Work place: Department of Computer and Information Sciences, Florida A&M University, Tallahassee, Florida, USA
E-mail: bhanupvsr@gmail.com
Website: http://www.bhanuprasad.org/
Research Interests: Software Engineering, Artificial Intelligence, Optimization
Biography
Bhanu Prasad received Master of Technology and Ph.D. degrees, both in computer science, from the Andhra University and the Indian Institute of Technology Madras respectively. He is currently serving as a Professor in the Department of Computer and Information Sciences at Florida A&M University. His current research interests are in the areas of artificial intelligence, software engineering, and optimization problems.
http://www.bhanuprasad.org
By Surekha Thota Shantala Devi Patil Gopal Krishna Shyam Bhanu Prasad
DOI: https://doi.org/10.5815/ijcnis.2024.06.06, Pub. Date: 28 Dec. 2024
Enterprises are adopting blockchain technology to build a server-less and trust-less system by assuring immutability and are contributing to blockchain research, innovation, and implementation. This led to the genesis of various decentralized blockchain platforms and applications that are unconnected with each other. Interoperability between these siloed blockchains is a must to reach its full potential. To facilitate mass adoption, technology should have the ability to transact between various decentralized applications (dapps) on the same chain, integrate with existing systems, and initiate transactions on other networks. In our research, we propose a secured authentication mechanism that enables various decentralized applications on the same chain to interact with each other using a global dapp authentication registry (GDAR). We carried out an in-depth performance evaluation and conclude that our proposed mechanism is an operative authentication solution for dapp interoperability.
[...] Read more.By Roman Bazylevych Bohdan Kuz Roman Kutelmakh Remy Dupas Bhanu Prasad Yll Haxhimusa Lubov Bazylevych
DOI: https://doi.org/10.5815/ijitcs.2016.05.01, Pub. Date: 8 May 2016
A parallel approach for solving a large-scale Traveling Salesman Problem (TSP) is presented. The problem is solved in four stages by using the following sequence of procedures: decomposing the input set of points into two or more clusters, solving the TSP for each of these clusters to generate partial solutions, merging the partial solutions to create a complete initial solution M0, and finally optimizing this solution. Lin-Kernighan-Helsgaun (LKH) algorithm is used to generate the partial solutions. The main goal of this research is to achieve speedup and good quality solutions by using parallel calculations. A clustering algorithm produces a set of small TSP problems that can be executed in parallel to generate partial solutions. Such solutions are merged to form a solution, M0, by applying the "Ring" method. A few optimization algorithms were proposed to improve the quality of M0 to generate a final solution Mf. The loss of quality of the solution by using the developed approach is negligible when compared to the existing best-known solutions but there is a significant improvement in the runtime with the developed approach. The minimum number of processors that are required to achieve the maximum speedup is equal to the number of clusters that are created.
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