Abstract
Hypersoft set theory is an extension of soft set theory and is a new mathematical tool for dealing with fuzzy problems; however, it still suffers from the parametric tools’ inadequacies. In order to boost decision-making accuracy even more, a new mixed mathematical model called the bipolar hypersoft set is created by merging hypersoft sets and bipolarity. It is characterized by two hypersoft sets, one of which provides positive information and the other provides negative information. Moreover, some fundamental properties relative to it such as subset, superset, equal set, complement, difference, relative (absolute) null set and relative (absolute) whole set are defined. Furthermore, some set-theoretic operations such as the extended intersection, the restricted union, intersection, union, AND-operation and OR-operation of two bipolar hypersoft sets with their properties are discussed and supported by examples. Finally, tabular representations for the purposes of storing bipolar hypersoft sets in computer memory are used.
1. Introduction
A soft set is made up of two parts—a predicate and an approximate value set—and it provides an approximate description of the object under consideration. In classical mathematics, exact solutions to mathematical models are often needed. If the model’s complications or complexities increase, it becomes more difficult to obtain exact solutions; instead, approximate solutions can be obtained by using a variety of methods. In soft set theory, on the other hand, we do not need to incorporate the principle of exact solution since the initial description of the object is approximate.
Soft set theory is a fully generic mathematical tool for modeling uncertainties that was introduced by Russian researcher Molodtsov [] in 1999. There are no restrictions on how objects are described and, thus, researchers can use any type of parameter they want, which significantly simplifies the decision-making process and renders it more effective and accurate in the face of incomplete data. Many techniques exist for modeling real-world complex systems, including classical probability theory, fuzzy set theory, interval mathematics and so on. All of these techniques have a weak point in that they lack parameterization, which means that they cannot be used to tackle problems in fields such as economics, environmental science and social science. Soft set theory is largely free of the difficulties associated with the methods described above and it has a broader scope for a variety of multidimensional applications. In the sense of soft sets, various operations analogous to union, intersection, complement, difference and others in set theory have been discussed. Definitions and results can be found in [,,,]. Moreover, some important works on soft sets can be found in [,,,,].
In 2013, Shabir and Naz [] independently brought out the concept of bipolar soft set as a hybrid of bipolarity [] and soft set theory []. Our decision-making is based on two sides, which are positive and negative, according to Dubois and Prade [] and we choose based on which one is better
A bipolar soft set is provided by considering not only a carefully chosen set of parameters but also an allied set of opposite meaning parameters named ”Not set of parameters”. As a result, a bipolar soft set is made up of two soft sets, one of which represents the positive side and the other the negative. Some notions, properties, operations and a bipolar soft set application in decision-making problems were investigated in []. Later on, Fatimah, Rosadi, Hakim and Alcantud [] proposed N-soft sets and they solved decision-making algorithms by using N-soft sets. Naz and Shabir [] brought out the concept of fuzzy bipolar soft sets and discussed their algebraic structures and applications. Abdullah, Aslam and Ullah [] introduced the bipolar fuzzy soft set and studied its applications in the decision-making problem. Karaaslan and Çaǧman [] pointed out the bipolar soft rough set and studied utilization in decision making. Shabir and Gul [] modified rough bipolar soft sets. Karaaslan and Karatas [] reintroduced a bipolar soft set by using a bijective map between a set of parameters and its negative. Karaaslan, Ahmad and Ullah [] constructed bipolar soft groups and investigated some of its properties. Ozturk [] defined bipolar soft topological spaces and obtained some properties and results on them. Fadel and Dzul-Kifli [] provided more properties on bipolar soft topological spaces. Malik and Shabir [] successfully applied rough fuzzy bipolar soft sets in decision-making problems. Many more authors have auspiciously adapted bipolar soft set theory in various fields (see Riaz and Tehrim [,,,], Zhang [], Ali et al. [], Al-shami [], Mahmood [], Wang et al. [], Ali et al. [], Kamacı and Petchimuthu [] and Lee and Hur []).
