Search

The aim of this paper is to introduce the concept of a generalized Pythagorean fuzzy soft set (GPFSS), which is a combination of the generalized fuzzy soft sets and Pythagorean fuzzy sets. Several of important operations of GPFSS including complement, restricted union, and extended intersection are discussed. The basic properties of GPFSS are presented. Further, an algorithm of GPFSSs is given to solve the fuzzy soft decision-making. Finally, a comparative analysis between the GPFSS approach and some existing approaches is provided to show their reliability over them. [ABSTRACT FROM AUTHOR]

In this paper, we will propose the novel notion of neighborhood rough sets on a universe set and study some of their basic properties. Then, the relationships between the neighborhood rough sets and covering rough sets are established. Further, the several related notions of probabilistic neighborhood rough sets are investigated and their basic theoretical are discussed. In addition, the notion of neighborhood rough sets over two different universes is defined, and interesting in their properties are explained. Depend on the neighborhood rough sets over two different universes, two algorithms are designed to solve the rough decision-making problems and clarify their applicability by two illustrative examples, respectively. Finally, a comparison between Liu et al.'s approach and our approach is given. [ABSTRACT FROM AUTHOR]

This paper aims to propose the notion of Type-1 single-valued neutrosophic complementary β -neighborhood (briefly, Type-1 SVN complementary β -neighborhood) and use it to introduce a novel class of 1-single-valued neutrosophic β -covering rough set (briefly, 1-SVN β -CRS). Then, we will merge the 1-SVN β -neighborhood and 1-SVN complementary β -neighborhood to create new two models of 1-SVN β -CRS. Furthermore, we will discuss the relationships between the present work and Wang and Zhang's work. For further study on Type-2 Wang and Zhang's models, we will define the 2-SVN complementary β -neighborhood and use it to present a novel class of 2-SVN β -CRS. Also, we combine the 2-SVN β -neighborhood and 2-SVN complementary β -neighborhood to investigate the new two models of 2-SVN β -CRS. Lately, we will demonstrate two illustrative examples as real problems to show the differences between two of our approaches and Wang and Zhang's approach. [ABSTRACT FROM AUTHOR]

The aim of this article is to present the concept of restricted union and extended intersection of belief interval-valued soft sets, along with its properties. In addition, we propose the concept of possibility belief interval-valued soft set theory and investigate their properties. For suitability of possible applications, there are seven kinds of operations (e.g., union, intersection, restricted union, extended intersection, complement, soft max-AND, and soft min-OR) on the possibility belief interval-valued soft sets are defined and their basic theoretical are given. Then, we construct two algorithms by using soft max-AND and soft min-OR operations of possibility interval-valued soft sets for fuzzy decision-making problem. Lastly, we introduce an algorithm using a possibility interval-valued soft set to solve the decision-making problems and clarify its applicability by a numerical example. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel concept of the generalized picture fuzzy soft set by combining the picture fuzzy soft set and the fuzzy parameter set. For possible applications, we explain five kinds of operations (e.g., subset, equal, union, intersection, and complement) based on generalized picture fuzzy soft sets. Then, we establish several theoretical operations of generalized picture fuzzy soft sets. In addition, we present the new type by using the AND operation of the generalized picture fuzzy soft set for fuzzy decision-making and clarify its applicability with a numerical example. Finally, we give a comparison between the picture fuzzy soft set theory and the generalized picture fuzzy soft set theory. It is shown that our proposed (i.e., generalized picture fuzzy soft set theory) is viable and provide decision makers a more mathematical insight before making decisions on their options. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce and study different kinds of fuzzy soft β-neighborhoods called fuzzy soft β-adhesion neighborhoods and to analyze some of their properties. Further, the concepts of soft β-adhesion neighborhoods are investigated and the related properties are studied. Then, we present new kinds of lower and upper approximations by means of different fuzzy soft β-neighborhoods. The relationships among our models (i.e., Definitions 3.9, 3.12, 3.15 and 3.18) and Zhang models [48] are also discussed. Finally, we construct an algorithm based on Definition 3.12, when k = 1 to solve the decision-making problems and illustrate its applicability through a numerical example. [ABSTRACT FROM AUTHOR]

The major concern of this paper is to highlight the notion of possibility m-polar fuzzy soft set (because it is useful in decision-making and other similar problems). For convenience of practical applications, several operations (such as subset, equal, complement, union, intersection, inf product, and sup product) over the possibility m-polar fuzzy soft sets are introduced. We present two algorithms by using inf product or sup product operations of possibility m-polar fuzzy soft sets for fuzzy decision-making problem. Finally, we construct an algorithm using a possibility m-polar fuzzy soft set to solve the decision-making problems and illustrate its applicability through a numerical example. From the study, we conclude that the proposed approach is viable in order to handle the uncertainties during the decision-making problems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to correct the assertions (3) and (4) of Theorem 3.23 proposed by Zhang and Shu [Journal of Intelligent and Fuzzy Systems 27 (2014) 2115-2125]. A counterexample illustrates the flaw of the assertions.We introduce the notion of generalized possibility multi-fuzzy soft sets and use it to correct the flawin those assertions. Finally we introduce two new definitions and five theorems to improve the work of Zhang and Shu, along with two examples. [ABSTRACT FROM AUTHOR]

We show that the assertions (vii) and (viii) of Theorem 3.1 proposed by Agarwal et al. [Appl. Soft Comput. 13 (2013) 3552-3566] are incorrect by a counterexample. [ABSTRACT FROM AUTHOR]