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Multi-attribute decision-making based on aggregations and similarity measures of neutrosophic hypersoft sets with possibility setting

Authors :
Rahman, Atiqe Ur
Saeed, Muhammad
Abd El-Wahed Khalifa, Hamiden
Source :
Journal of Experimental & Theoretical Artificial Intelligence; February 2024, Vol. 36 Issue: 2 p161-186, 26p
Publication Year :
2024

Abstract

ABSTRACTPossibility neutrosophic hypersoft set is the generalisation of possibility neutrosophic soft set, and it is the blend of possibility neutrosophic set and hypersoft set. It tackles the insufficiencies of possibility intuitionistic fuzzy set and soft set for the entitlement of degree of indeterminacy and multi-argument approximate function, respectively. This kind of function maps the sub-parametric tuples to the power set of universe. It emphasises the partitioning of each attribute into its attribute-valued set that is missing in the existing fuzzy soft set-like structures. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. This research aims to initiate a novel structure that hybridises the possibility neutrosophic set with the hypersoft set for dealing with uncertainties efficiently. Therefore, the possibility neutrosophic hypersoft set is developed by employing theoretic, axiomatic, graphical and algorithmic approaches. After conceptual characterisation of its essential elementary notions, decision-support systems are presented based on AND and OR operations with the proposal of algorithms to assist the decision-making process. Similarity measures between possibility neutrosophic hypersoft sets are elaborated with the support of daily life applications. The comparison of the proposed work is discussed with the existing relevant models under certain evaluating features.

Details

Language :
English
ISSN :
0952813x and 13623079
Volume :
36
Issue :
2
Database :
Supplemental Index
Journal :
Journal of Experimental & Theoretical Artificial Intelligence
Publication Type :
Periodical
Accession number :
ejs65073280
Full Text :
https://doi.org/10.1080/0952813X.2022.2080869