On the other part, by replacing the function F with a multi-argument function defined on the Cartesian product of n different sets of parameters, Smarandache [] extended the notion of a soft set to the hypersoft set in 2018. This definition is more adaptable than the soft set and better suited for decision-making problems. A hypersoft set has gained more importance as a generalization of soft sets and has been investigated for possible extensions in many fields of mathematics. Saeed et al. [,] and Abbas et al. [] introduced and studied several operations on hypersoft sets. Recently, hypersoft sets have been expanded by embedding the idea of the fuzzy set, intuitionistic fuzzy set, neutrosophic set and plithogenic set to expand the field of application of hypersoft sets. For instance, the fuzzy hypersoft set [,], intuitionistic fuzzy hypersoft set [,], neutrosophic hypersoft set [,], plithogenic hypersoft set [], convex hypersoft sets and concave hypersoft sets [] are some of the focuses of certain studies. Recently, several researchers have auspiciously adapted the hypersoft set theory in various fields (see Martin et al. [], Zulqarnain et al. [,], Ahmad et al. [], Zhang [], Deli [], Al-Tahan et al. [], Saqlain et al. [] and Rahman et al. []).
The paper is structured as follows: Firstly, we recall the necessary background on hypersoft sets and bipolar soft sets. Secondly, we present some operations on bipolar hypersoft set theory, such as subset, complement, difference, extended intersection, restricted union, intersection, union, difference, AND and OR with their properties. Thirdly, we proposed the necessity and possibility operations and some properties and examples. Furthermore, tabular representations are used for the purpose of storing bipolar hypersoft sets in a computer. Finally, we conclude the paper.
2. Hypersoft Sets and Bipolar Soft Sets
2.1. Hypersoft Sets
Let be an initial universe, the power set of and the pairwise of disjoint sets of parameters. Let for .
Definition 1
([]). A pair is called a hypersoft set over , where is a mapping given by .
For the sake of simplicity, we write the symbol for and for the subsets of . The symbols for , for and for . Clearly, each element in and is an n-tuple element.
Definition 2
([]). For two hypersoft sets and over a common universe , we say that is a hypersoft subset of if the following is the case:
- and;
- for all .
We write .
is said to be a hypersoft superset of if is a hypersoft subset of . We denote it by .
Definition 3
([]). Two hypersoft sets and over a common universe are said to be hypersoft equal if is a hypersoft subset of and is a hypersoft subset of .
Definition 4
([]). Let be a set of parameters. The NOT set of denoted by is defined by where not for .
The following results are obvious.
Proposition 1.
For any subsets .
- .
- .
- .
Definition 5
([]). The relative complement of a hypersoft set is denoted by and is defined by where is a mapping given by for all .
Definition 6
([]). A hypersoft set over is said to be a relative null hypersoft set denoted by if for all , .
Definition 7
([]). A hypersoft set over is said to be a relative whole hypersoft set denoted by if for all , .
Definition 8
([]). A hypersoft set over is said to be an absolute whole hypersoft set denoted by if for all , .
Definition 9
([]). The union of two hypersoft sets and over a common universe is a hypersoft set , where and for all .
We write .
Definition 10
([]). The extended intersection of two hypersoft sets and over a common universe is a hypersoft set , where and for all .
We write .
Definition 11
([]). The restricted union of two hypersoft sets and over a common universe is a hypersoft set , where and for all , . We write .
Definition 12
([]). The intersection of two hypersoft sets and over a common universe is a hypersoft set , where and for all , . We write .
Definition 13
([]). The OR-operation of two hypersoft sets and over a common universe is a hypersoft set , where and for , , , . We write .
Definition 14
([]). The AND-operation of two hypersoft sets and over a common universe is a hypersoft set , where and for , , , . We write .
2.2. Bipolar Soft Sets
Let be an initial universe and E be a set of parameters. Let denote the power set of and are non-empty subsets of E.
Definition 15
([]). A triple is called a bipolar soft set over , where and are mappings given by and such that for all .
In other words, a bipolar soft set over gives two parameterized families of subsets of the universe and the conditions for all are imposed as a consistency constraint.
Definition 16
([]). For two bipolar soft sets and over a common universe , we say that is a bipolar soft subset of if the following is the case:
- and;
- and for all .
We write .
is said to be a bipolar soft superset of if is a bipolar soft subset of . We denote it by .
Definition 17
([]). Two bipolar soft sets and over a common universe are said to be bipolar soft equal if is a bipolar soft subset of and is a bipolar soft subset of .
