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Fuzzy Multiple Attribute Decision Making Methods And Applications

This paper presents three novel single-valued neutrosophic soft set (SVNSS) methods. First, we initiate a new axiomatic definition of single-valued neutrosophic similarity measure, which is expressed by single-valued neutrosophic number (SVNN) that will reduce the information loss and remain more original information. Then, the objective weights of various parameters are determined via grey system theory.

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Algorithms for Neutrosophic Soft Decision Making Based on Edas, New Similarity Measure and Level Soft Set

In this paper, with the aid of fuzzy soft $$\beta $$
 -neighborhoods, we introduce fuzzy soft covering-based multi-granulation fuzzy rough set models. We examine some of the relevant properties of fuzzy soft covering based on optimistic, pessimistic, and variable precision multi-granulation fuzzy rough set models. Then, we give fuzzy soft coverings based on $$\psi $$
 -optimistic and $${\mathscr {D}}$$
 -optimistic (
 $$\psi $$
 -pessimistic and $${\mathscr {D}}$$
 -pessimistic) multi-granulation fuzzy rough sets from fuzzy soft measures. It also discusses the interactions between these forms of fuzzy soft coverings based on multi-granulation fuzzy rough sets. Eventually, we apply the proposed models for solving MAGDM problems. The effectiveness and feasibility of our approach are noted from the introduced comparisons between our method and some methods given in the previous studies.

Fuzzy soft covering-based multi-granulation fuzzy rough sets and their applications

Covering-based rough sets are important generalizations of the classical rough sets of Pawlak. In this paper, by means of j-neighborhoods, complementary j-neighborhoods and j-adhesions, we build some new different types of j-covering approximations based rough sets and study related properties. Also, we explore the relationships between the considered j-covering approximations and investigate the properties of them. Using different neighborhoods, some different general topologies are generated as topologies induced from a binary relation. Finally, an interesting application of the new types of covering-based rough sets to the rheumatic fever is given.

Certain types of coverings based rough sets with application

Soft rough set theory has been presented as a basic mathematical model for decision-making for many real-life data However, soft rough sets are based on a possible fusion of rough sets and soft sets which were proposed by Feng et al [20] The main contribution of the present article is to introduce a modification and a generalization for Feng's approximations, namely, soft β -rough approximations, and some of their properties will be studied A comparison between the suggested approximations and the previous one [20] will be discussed Some examples are prepared to display the validness of these proposals Finally, we put an actual example of the infections of coronavirus (COVID-19) based on soft β -rough sets This application aims to know the persons most likely to be infected with COVID-19 via soft β -rough approximations and soft β -rough topologies [ABSTRACT FROM AUTHOR] Copyright of Turkish Journal of Mathematics is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Soft β-rough sets and their application to determine COVID-19

Neighborhood systems are used to approximate graphs as finite topological structures. Throughout this article, we construct new types of eight neighborhoods for vertices of an arbitrary graph, say, j-adhesion neighborhoods. Both notions of Allam et al. and Yao will be extended via j-adhesion neighborhoods. We investigate new types of j-lower approximations and j-upper approximations for any subgraph of a given graph. Then, the accuracy of these approximations will be calculated. Moreover, a comparison between accuracy measures and boundary regions for different kinds of approximations will be discussed. To generate j-adhesion neighborhoods and rough sets on graphs, some algorithms will be introduced. Finally, a sample of a chemical example for Walczak will be introduced to illustrate our proposed methods.

/pdf/rough-approximation-models-via-graphs-based-on-neighborhood-4t3ftb4tc6.pdf

Rough approximation models via graphs based on neighborhood systems

In this paper, new forms of nano topological spaces through a neighborhood system of vertices for a digraph will be presented and studied. We apply the connection between digraph theory and nano to...

A model of a human heart via graph nano topological spaces

The rough set principle was proposed as a methodology to cope with vagueness or uncertainty of data in the information systems. Day by day, this theory has proven its efficiency in handling and modeling many real-life problems. To contribute to this area, we present new topological approaches as a generalization of Pawlak’s theory by using j-adhesion neighborhoods and elucidate the relationship between them and some other types of approximations with the aid of examples. Topologically, we give another generalized rough approximation using near open sets. Also, we generate generalized approximations created from the topological models of j-adhesion approximations. Eventually, we compare the approaches given herein with previous ones to obtain a more affirmative solution for decision-making problems.

/pdf/some-topological-approaches-for-generalized-rough-sets-and-3tfkxyxt.pdf

Some Topological Approaches for Generalized Rough Sets and Their Decision-Making Applications

Fuzzy graphs are an individual of application tools in the area of mathematics, which permit the users to define the relative between concepts because the wildlife of fuzziness is satisfactory for any situation. They are helpful to give more exactness and suppleness to the classification as associated with the traditional models. A topological structure is a set model for graphs. The main purpose of this paper is to introduce a new kind of fuzzy topological structures in terms of fuzzy graphs called fuzzy topological graphs due to a class of fuzzy subsets, and some of their properties are investigated. Also, a new procedure to calculate the number of edges in fuzzy graphs will be defined. Further, we consider the concept of a homeomorphic between fuzzy topological graphs as a fuzzy topological property that can be used to prove the isomorphic between fuzzy graphs. Moreover, an algorithm based on the proposed operations that build some fuzzy topological graphs will be presented. Finally, we give a new method to explain the homeomorphic between some fuzzy topological graphs which will be applied in smart cities.

A. S. Nawar

Papers

Rough approximation models via graphs based on neighborhood systems

Certain types of coverings based rough sets with application

A model of a human heart via graph nano topological spaces

Some Topological Approaches for Generalized Rough Sets and Their Decision-Making Applications

Fuzzy topological structures via fuzzy graphs and their applications