Work place: Dept. of Comp. Sc., Berhampur University, Berhampur, Odisha, India
M. R. Patra holds a Ph.D. degree in computer Science from the Central University of Hyderabad. Currently, he is the director of the computer centre at Berhampur University in addition to his teaching assignment in the PG department of computer science. As a United Nations Visiting Fellow, he worked at the International Institute for Software Technology, United Nations University, Macau; and Institute for Development and Research in Banking Technology, Hyderabad. His research interests include service based computing, applications of data mining, and e-Governance. He has supervised 5 Ph.D. students and has more than 100 publications in journals and international conferences to his credit. He is a life member of CSI, ISTE and OITS, and a fellow of ACEEE.
DOI: https://doi.org/10.5815/ijisa.2015.04.04, Pub. Date: 8 Mar. 2015
Convergence of information and communication technology has brought a radical change in the way data are collected or generated for ease of multi criterion decision making. The huge data is of no use unless it provides certain information. It is very tedious to select a best option among an array of alternatives. Also, it becomes more tedious when the data contains uncertainties and objectives of evaluation vary in importance and scope. Unlocking the hidden data is of no use to gain insight into customers, markets and organizations. Therefore, processing these data for obtaining decisions is of great challenge. Based on decision theory, in the past many methods are introduced to solve multi criterion decision making problem. The limitation of these approaches is that, they consider only certain information of the weights and decision values to make decisions. Alternatively, it makes less useful when managing uncertain and vague information. In addition, an information system establishes relation between two universal sets. In such situations, multi criterion decision making is very challenging. Therefore, an effort has been made in this paper to process inconsistencies in data with the introduction of intuitionistic fuzzy rough set theory on two universal sets.[...] Read more.
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