Fuzzy Soft Expert Set Research Articles

Novel MCGDM analysis under m-polar fuzzy soft expert sets

Multi-criteria group decision-making (MCGDM) is an approach to tackle different decision-making situations, where the criteria that drive the decision are considered by different experts. Research regarding MCGDM is quite challenging due to both the structure of the problem and the existence of complex uncertain information. The goal of this article is to produce a new MCGDM model which covers criteria evaluation by different experts. A novel hybrid model called m-polar fuzzy soft expert sets is developed by the combination of m-polar fuzzy sets with soft expert sets; thus, it investigates soft expert sets in the m-polar fuzzy environment. The characteristics of this hybrid model are explored with the aid of numerical examples. Further, its basic properties are investigated, and the operations of subsethood, complement, intersection, union, plus the OR and AND operators are introduced. Two well-known real-world problems are solved with the developed hybrid model, namely site selection for a dam and human trafficking analysis for different countries. The algorithm of the proposed model proves its efficiency and cogency. A comparison of the developed model with existing mathematical methods is provided.

Application of Decision Making Problem in Bipolar Fuzzy Soft Expert Set

Application of Decision Making Problem in Bipolar Fuzzy Soft Expert Set

Fuzzy Parameterized Bipolar Fuzzy Soft Expert Set and Its Application in Decision Making

Fuzzy Parameterized Bipolar Fuzzy Soft Expert Set and Its Application in Decision Making

Recommendation system for under graduate students using FSES-TOPSIS

Existing recommendation systems lack to address the need of several problems in education domain due to the availability of limited information. Study of recommendation systems to facilitate students’ education in their relevant services is being carried out in different perspective. Hence, the objective of this work is to apply existing knowledge for decision making to recommend students during their course registration process in an institution. The situation where more number of experts involves in decision making was influenced by Fuzzy Soft Expert Set (FSES). This work emphasizes the application of FSES for facilitating students’ course registration process assuming that the same course is offered by more number of faculty members during a semester. As more number of experts involved in this process and the uncertainty of the available data, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) along with FSES was considered. A new methodology FSES-TOPSIS was also applied in this work. The proposed method was accurate enough to enable students to rank the faculty members based on their previous performance and to enroll themselves with the faculty members of their interest. The method proposed here can be extended for various problems of similar nature.

Fuzzy Parameterized Complex Neutrosophic Soft Expert Set for Decision under Uncertainty

In the definition of the complex neutrosophic soft expert set (CNSES), parameters set is a classical set, and the parameters have the same degree of importance, which is considered as 1. This poses a limitation in modeling of some problems. This paper introduces the concept of fuzzy parameterized complex neutrosophic soft expert set (FP-CNSES) to handle this issue by assigning a degree of importance to each of the problem parameters. We further develop FP-CNSES by establishing the concept of weighted fuzzy parameterized complex neutrosophic soft expert set (WFP-CNSES) based on the idea that each expert has a relative weight. These new mathematical frameworks reduce the chance of unfairness in the decision making process. Some essential operations with their properties and relevant laws related to the notion of FP-CNSES are defined and verified. The notation of mapping on fuzzy parameterized complex neutrosophic soft expert classes is defined and some properties of fuzzy parameterized complex neutrosophic soft expert images and inverse images was investigated. FP-CNSES is used to put forth an algorithm on decision-making by converting it from complex state to real state and subsequently provided the detailed decision steps. Then, we provide the comparison of FP-CNSES to the current methods to show the ascendancy of our proposed method.

Fuzzy Parameterized Complex Multi-Fuzzy Soft Expert Set Theory and Its Application in Decision-Making

Contemporary research has refined systems with complex fuzzy sets in order to improve the design and model of real-life applications. Symmetry and antisymmetry are basic characteristics of binary relations used when modeling the decision maker’s preferences. A recent focus has been the analysis of a complex data set using the properties of fuzzy concept lattice and the complex soft set. We will introduce a new concept to represent the information which utilizes the time factor, called fuzzy parameterized complex multi-fuzzy soft expert set ( F P - CMFSES ), and investigate part of its fundamental properties. This F P - CMFSES model allows us to validate the information provided by an expert, at a given phase of time, using the properties of complex fuzzy sets. We then construct an algorithm based on this concept by converting it from the complex state to the real state. Eventually, we implement it to a decision-making problem to demonstrate the applicability of the suggested method. A comparison among F P - CMFSES and other existing methods is made to expose the dominance of the suggested method. Apart from that, we also propose the weighted fuzzy parameterized complex multi-fuzzy soft expert set and investigate its application to decision-making.

