207
PROGRAMS
OFFERED
OFFERED
Radha D.
Asst. Professor, Computer Science, School of Engineering, Bengaluru
Asst. Professor, Computer Science, School of Engineering, Bengaluru
Qualification:
M.Tech, BE
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
| 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. |
|
2017 |
Conference Paper |
, Radha D., and Amudha, J., “Effectual Training For Object Detection Using Eye Tracking Data Set”, in 2nd International conference on Inventive Computation Technologies(ICICT-2017), Coimbatore, 2017. |
| 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. More »» |
|
2014 |
Journal Article |
Radha D., Amudha, J., and Jyotsna C, “Study of Measuring Dissimilarity between Nodes to Optimize the Saliency Map”, Int.J.Computer Technology & Applications, vol. 5, no. 3, pp. 993-1000, 2014.[Abstract] An analytical conclusion based on eye tracking data sets has shown that Graph Based Visual Saliency (GBVS) measures saliency in a better way. GBVS promotes higher saliency at the center of the image plane and strongly highlights salient regions even for the locations that are far-away from object borders. It predicts human fixations more consistently than the standard algorithms. Every pixel in an image is mapped as an individual graph node in the activation map. This in turn increases the computational time. Hence the objective of this paper is to analyze the performance of saliency measure in GBVS by modeling different grouping strategies to represent a node. Here, we concentrate on finding the dissimilarity between the nodes by grouping pixels as a node with overlapping or non-overlapping pixels in the surrounding nodes which optimize the saliency closer to the Eye-Tracker’s saliency. The different grouping strategies of GBVS are analyzed across several performance measures like Normalized Scanpath Saliency the Linear Correlation Coefficient, Area Under Curve, , Similarity, Kullback – Leibler Divergence to prove its efficiency. Key terms – Visual Attention Models, Saliency maps, Eye-Tracking, Grouping pixels. More »» |
|
2013 |
Journal Article |
Radha D. and Amudha, J., “Detection of Unauthor- ized Human Entity in Surveillance Video”, International Journal of Engineering and Technology (IJET), vol. 5, no. 3, pp. 3101-3108, 2013.[Abstract] With the ever growing need for video surveillance in various fields, it has become very important to automate the entire process in order to save time, cost and achieve accuracy. In this paper we propose a novel and rapid approach to detect unauthorized human entity for the video surveillance system. The approach is based on bottom-up visual attention model using extended Itti Koch saliency model. Our approach includes three modules- Key frame extraction module, Visual attention model module, Human detection module. This approach permits detection and separation of the unauthorized human entity with higher accuracy than the existing Itti Koch saliency model. |
Faculty Research Interest:
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AMRITA
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