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Open AccessJournal ArticleDOI

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.

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Book ChapterDOI

Hybrid reduction in soft set decision making

TL;DR: An extended technique of decision making by implementing column reduction with reduction based on calculated maximal support objects and shows that the proposed model of hybrid reduction yielded a better data size reduction whilst still maintaining consistent results.
Proceedings ArticleDOI

Distance and Similarity Measures of Interval Neutrosophic Soft Sets

TL;DR: In this paper, distance and similarity measures of interval neutrosophic soft sets are introduced based on the geometric model, the set theoretic approach and the matching function, and the similarity measure has successfully shown an application of this similarity measure.
Journal Article

Multi-Valued Interval Neutrosophic Soft Set: Formulation and Theory

TL;DR: This paper introduces a concept of multi-valued interval neutrosophic soft set which amalgamates multi- valued interval neut ROSophic set and soft set and studies some basic operations such as complement, equality, inclusion, union, intersection, “AND” and “OR” for multi- valuation neutrosphic soft elements.
Posted Content

Generalised intuitionistic fuzzy soft sets and its application in decision making

TL;DR: An application of generalised intuitionistic fuzzy soft sets in decision making with respect to degree of preference is investigated.
Journal Article

Multi-Polar Neutrosophic Soft Sets with Application in Medical Diagnosis and Decision-Making

TL;DR: A Similarity measure for Neutrosophic function performs a fundamental role in tackling the problems that include blurred and hazed information but is not able to handle the fuzziness and vagueness of the problems which have numerous information.
References
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