N. O. Alshehri

Intuitionistic fuzzy cycles and intuitionistic fuzzy trees.

Connectivity has an important role in neural networks, computer network, and clustering. In the design of a network, it is important to analyze connections by the levels. The structural properties of intuitionistic fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of intuitionistic fuzzy bridges, intuitionistic fuzzy cut vertices, intuitionistic fuzzy cycles, and intuitionistic fuzzy trees in intuitionistic fuzzy graphs and investigate some of their interesting properties. Most of these various types are defined in terms of levels. We also describe comparison of these types.

A New Multi-Attribute Decision-Making Method Based on m-Polar Fuzzy Soft Rough Sets

We introduce notions of soft rough m-polar fuzzy sets and m-polar fuzzy soft rough sets as novel hybrid models for soft computing, and investigate some of their fundamental properties. We discuss the relationship between m-polar fuzzy soft rough approximation operators and crisp soft rough approximation operators. We also present applications of m-polar fuzzy soft rough sets to decision-making.

Intuitionistic Fuzzy Planar Graphs

Graph theory has numerous applications in modern sciences and technology. Atanassov introduced the concept of intuitionistic fuzzy sets as a generalization of fuzzy sets. Intuitionistic fuzzy set has shown advantages in handling vagueness and uncertainty compared to fuzzy set. In this paper, we apply the concept of intuitionistic fuzzy sets to multigraphs, planar graphs, and dual graphs. We introduce the notions of intuitionistic fuzzy multigraphs, intuitionistic fuzzy planar graphs, and intuitionistic fuzzy dual graphs and investigate some of their interesting properties. We also study isomorphism between intuitionistic fuzzy planar graphs.

Certain Types of Interval-Valued Fuzzy Graphs

We propose certain types of interval-valued fuzzy graphs including balanced interval-valued fuzzy graphs, neighbourly irregular interval-valued fuzzy graphs, neighbourly total irregular interval-valued fuzzy graphs, highly irregular interval-valued fuzzy graphs, and highly total irregular interval-valued fuzzy graphs. Some interesting properties associated with these new interval-valued fuzzy graphs are investigated, and necessary and sufficient conditions under which neighbourly irregular and highly irregular interval-valued fuzzy graphs are equivalent are obtained. We also describe the relationship between intuitionistic fuzzy graphs and interval-valued fuzzy graphs.

Cayley Bipolar Fuzzy Graphs

We introduce the concept of Cayley bipolar fuzzy graphs and investigate some of their properties. We present some interesting properties of bipolar fuzzy graphs in terms of algebraic structures. We also discuss connectedness in Cayley bipolar fuzzy graphs.

Applications of Soft Sets in -Algebras

In 1999, Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty and vagueness. In this paper, we apply the concept of soft sets to K-algebras and investigate some properties of Abelian soft K-algebras. We also introduce the concept of soft intersection K-algebras and investigate some of their properties.

Generalized Derivations of BCC-Algebras

The notion of generalized derivations of BCC-algebras is introduced, and some related properties are investigated. Also, we consider regular generalized derivations and the D-invariant on ideals of BCC-algebras. We also characterized KerD by generalized derivations.

Multi-Criteria Decision-Making Methods in Bipolar Fuzzy Environment

Bipolar fuzzy set theory, an extension of fuzzy set theory, deals with incomplete and vague information. The purpose of this research paper is to develop new methodologies in handling multi-criteria decision-making (MCDM) problems where the subjective data given by a decision maker are expressed with bipolar fuzzy information. Every alternative has a rating consists of two parts: positive and negative. The positive part represents the benefit (or satisfaction degree) and the negative part represents the cost (or dissatisfaction degree) of the alternative on the corresponding criterion. In this paper, we present bipolar fuzzy TOPSIS (BF-TOPSIS) method and bipolar fuzzy ELECTRE I (BF-ELECTRE I) method for solving MCDM problems that are equipped with bipolar fuzzy information. We illustrate our proposed methods with examples. We design algorithms of BF-TOPSIS method and BF-ELECTRE I method. We also give comparison of BF-TOPSIS method and BF-ELECTRE I method.

Hesitant Anti-Fuzzy Soft Set in BCK-Algebras

We introduce the notions of hesitant anti-fuzzy soft set (subalgebras and ideals) and provide relation between them. However, we study new types of hesitant anti-fuzzy soft ideals (implicative, positive implicative, and commutative). Also, we stated and proved some theorems which determine the relationship between these notions.

On m-polar fuzzy graph structures

Abstract Sometimes information in a network model is based on multi-agent, multi-attribute, multi-object, multi-polar information or uncertainty rather than a single bit. An m-polar fuzzy model is useful for such network models which gives more and more precision, flexibility, and comparability to the system as compared to the classical, fuzzy and bipolar fuzzy models. In this research article, we introduce the notion of m-polar fuzzy graph structure and present various operations, including Cartesian product, strong product, cross product, lexicographic product, composition, union and join of m-polar fuzzy graph structures. We illustrate these operations by several examples. We also investigate some of their related properties.