M.Tech, B-Tech

Krishna Prasad currently serves as Assistant Professor at the Department of Computer Science and Engineering at Amrita School of Engineering, Amritapuri. He has completed M. Tech. in Computer Science and B. Tech. in Information Technology. He has more than 9 years of academic experience and 1 year of industry experience.


  • 'A' grade (highest grade) in Python Workshop conducted by Infosys Campus Connect Program


Publication Type: Conference Proceedings

Year of Publication Title


K. V. Sankar Reddy, K. Nihith, S., Krishna Prasad T. R., and M. Chowdary, S., “An Efficient Word Embedded Click-Bait Classification of YouTube Titles Using SVM”, Symposium on Machine Learning and Metaheuristics Algorithms, and Applications SoMMA 2019: Machine Learning and Metaheuristics Algorithms, and Applications, Part of the Communications in Computer and Information Science book series (CCIS), vol. 1203. Springer Science and Business Media LLC, pp. 175–184, 2020.[Abstract]

Most of the online media outlets normally depend on the revenues generated from the clicks made by their viewers and due to presence of their outlets. To increase traffic onto their websites some people come with some misleading headlines as titles for their links. Such misleading headlines are known as Click-baits. These click-baits leave the user disappointed as the content of the address is very different from the headline or the title. The current work focuses on classification of YouTube titles into click-baits and non click-baits using tokenization and word embedding applied to SVM. Upon simulation of the algorithm we are able to increase the accuracy, compared to some of the other classification algorithms.

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Krishna Prasad T. R., Jayakumar P., and Dr. Sajeev G. P., “A K-Clique Based Clustering Protocol for Resource Discovery in P2P Network”, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. pp. 2475-2480, 2018.[Abstract]

Data transfer amongst peers in a network without a central authority to regulate traffic, is on the ascendancy in the recent years. In this paper, we propose a k-clique based overlay network formation using multi key-single value pair mapping mechanism within a peer to peer network. This clique based model aims to discover resources of the same metadata type within a cluster of subnet with a minimum of hops as possible subject to the nodes having certain properties. The discovery of a subset of resources sharing similar characteristics is essential in the context of requirements being dynamic, in fields related to Internet of Things and Cloud systems. The simulated experiment validates our approach as it discovers resources in very less number of hops.

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Krishna Prasad T. R. and Dr. Sajeev G. P., “A novel method for resource discovery from a range based request in a P2P network”, Symposium on Emerging Topics in Computing and Communications (SETCAC'16), International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, Jaipur, India, 2016.[Abstract]

Peer to Peer (P2P) systems have increased the curiosity and pathways for people to discover and share the resources. While various methods have been proposed in the discovery of discrete value based resources, there is also a surging interest in discovering a range of resources for a given request. This work is a novel design of a P2P network that adheres to range requests and seeks to discover the resources sought for in the request. The proposed model seeks to find out the range of resource values from within a P2P network of nodes that are in a circular overlay structure. The validation of the design reaches the conclusion that the proposed model increases in efficiency as the number of hubs increases with respect to discovering a range of resources in the hubs.

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