Praveen Dhyani

Work place: Banasthali University, Computer Science Department, Jaipur 302001, India



Research Interests: Theory of Computation, Mathematics of Computing, Computer systems and computational processes, Computational Science and Engineering


Praveen Dhyani received a Ph.D. degree from Birla Institute of Technology and Science (BITS), Pilani, India. Currently he is a Professor of Computer Science and Executive Director at Banasthali University Jaipur Campus. Previously Dr. Dhyani established and headed national and international centers of BIT MESRA at Jaipur, Bahrain, Muscat, and RAK (UAE). His R&D accomplishments include electronic devices to aid foot drop patients and development of voice operated wheel chair. He is also a member of the Program me Execution Committee (PEC), UIDAI Biometric Centre of Competence (UBCC), Unique Iden-tification Authority of India (UIDAI), Planning Commission, Government of India.

Author Articles
A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition

By Vivek Gaur Praveen Dhyani O. P. Rishi

DOI:, Pub. Date: 8 Feb. 2015

Cloud computing is an emerging internet-based paradigm of rendering services on pay- as -per -use basis. Increasing growth of cloud service providers and services creates the need to provide a tool for retrieval of the high-quality optimal cloud services composition with relevance to the user priorities. Quality of Service rank-ings provides valuable information for making optimal cloud service selection from a set of functionally equiva-lent service candidates. To obtain weighted user-centric Quality of Service Composition, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes framework for predic-tion of optimal composition of services requested by the user. Taking advantage of the past service usage experi-ences of the consumers more cost effective results are achieved. Our proposed framework enables the end user to determine the optimal service composition based on the input weight for individual service Quality of Service. The Genetic algorithm and basic Tabu search is applied for the user-centric Quality of Service ranking prediction and the optimal service composition. The experimental results proves that our approaches outperform other competing approaches.

[...] Read more.
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