This study introduces a novel approach to decision-making problems, especially in the context of hard disk selection, using the concept of the fuzzy parameterized single-valued neutrosophic soft set (FP-SVNSS). Primarily, the focus is on assigning different levels of importance to each parameter within the set, which enables a more nuanced and flexible evaluation process. This is underpinned by the development of several related concepts and the definition of basic operations such as complement, subset, union, and intersection. In the quest for clarity, the nuances of these operations and the overall framework of the FP-SVNSS method are illustrated via numerous examples. The superiority of the FP-SVNSS method over other decision-making methods is affirmed through a comprehensive comparison. The unique strength of the proposed approach lies in its ability to handle imperfect, ambiguous, and inconsistent data. Consequently, it offers greater accuracy and practicality than existing models. In the latter part of the study, the theory is put to the test by tackling a real-world decision-making problem. The selected case involves the optimal selection of hard disks, a common issue in information technology procurement. The successful application of the FP-SVNSS method to this issue provides a compelling demonstration of its potential value in practical settings. Through the exploration of this innovative decision-making methodology, this research contributes to the broader field of soft computing and decision-making theory. The findings suggest a myriad of future applications of the FP-SVNSS method in dealing with various complex and fuzzy problems in both academic and industrial contexts.

Muhammad Ihsan, Atiqe Ur Rahman, Muhammad Saeed, and Hamiden Abd El-Wahed Khalifa. International Journal of Fuzzy Logic and Intelligent Systems 2021;21:233-42. https://doi.org/10.5391/IJFIS.2021.21.3.233

Soft set theory is considered as one of the best effective tool which provides parameterization approach to tackle the inadequacy of fuzzy set. So far, it has been applied to different mathematical concepts such as set operations, algebraic structure (e.g., group and ring theory) and topological spaces. Many researchers have studied classical concept of convex and concave set under fuzzy-like, soft-like and fuzzy soft-like environments. In this paper, new notions of (m, n)-convex and (m, n)- concave fuzzy soft sets are developed first and then their versions for first and second senses are established. Further some known classical results and properties are generalized under fuzzy soft set environment. Moreover, special cases of (m, n)-convexity on fuzzy soft sets are established

In many real-world decision-making situations, uncertain nature of parameters is to be discussed to have unbiased and reliable decisions. Most of the existing literature on fuzzy soft set and its related structures ignored the uncertain parametric attitudes. The concept of fuzzy parameterization is launched to tackle the limitations of existing soft set-like models. Several extensions have already been introduced by using the concept of fuzzy parameterization. In this research, a novel extension, fuzzy parameterized Pythagorean fuzzy hypersoft expert set is aimed to be characterized. This model is more flexible and reliable as compared to existing models because it addresses their insufficiencies for the consideration of multi-argument approximate function. With the entitlement of this function, it tackles the real-life scenarios where each attribute is meant to be further classified into its respective sub-attribute valued disjoint set. The characterization of fuzzy parameterized Pythagorean fuzzy hypersoft expert set is accomplished by employing theoretic, axiomatic and algorithmic approaches. In order to validate the proposed model, an algorithm is proposed to study its role in decision-making while dealing with real-world problem. Moreover, the proposed model is compared with the most relevant existing models to assess its advantageous aspects.