Balakrushna Tripathy

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

D p acharjya

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

2006 3rd International IEEE Conference Intelligent Systems

Decision making has become a common feature in day-to-day activities. Uncertainty-based models are more efficient in handling such problems. In this chapter, we propose an algorithm in this direction by using a hybrid model formed by combining the two models of soft set (SS) and the intuitionistic fuzzy set (IFS). We follow the characteristic function approach used by Tripathy et al. for the purpose. The illustrative real-life example shows the efficiency of our algorithm over other such models. © Springer Nature Singapore Pte Ltd. 2018.

Smart Computing and Informatics Smart Innovation, Systems and Technologies

Improved Decision Making Through IFSS

The multigranular rough set (MGRS) models of Qian et al. were extended to put forth covering-based multigranular rough sets (CBMGRS) by Liu et al. in 2012. The equality of sets, which is restrictive and redundant, was extended first in Pawlak (Rough sets: theoretical aspects of reasoning about data. Kluwer, London, 1991) and subsequently in Tripathy (Int J Adv Sci Technol 31:23–36, 2011) to propose four types of rough set-based approximate equalities. These basic concepts of rough equalities have been extended to several generalized rough set models. In this paper, covering-based pessimistic multigranular (CBPMG) approximate rough equivalence is introduced and several of their properties are established. Real life examples are taken for constructing counter examples and also for illustration. We have also discussed how these equalities can be applied in approximate reasoning and our latest proposal is no exception. © Springer Nature Singapore Pte Ltd. 2018.

Covering-Based Pessimistic Multigranular Approximate Rough Equivalences and Approximate Reasoning

Marketing management employs various tools and techniques, including market research, to perform accurate marketing analysis. Information and communication technology provided a new dimension in marketing research to maximise the revenues and profits of the firm by identifying the chief attributes affecting decisions. In this paper, we present a hybrid approach for attribute selection in marketing based on rough computing and formal concept analysis. Our approach is aimed at handling an information system that contains numerical attribute values that are “almost similar” instead of “exact similar”. To handle such an information system we use two processes—pre-process and post-process. In pre-process, we use rough set on intuitionistic fuzzy approximation space with ordering rules to find knowledge and associations, whereas in post-process we use formal concept analysis to identify the chief attributes affecting decisions. © 2017

Fulltext

IIMB Management Review

A framework for attribute selection in marketing using rough computing and formal concept analysis

The basic rough sets as introduced by Pawlak have been extended in many directions. Two such extensions are the rough sets on fuzzy approximation spaces and the rough sets by De et al. in 1999 and rough sets on intuitionistic fuzzy approximation spaces by Tripathy in 2006. There are several properties of these two types of rough sets established so far. However, in this paper we show that some of the properties established are erroneous through counter examples, establish their correct versions and illustrate their application in real life situations through an example in computer vision. © Springer Nature Singapore Pte Ltd. 2017.

Advances in Intelligent Systems and Computing Proceedings of Sixth International Conference on Soft Computing for Problem Solving

Some Properties of Rough Sets on Intuitionistic Fuzzy Approximation Spaces and Their Application in Computer Vision

The notion of Rough sets introduced by Pawlak has been extended in many directions to enhance its modelling power. One such approach is to reduce the restriction of the base relation being an equivalence relation. Adding the flavour of fuzzy sets to it a fuzzy proximity relation was used to generate a fuzzy approximation space by De et al. in 1999 and hence the rough sets on fuzzy approximation spaces could be generated. These are much more general than the basic rough sets and also the rough sets defined on proximity relations. However, some of the results established in this direction by De et al. have been found to be faulty. In this paper we show through examples that the results are actually faulty and provide their correct versions. Also, we establish some more properties of these rough sets. A real life application is provided to show the application of the results. © Springer Nature Singapore Pte Ltd. 2017.

Advances in Intelligent Systems and Computing Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Journal	Data powered by Typeset2006 3rd International IEEE Conference Intelligent Systems
Publisher	Data powered by TypesetIEEE
ISSN	15411672
Open Access	0