Definition 18
([]). The complement of a bipolar soft set is denoted by and is defined by , where and are mappings given by and for all .
Definition 19
([]). A bipolar soft set over is said to be a relative null bipolar soft set denoted by , if for all , and .
The relative null bipolar soft set with respect to the universe set of parameters E is called the absolute null bipolar soft set over and is denoted by .
Definition 20
([]). A bipolar soft set over is said to be a relative whole bipolar soft set denoted by , if for all , and .
The relative whole bipolar soft set with respect to the universe set of parameters E is called the absolute whole bipolar soft set over and is denoted by .
Definition 21
([]). The union of two bipolar soft sets and over a common universe is a bipolar soft set , where and the following is the case for all :
We write .
Definition 22
([]). Extended intersection of two bipolar soft sets and over a common universe is a bipolar soft set , where and the following is the case for all :
We write .
Definition 23
([]). Restricted union of two bipolar soft sets and over a common universe is a bipolar soft set , where and for all , and .
We write .
Definition 24
([]). The intersection of two bipolar soft sets and over a common universe is a bipolar soft set , where and for all , and .
We write .
Definition 25
([]). The OR-operation of two bipolar soft sets and over a common universe is a bipolar soft set , where and for all , , , and .
We write .
Definition 26
([]). The AND-operation of two bipolar soft sets and over a common universe is a bipolar soft set , where and for all , , , and .
We write .
3. Bipolar Hypersoft Sets
In this section, we present the notion of bipolar hypersoft sets and some of its basic operations.
Definition 27.
A triple is called a bipolar hypersoft set over , where and are mappings given by and such that for all .
In other words, a bipolar hypersoft set provides two parameterized families of subsets of the universe and the consistency constraint condition for all is imposed.
Now, we can represent a bipolar hypersoft set as the following form.
Example 1.
Let be the set of TVs under consideration and let the parameters be the following.
and
Suppose that the following is the case:
Now suppose that Mr. X wants to buy a television according to the following: I . Define a bipolar hypersoft set as follows.
It is noted that, although Mr. X thinks that and are the televisions of I, , is not, whereas is not parameterized. Similarly, Mr. X thinks that and are the televisions of , , whereas and are not.
Definition 28.
For two bipolar hypersoft sets and over a common universe , we say that is a bipolar hypersoft subset of if the following is the case.
- and;
- and for all .
We write .
is said to be a bipolar hypersoft superset of if is a bipolar hypersoft subset of . We denote it by .
Definition 29.
Two bipolar hypersoft sets and over a common universe are said to be bipolar hypersoft equal if is a bipolar hypersoft subset of and is a bipolar hypersoft subset of .
Example 2.
Let as follows.
Let the bipolar hypersoft sets and be defined by the following.
Then, .
Definition 30.
The complement of a bipolar hypersoft set is denoted by and is defined by , where and are mappings given by and for all .
Definition 31.
A bipolar hypersoft set over is said to be a relative null bipolar hypersoft set denoted by , if, for all , and .
The relative null bipolar hypersoft set with respect to the universe set of parameters is called the absolute null bipolar hypersoft set over and is denoted by .
Definition 32.
A bipolar hypersoft set over is said to be a relative whole bipolar hypersoft set that is denoted by , if, for all , and .
The relative whole bipolar hypersoft set with respect to the universe set of parameters is called the absolute whole bipolar hypersoft set over and is denoted by .
Definition 33.
The union of two bipolar hypersoft sets and over a common universe is a bipolar hypersoft set , where and, for all , the following is the case:
We write .
Definition 34.
The extended intersection of two bipolar hypersoft sets and over a common universe is a bipolar hypersoft set , where and, for all , the following is the case.
We write .
Definition 35.
The restricted union of two bipolar hypersoft sets and over a common universe is a bipolar hypersoft set , where and the following is the case for all .
We write .
Definition 36.
The intersection of two bipolar hypersoft sets and over a common universe is a bipolar hypersoft set , where and the following is the case for all .
We write .
Example 3.
Let as follows.
Let the bipolar hypersoft sets and be defined by the following.
Suppose that . Then the following is the case.
Moreover, let . Then the following is the case.
Definition 37.
