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Multi-Attribute Decision-Support System Based on Aggregations of Interval-Valued Complex Neutrosophic Hypersoft Set
Author(s) -
Atiqe Ur Rahman,
Muhammad Saeed,
Muhammad Arshad,
Salwa El-Morsy
Publication year - 2021
Publication title -
applied computational intelligence and soft computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 10
eISSN - 1687-9732
pISSN - 1687-9724
DOI - 10.1155/2021/4368770
Subject(s) - computer science , set (abstract data type) , soft set , generalization , universal set , interval (graph theory) , fuzzy set , function (biology) , set function , theoretical computer science , data mining , artificial intelligence , algorithm , fuzzy logic , mathematics , mathematical analysis , combinatorics , evolutionary biology , biology , programming language
Hypersoft set is an emerging field of study that is meant to address the insufficiency and the limitation of existing soft-set-like models regarding the consideration and the entitlement of multi-argument approximate function. This type of function maps the multi-subparametric tuples to the power set of the universe. It focuses on the partitioning of each attribute into its attribute-valued set that is missing in existing soft-set-like structures. This study aims to introduce novel concepts of complex intuitionistic fuzzy set and complex neutrosophic set under the hypersoft set environment with interval-valued settings. Two novel structures, that is, interval-valued complex intuitionistic hypersoft set (IV-CIFHS-set) and interval-valued complex neutrosophic hypersoft set (IV-CNHS-set), are developed via employing theoretic, axiomatic, graphical, and algorithmic approaches. After conceptual characterization of essential elementary notions of these structures, decision-support systems are presented with the proposal of algorithms to assist the decision-making process. The proposed algorithms are validated with the help of real-world applications. A comprehensive inter-cum-intra comparison of proposed structures is discussed with the existing relevant models, and their generalization is elaborated under certain evaluating features.

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