B
B. K. Tripathy
Researcher at VIT University
Publications - 268
Citations - 2140
B. K. Tripathy is an academic researcher from VIT University. The author has contributed to research in topics: Rough set & Cluster analysis. The author has an hindex of 22, co-authored 243 publications receiving 1735 citations. Previous affiliations of B. K. Tripathy include Berhampur University.
Papers
More filters
Journal ArticleDOI
On the theory of bags and lists
TL;DR: The concept of bag complement is redefined suitably and some theorems involving bag operations have beenestablished and many existing and new results have been established based upon this new definition.
Book ChapterDOI
On Intuitionistic Fuzzy Soft Sets and Their Application in Decision-Making
TL;DR: This paper follows the approach of Tripathy et al. in redefining IFSS and presents an application of IFSS in decision-making which substantially improve and is more realistic than the algorithms proposed earlier by several authors.
Proceedings ArticleDOI
A New Approach to Manage Security against Neighborhood Attacks in Social Networks
B. K. Tripathy,G. K. Panda +1 more
TL;DR: This paper proposes a modification to Bin Zhou and Pei's algorithm for the network anonymization which can handle the situations in which an adversary has knowledge about vertices in the second or higher hops of a vertex, in addition to its immediate neighbors.
Posted Content
A Framework for Intelligent Medical Diagnosis using Rough Set with Formal Concept Analysis
TL;DR: In this paper, two processes such as pre process and post process are used to mine suitable rules and to explore the relationship among the attributes to explore better knowledge and most important factors affecting the decision making.
Proceedings ArticleDOI
Investigation of recurrent neural networks in the field of sentiment analysis
Kiran Baktha,B. K. Tripathy +1 more
TL;DR: Three popular deep learning architectures, namely vanilla RNNs, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are analyzed for analyzing sentiments in sentences using pre-trained word vectors from the Google News dataset.