pursuing Ph.D in the area of High Performance computing in Image Processing.

Education

Year of Passing Degree Name of the Board/University
2007 M. Tech. in CSE Dr. MGR University
1997 B. E. in CSE Madurai Kamaraj Univeristy

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

J. Amudha and Radha D., “Optimization of Rules in Neuro-Fuzzy Inference Systems”, in International Conference on Computational Vision and Bio-inspired Computing (ICCVBIC 2017), Inventive Research Organization and RVS Technical Campus, Coimbatore, 2017.

2017

Conference Paper

Radha D. and Kavikuil, K., “Centrality Measures to Analyze Transport Network for Congestion Free Shipment ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017.

2017

Conference Paper

Radha D. and Kulkarni, S., “A Social Network Analysis of World Cities Network ”, in the 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017.

2017

Conference Paper

Radha D. and Nithia, K. P. T., “A Case Study on Social Network Analysis: Thesaurus Book ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017.

Publication Type: Journal Article

Year of Publication Publication Type Title

2015

Journal Article

A. J., Radha D., and S., S., “Analysis of fuzzy rule optimization models”, International Journal of Engineering and Technology, vol. 7, pp. 1564-1570, 2015.[Abstract]


Optimization without losing the accuracy and interpretability of rules is a major concern in rule based system. Fuzzy Inference system characterized by uncertainty tolerance is the best way to represent a knowledge based system. Optimization of rule based systems starts by incorporating selflearning ability to a fuzzy inference system. This can be achieved by neural networks, there by developing a neuro fuzzy inference system. This paper analyses different neuro fuzzy inference systems.The analysis has been performed in different types of datasets in terms of dimensionality and noises. Analysis results concludes that the neuro fuzzy model DENFIS (Dynamically Evolving Neuro Fuzzy Inference System) shows an improved performance when handling with high dimensional data. Simulation results on low dimensional data exhibits similar performance in ANFIS (Adaptive Neuro Fuzzy Inference System) and Denfis.

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207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
9th
RANK(INDIA):
NIRF 2017
150+
INTERNATIONAL
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