A Study Of The Fundamentals Of Hypersoft Set Theory
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A Study of The Fundamentals of Hypersoft Set Theory
In this study, we discuss the fundamentals of hypersoft set such as hypersoft subset, complement, not hypersoft set and aggregation operators. After that we discuss the hypersoft set relation, sub relation, complement relation, function, matrices and operations on hypersoft matrices.
Theory and Application of Hypersoft Set
Author: Florentin Smarandache
language: en
Publisher: Infinite Study
Release Date: 2021-02-01
Florentin Smarandache generalize the soft set to the hypersoft set by transforming the function 𝐹 into a multi-argument function. This extension reveals that the hypersoft set with neutrosophic, intuitionistic, and fuzzy set theory will be very helpful to construct a connection between alternatives and attributes. Also, the hypersoft set will reduce the complexity of the case study. The Book “Theory and Application of Hypersoft Set” focuses on theories, methods, algorithms for decision making and also applications involving neutrosophic, intuitionistic, and fuzzy information. Our goal is to develop a strong relationship with the MCDM solving techniques and to reduce the complexion in the methodologies. It is interesting that the hypersoft theory can be applied on any decision-making problem without the limitations of the selection of the values by the decision-makers. Some topics having applications in the area: Multi-criteria decision making (MCDM), Multi-criteria group decision making (MCGDM), shortest path selection, employee selection, e-learning, graph theory, medical diagnosis, probability theory, topology, and some more.
A Novel MCDM Method Based on Plithogenic Hypersoft Sets under Fuzzy Neutrosophic Environment
In this paper, we advance the study of plithogenic hypersoft set (PHSS).We present four classifications of PHSS that are based on the number of attributes chosen for application and the nature of alternatives or that of attribute value degree of appurtenance. These four PHSS classifications cover most of the fuzzy and neutrosophic cases that can have neutrosophic applications in symmetry. We also make explanations with an illustrative example for demonstrating these four classifications. We then propose a novel multi-criteria decision making (MCDM) method that is based on PHSS, as an extension of the technique for order preference by similarity to an ideal solution (TOPSIS).