Soft set theory—First results
TLDR
The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the the theory, and to discuss some problems of the future.Abstract:
The soft set theory offers a general mathematical tool for dealing with uncertain, fuzzy, not clearly defined objects. The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the theory, and to discuss some problems of the future.read more
Citations
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Complex T-Spherical Fuzzy Relations With Their Applications in Economic Relationships and International Trades
TL;DR: In this article, the concept of CTSF relations (CTSFRs) is introduced, which can give many generalized types of fuzzy relations, such as IFRs, CFRs, Pythagorean FRs, q-rung orthopair FSs, SFRs and TSFSs.
Journal ArticleDOI
01 Linear programming methods for optimal normal and pseudo parameter reductions of soft sets
TL;DR: This paper defines the matrix of dominant support parameters and uses it to explain the essential reasons for the different choice values of arbitrary pair of objects and proposes techniques for translating the normal and pseudo parameter reduction problems of soft sets into several equivalent linear programming models.
Journal ArticleDOI
A decision making model using soft set and rough set on fuzzy approximation spaces
Tapan Kumar Das,D. P. Acharjya +1 more
TL;DR: This paper proposes a decision making model that consists of two processes such as preprocess and postprocess to mine decisions and uses rough set on fuzzy approximation spaces to get the almost equivalence classes whereas in postprocess the authors use soft set techniques to obtain decisions.
Characterization of Ordered Semigroups in Terms of Fuzzy Soft Ideals
TL;DR: In this paper, the authors apply the concept of fuzzy soft sets to ordered semigroup the-ory and derive three kinds of lattice structures of the set of all (2g;2g_qd)-fuzzy soft left (right) ideals of an ordered semiigroup.
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Pruning method for optimal solutions of intm–intn decision making scheme
TL;DR: A pruning method is developed which filters out those objects that cannot be elements of any optimal decision sets in the beginning and has higher efficiency in computing the optimal solutions of Feng et al.'s int m – int n decision making scheme.
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