Sets In Decision Making Problem Research Articles

Decision-making in diagnosing heart failure problems using basic rough sets

<p>This manuscript introduces novel rough approximation operators inspired by topological structures, which offer a more flexible approach than existing methods by extending the scope of applications through a reliance on a general binary relation without constraints. Initially, four distinct types of neighborhoods, termed basic-minimal neighborhoods, are generated from any binary relation. The relationships between these neighborhoods and their properties are elucidated. Subsequently, new rough set models are constructed from these neighborhoods, outlining the main characteristics of their lower and upper approximations. These approximations are applied to classify the subset regions and to compute the accuracy measures. The primary advantages of this approach include its ability to achieve the highest accuracy values compared to all approaches in the published literature and to maintain the monotonicity property of the accuracy and roughness measures. Furthermore, the efficacy of the proposed technique is demonstrated through the analysis of heart failure diagnosis data, showcasing a 100% accuracy rate compared to previous methods, thus highlighting its clinical significance. Additionally, the topological properties of the proposed approaches and the topologies generated from the suggested neighborhoods are discussed, positioning these methods as a bridge to more topological applications in the rough set theory. Finally, an algorithm and flowchart are developed to illustrate the determination and utilization of basic-minimal exact sets in decision-making problems.</p>

A Novel Approach towards Bipolar Soft Sets and Their Applications

The notion of bipolar soft sets has already been defined, but in this article, the notion of bipolar soft sets has been redefined, called T-bipolar soft sets. It is shown that the new approach is more close to the concept of bipolarity as compared to the previous ones, and further it is discussed that so far in the study of soft sets and their generalizations, the concept introduced in this manuscript has never been discussed earlier. We have also discussed the operational laws of T-bipolar soft sets and their basic properties. In the end, we have deliberated the algebraic structures associated with T-bipolar soft sets and the applications of T-bipolar soft sets in decision-making problems.

Some new operations on Atanassov’s intuitionistic fuzzy sets in decision-making problems

Some new operations on Atanassov’s intuitionistic fuzzy sets in decision-making problems

Soft Incomplete Discernibility Matrix for Decision-Making Problems

Many decision-making processes are determined in an incomplete data environment. The analysis of decision-making problems on incomplete soft set data is usually performed by first estimating incomplete values. In this paper, we analyze the decision-making problems of incomplete soft sets with incomplete data without turning them into complete data. We define a soft incomplete discernibility matrix and a soft parameter dominant incomplete discernibility matrix to solve the application of incomplete soft sets in decision-making problems. We focus on the classification ability of the corresponding parameter for a given incomplete soft set. The novel method maintains the original data state and successfully constructs a decision-making approach that can be applied to incomplete soft sets. Finally, the weighted soft incomplete discernibility matrix is defined for application in weighted parameter decision-making issues.

Certain Applications of Fuzzy Parameterized Fuzzy Soft Sets in Decision-Making Problems

<p>In this paper, we present certain applications of fuzzy parameterized fuzzy soft sets (FPFS-sets) in decision-making problems. We define AND and OR operations for FPFS-sets. We study aggregation operator, reduct and comparison tables for FPFS-set. We present our methods in the form of some algorithms with the help of<br />examples.</p>