Prabhat Mahanti

Work place: University of New Brunswick, St. John, Canada



Research Interests: Information Theory, Information Systems, Information Security, Computer Science & Information Technology


Dr. Prabhat K. Mahanti is a Professor of Computer Science at the University of New Brunswick, Canada. Previous to his appointment as Professor at UNB, Dr.Mahanti was the Chair and Professor of Computer Science and Engineering Department, Birla Institute of Technology, India. He has published over 100 research papers in referred journals and conference proceedings including book chapters. He has been a supervisor and thesis committee member of graduate students both in India and Canada. He actively participates in numerous technical conferences; including serving as a conference chair and reviewers on many of them. Currently, he is on the editorial board for the Computer and Informatics- Slovak Academy of Sciences, Slovakia, Central European Journal of Computer Science, Germany, and the Journal of Computing and Information Technology-Croatia. He is also the Editor-in-Chief for the Journal of Computers, Finland.

Author Articles
Autonomous Image Segmentation using Density-Adaptive Dendritic Cell Algorithm

By Vishwambhar Pathak Praveen Dhyani Prabhat Mahanti

DOI:, Pub. Date: 8 Aug. 2013

Contemporary image processing based applications like medical diagnosis automation and analysis of satellite imagery include autonomous image segmentation as inevitable facility. The research done shows the efficiency of an adaptive evolutionary algorithm based on immune system dynamics for the task of autonomous image segmentation. The recognition dynamics of immune-kernels modeled with infinite Gaussian mixture models exhibit the capability to automatically determine appropriate number of segments in presence of noise. In addition, the model using representative density-kernel-parameters processes the information with much reduced space requirements. Experiments conducted with synthetic images as well as real images recorded assured convergence and optimal autonomous model estimation. The segmentation results tested in terms of PBM-index values have been found comparable to those of the Fuzzy C-Means (FCM) for the same number of segments as generated by our algorithm.

[...] Read more.
Other Articles