Enhanced Decision Support System Applications Based on Complex Intuitionistic Fuzzy Soft Expert Sets Theory

Decision making is one of the important milestones in the development of various real-life aspects. Decision Support System (DSS) is a computerized information system used to enhance decision-making in several areas. This study aims to improve DSS applications by extending two concepts of complex intuitionistic fuzzy sets and soft expert sets to propose the theory of complex intuitionistic fuzzy soft expert sets and defines some related concepts pertaining to this notion as well as the basic operations on this concept. Moreover, the algorithm for such concept is developed.

Bipolar fuzzy soft expert set and its application in decision making

In this paper, we extend the two concepts of bipolar fuzzy sets and soft expert sets to bipolar fuzzy soft expert sets. We will define its basic theoretic operation, namely complement, union, intersection, AND and OR on bipolar fuzzy soft expert sets along with illustrative examples, and study some related properties with supporting proofs. The basic properties and relevant laws pertaining to this concept are proven. We then construct an algorithm based on this concept. Finally, we apply it to a decision-making problem to demonstrate the applicability of the proposed method. It is shown that this concept is effective in solving decision-making problems using an illustrative example.

Bipolar Fuzzy Soft Expert Set and its Application in Decision Making

Bipolar Fuzzy Soft Expert Set and its Application in Decision Making

Fuzzy parameterized intuitionistic fuzzy soft expert set theory and its application in decision making

Fuzzy parameterized intuitionistic fuzzy soft expert set theory and its application in decision making

Possibility Intuitionistic Fuzzy Soft Expert Set Theory and Its Application in Decision Making

We propose the theory of possibility intuitionistic fuzzy soft expert theory and define some related concepts pertaining to this notion as well as the basic operations on this concept, namely, the complement, union, intersection, AND, and OR. The basic properties and relevant laws pertaining to this concept such as De Morgan’s laws are proved. Lastly, a generalized algorithm is introduced and applied to the concept of possibility intuitionistic fuzzy soft expert sets in hypothetical decision making problem.

Analysis of S-box image encryption based on generalized fuzzy soft expert set

This article is about a criterion based on generalized fuzzy soft expert set to scrutinize the prevailing substitution boxes and learn their properties to sort out their suitability in image encryption applications. The projected criterion makes use of the results of correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis. These analyses are applied to Advanced Encryption Standard S-box, Affine-Power-Affine S-box, Gray S-box, \(\hbox {S}_{8}\) S-box, residue prime S-box, SKIPJACK S-box and Liu J S-box. The outcome of these analyses is additional observed and a generalized fuzzy soft expert set criterion is used to decide the suitability of an S-box to image encryption applications.

Fuzzy Soft Expert Set and Its Application

In 1999, Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. Alkhazaleh and Salleh (2011) define the concept of soft expert sets where the user can know the opinion of all experts in one model and give an application of this concept in decision making problem. So in this paper, we generalize the concept of a soft expert set to fuzzy soft expert set, which will be more effective and useful. We also define its basic operations, namely complement, union, intersection, AND and OR. We give an application of this concept in decision making problem. Finally, we study a mapping on fuzzy soft expert classes and its properties.

Generalized Fuzzy Soft Expert Set

In 2011 Alkhazaleh and Salleh defined the concept of soft expert sets where the user can know the opinion of all the experts in one model and give an application of this concept in decision‐making problems. Also, they introduced the concept of the fuzzy soft expert set as a combination between the soft expert set and the fuzzy set. In 2010 Majumdar and Samanta presented the concept of a generalized fuzzy soft sets. The purpose of this paper is to combine the work of Alkhazaleh and Salleh (2011) and Majumdar and Samanta (2010), from which we can obtain a new concept: generalized fuzzy soft expert sets (GFSESs). We also introduce its operations, namely, complement, union intersection, “AND” and “OR”, and study their properties. The generalized fuzzy soft expert sets are used to analyze a decision‐making problem. Also in our model the user can know the opinion of all experts in one model. In this work we also introduce the concept of a generalized fuzzy soft expert sets with multiopinions (four opinions), which will be more effective and useful. Finally, we give an application of this concept in decision‐making problem.

Possibility Fuzzy Soft Expert Set

In this paper we introduce the concept of possibility fuzzy soft expert set .We also define its basic operations, namely complement, union, intersection, AND and OR, and study some of their properties. Finally, we give an application of this theory in solving a decision making problem.