The difference of two bipolar hypersoft sets and over a common universe is a bipolar hypersoft set , where and the following is the case for all .
We write =
.
Definition 38.
The OR-operation of two bipolar hypersoft sets and over a common universe is a bipolar hypersoft set , where and the following is the case for all , , .
We write .
Definition 39.
The AND-operation of two bipolar hypersoft sets and over a common universe is a bipolar hypersoft set , where and the following is the case for all . , ,
We write .
Example 4.
Consider Example 3, let , then the following is the case.
Now, if we let , then the following is the case.
Proposition 2.
If , and are any bipolar hypersoft sets over a universe . Then the following is the case:
- ;
- ;
- ;
- If and then ;
- If = and = then = .
Proof.
This is straightforward. □
Proposition 3.
Let and be two bipolar hypersoft sets over a universe . Then the following is the case:
- = and = ;
- = ;
- then ;
- ;
- If then = ;
- If then = .
Proof.
This is straightforward. □
Proposition 4.
If and are two bipolar hypersoft sets over a universe . Then the following is the case:
- = ;
- = ;
- = ;
- = ;
- = ;
- = .
Proof.
(1) Let = where . Then = = . By definition, the following is the case.
Thus, we have the following.
Moreover, let = = where , then the following is obtained.
Since and are the same set-valued mapping for all , the proof is completed.
The remaining parts can be proved with the same method. □
Proposition 5.
If and are two bipolar hypersoft sets over a universe , then we have the following:
- = ;
- = ;
- = and = ;
- = and = ;
- = and = .
Proof.
This is straightforward. □
Proposition 6.
Let , and be any bipolar hypersoft sets over a universe , then we have the following:
- = ;
- = ;
- = ;
- = ;
- = ;
- = ;
- = ;
- = .
Proof.
(7) Suppose that = . Then, for all , we have the following.
Assume = . Then, for all , we have the following:
On the other hand, let = . Then, for all , we have the following.
Assume = . Then, for all , we have the following.
Since and are the same set-valued mapping for all , the proof is completed.
The remaining parts can be proved with the same method. □
Proposition 7.
Let , and be any bipolar hypersoft sets over a universe . Then, the following is the case:
- = ;
- = ;
- = ;
- = ;
- = ;
- =.
Proof.
Now, suppose that = where and . For all , we have the following.
(4) Suppose that = . For all , we have the following.
Assume that = = where and . For all , we have the following.
In this case, we obtain the following.
On the other hand, let = . For all , we have the following.
Let = . For all , we obtain the following.
Since and are the same set-valued mapping for all , the proof is completed.
The remaining parts can be proved with the same method. □
A representation of a bipolar hypersoft set in the form of a matrix or a table may be desired for storing it in a computer. The entry in table is as follows.
With reference to Example 1, the tabular representation of the bipolar hypersoft set for each of the functions and is given in Table 1.
Table 1.
A pair of tables are used to represent in a tabular format.
Moreover, we can represent a bipolar hypersoft with the help of a single table by inputting the following.
With reference to Example 1, the tabular representation of the bipolar hypersoft set is given in Table 2.
Table 2.
A single table is used to represent in a tabular format.
4. Conclusions
Hypersoft sets are derived by transforming the approximate function in the structure of a soft set into a multi-attribute approximate function. Meanwhile, bipolarity refers to an explicit handling of positive and negative sides of information. In this paper, we have introduced the concept of bipolar hypersoft sets with some basic definitions. After that, we proposed some operations on the bipolar hypersoft sets such as subset, complement, difference, extended intersection, restricted union, intersection, union, AND and OR. Finally, the necessity and possibility operations on bipolar hypersoft sets with suitable examples and properties have been presented. For future trends, we can construct the bipolar hypersoft points and operations on bipolar hypersoft functions. Furthermore, we can define the bipolar hypersoft topological spaces with their properties by using the proposed operations. Finally, we provided an application of the bipolar hypersoft set in a decision-making problem.
Author Contributions
Funding acquisition, S.Y.M. and B.A.A.; investigation, B.A.A.; methodology, B.A.A.; resources, S.Y.M.; writing–original draft, S.Y.M. and B.A.A.; writing—review and editing, S.Y.M. Both authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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