Virtual intuitionistic fuzzy parameterized intuitionistic fuzzy soft sets and their application in decision-making

Dalkılıç, Orhan; Demirtaş, Naime

doi:10.17714/gumusfenbil.993353

Cited by 2 publications

(2 citation statements)

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“…if ψ b j (Q) � 1, then (14) return Q is an exact set. ( 15) else (16) return Q is a rough set. ( 17) end (18) end (19) end ALGORITHM 1: An algorithm for using the j-approximations in decision-making problems.…”

Section: Yao Method L(a) � Qmentioning

confidence: 99%

“…Topologists used relations to build a general topology which is the applicable mathematical structure for any group linked by relations. We conclude that the relations have been entered to build topological structures in a variety of felds such as in rough sets and their extensions [8][9][10][11], rough multisets [12], decision-making problems [13][14][15][16], medical applications [14,[17][18][19], bipolar soft ordered topology [20], economic felds [21], topological reductions of attributes for predicting of a lung cancer disease [22] and heart failure [23], biochemistry [24][25][26], computer sciences [27][28][29][30], structure analysis [31], fuzzy soft approaches [32][33][34], topological study of zeolite socony mobil-5 [35], near sets theory [36], and covering rough sets [37][38][39][40]. In 2022, Dalkılıç [41] introduced some topological structures of virtual fuzzy parameterized fuzzy soft sets and proposed some applications of his methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Economic Decision-Making Using Rough Topological Structures

El-Gayar

Abu-Gdairi

El-Bably

et al. 2023

Journal of Mathematics

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This paper suggests new approximations that are inspired by topological structures. The primary goal of this work is to define four neighborhoods resulting from a binary relation. Thus, we have four distinct techniques for approximating rough sets. The suggested approaches represent topological generalizations of the previous works. The characteristics and connections of these approaches are investigated. For the sake of the application, we provide some useful examples to compare our techniques to those in the published literature. The merit of the current technique is to obtain a more accurate decision for the problems in which these cases are the appropriate frame to describe them; for instance, machine learning (ML, for short) applications of finance, etc. To demonstrate this fact, an economic application is proposed. We employ the proposed technique in defining accurate decisions to identify the growth of countries. An algorithm for decision-making problems is proposed and tested on fictitious data to compare our methods with the previous approaches.

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Section: Yao Method L(a) � Qmentioning

confidence: 99%