Tribikram Pradhan
Recommender System Information Retrieval Social Networks Knowledge Graph NLP Information Retrieval and Data Mining Data Mining and Machine Learning Applications Computational learning theory Advanced Graph Neural Networks Data Mining Algorithms and Applications Mathematics Subject Classification Dominance-based rough set approach Rule induction Coronary artery disease Left main coronary artery disease Coronary disease Coronary heart disease Coronary artery ectasia Coronary artery occlusion Calcium channel blocker Information Retrieval and Search Behavior Library Science and Information Social Sciences Citation Index Merge sort Sorting algorithm Merge algorithm Artificial Intelligence Critical Care and Intensive Care Medicine Information Systems

Bio

Tribikram Pradhan is an Assistant Professor in the Department of Information and Communication Technology at Manipal Institute of Technology, Manipal University, India. With a Master of Technology in Software Technology, he has established himself as a dedicated academic and researcher. His work spans software engineering, with a particular focus on rule induction systems, rough set theory, genetic algorithms, and Boolean algebra, as demonstrated in his publication 'THA-A Hybrid Approach for Rule Induction System using Rough Set Theory, Genetic Algorithm and Boolean algebra'. He has contributed significantly to the academic community, with 50 publications, over 420 citations, an h-index of 11, and an i10-index of 12. His expertise is recognized through his role as a reviewer for the Global Journal of Computer Science and Technology (GJCST), where he has reviewed numerous papers. He is also an active member of the academic community, holding an ORCID iD (0000-0001-5458-2286) and being indexed in Scopus and OpenAlex.

Educational Journey

M.Tech • Software Technology

Experience

Assistant Professor

0 - 0 • Information and Communication Technology

Editors Role

Reviewer

GJCST

2014 -

Research

THA-A Hybrid Approach for Rule Induction System using Rough Set Theory, Genetic Algorithm and Boolean algebra

Article June 30, 2014

The major process of discovering knowledge in database is the extraction of rules from classes of data. One of the major obstacles in performing rule induction from training data set is the inconsistency of information about a problem domain. In order to deal with this problem, many theories and technology have been developed in recent years. Among them the most successful ones are decision tree, fuzzy set, Dempster-Shafer theory of evidence. Unfortunately, all are referring to either prior or posterior probabilities. The rough set concept proposed by Pawlak is a new mathematical approach to inconsistent, vagueness, imprecision and uncertain data. In this paper we have proposed a hybridized model THA (Training dataset on hybrid approach) which combines rough set theory, genetic algorithm and Boolean algebra for discovering certain rules and also induce probable rules from inconsistent information. The experimental result shows that the projected method induced maximal generalized rules efficiently. The hybridized model was validated using the data obtained from observational study.