A Configuration of Some Soft Decision-Making Algorithms via fpfs-matrices

Abstract

Since many problems have a large amount of data or uncertainty, the computer mathematics has become compulsory. To deal with such kinds of these problems, the concept of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) has been defined by Enginoğlu. In this paper, we first give some of its basic definitions. We then configure some decision-making algorithms constructed by soft sets, fuzzy soft sets, fuzzy parameterized soft sets, fpfs-sets, and their matrix representations. We finally discuss later works.

Configurations of Several Soft Decision-Making Methods to Operate in Fuzzy Parameterized Fuzzy Soft Matrices Space

“…Secondly, we give these configured methods MBR01, MRB02, and CCE10 as provided in [27]. Here, MBR01 refers to the method constructed by Maji, Biswas, and Roy in 2001 and MRB02 to the method constructed by Maji, Roy, and Biswas in 2002.…”

“…Obtain the decision set { ( ) | ∈ } such that ( ) = 1 max 1 Note 1. Since the reduction steps in the original algorithm lead to some errors [49,50], these steps have not been considered by the authors in [27].…”

A Generalization of Fuzzy Soft Max-Min Decision-Making Method and Its Application to a Performance-Based Value Assignment in Image Denoising

“…Soft sets [1] are designed to cope with uncertainties, and so far, many applied and theoretical studies have been conducted on that . Recently, some soft decision-making algorithms have been configured via fuzzy parameterized fuzzy soft matrices ( -matrices) by Enginoğlu and Memiş [34], faithfully to the original. Moreover, the authors have remarked that studies on the simplifications and different configurations of these methods therein are worth doing.…”

“…Moreover, the authors have remarked that studies on the simplifications and different configurations of these methods therein are worth doing. In the recent time, several soft decision-making algorithms given in [34] have been configured and simplified [35][36][37][38][39] to apply them to a decision-making problem in computer science such as image denoising and machine learning.…”

“…The soft max-min decision-making method SMmDM and the fuzzy soft max-min decision-making method FSMmDM provided in [5,12] and configured in [34] have a similar disadvantage considered in [35][36][37][38][39]. Therefore, in this paper, we have focused on improving two new methods being a different generalisation of them and free of the disadvantages mentioned above.…”