Abstract
The aim of this paper is to give an interesting connection between two mathematical approaches to vagueness: binary bipolar soft sets and binary bipolar soft topology. The binary bipolar soft points are defined using binary bipolar soft sets. The binary bipolar soft set will be the binary bipolar soft union of its binary bipolar soft points. Moreover, the notion of binary bipolar soft topological spaces over two universal sets and a parameter set is proposed. Some topological properties of binary bipolar soft sets, such as binary bipolar soft open, binary bipolar soft closed, binary bipolar soft closure, binary bipolar soft interior, and binary bipolar soft boundary, are introduced. Some important properties of these classes of binary bipolar soft sets are investigated. Furthermore, the symmetry relation is compared between binary bipolar soft topology and binary soft topology on a common universe set. Finally, some results and counterexamples are demonstrated to explain this work.
1. Introduction
For formal modeling, reasoning, and computing, most traditional tools are characterized by being crisp, deterministic, and precise. However, many complex problems exist in the domains of economics, engineering, the environment, social science, medical science, and so on. Therefore, traditional methods based on cases may not be suitable for solving or modeling these issues. Based on this, a set of theories has been proposed to tackle these problems. Molodtsov [1] introduced a new concept, namely soft set. In [1,2], Molodtsov successfully applied the soft theory in several directions, such as smoothness of functions, game theory, operations research, Riemann integration, Perron integration, probability, theory of measurement, etc.
In 2013, Shabir and Naz [3] explained that the bipolar soft set structure has clearer and more general results than the soft set structure. They came up with the fuzzy bipolar soft sets and bipolar fuzzy soft sets and established some algebraic structures of these two classes of bipolar soft sets. They presented an application of fuzzy bipolar soft sets in decision-making problems [4]. Based on Dubois and Prada [5], decision making is constructed on two sides, namely positive and negative. Bipolarity is significant for characterization between positive and negative information to differentiate between reasonable and unreasonable events. Shabir and Bakhtawar [6] introduced the concept of bipolar soft topological spaces and studied some of their properties. Fadel and Dzul-Kifli [7] defined the concept of bipolar soft topological spaces via bipolar soft sets and some properties. Öztürk [8,9] presented more properties and operations on bipolar soft sets, and the bipolar soft points are introduced.
Subsequently, a number of definitions, operations, and applications on bipolar soft sets and bipolar soft structures have been investigated. For instance, Dizman and Öztürk [10] introduced fuzzy bipolar soft topological spaces via fuzzy bipolar soft sets. Abdullah et al. [11] proposed a bipolar fuzzy soft set, which is a new idea of bipolar soft set and they introduced some basic operations and an application of bipolar fuzzy soft set into decision-making problems. They gave an algorithm to solve decision-making problems by using a bipolar fuzzy soft set. Al-Shami [12] defined some ordinary points on bipolar soft sets and presented an application of optimal choices by applying the idea of bipolar soft sets. Karaaslan and Karataş [13] redefined the concept of the bipolar soft set and bipolar soft set operations and presented a decision-making method with application. Karaaslana et al. [14] defined normal bipolar soft subgroups. Mahmood [15] defined a novel approach towards bipolar soft sets and discussed an application on decision-making problems. Wang et al. [16], and Rehman and Mahmood [17] combined some generalizations of fuzzy sets and bipolar soft sets. They investigated applications in decision-making problems. Hussain [18] defined and discussed binary soft connected spaces in binary soft topological spaces. He proposed an application of a decision-making problem by using the approach of rough sets. Sathiyaseelan et al. [19] presented symmetric matrices on inverse soft expert sets and investigated their applications.
Yet, studies conducted on the limit point concept were required by mathematicians to bring about more developments in mathematics. Musa and Asaad [20] introduced the concept of the bipolar hypersoft set as a combination of a hypersoft set with a bipolarity setting and investigated some of its basic operations. They also discussed some topological notions in the frame of bipolar hypersoft setting [21]. In 2022, Saleh et al. [22,23] studied bipolar soft generalized topological spaces and defined the basic notions of bipolar soft topological properties with the investigation of a number of their symmetric properties.
Correspondingly, in 2016, the concept of the binary soft set was first defined by Açıkgöz and Taş in [24], where they introduced the binary soft set on two initial universal sets and proposed some of their properties. After that, Benchalli et al. [25] presented some related basic properties, which are defined over two initial universal sets with suitable parameters, and they defined the binary soft topological spaces with some of their properties. Recently, in 2023, Naime and Orhan [26] defined a new concept of bipolar soft sets over two universal sets and a parameter set, namely binary bipolar soft sets, which is an extension of bipolar soft sets and binary soft sets. They presented some operations on binary bipolar soft sets, such as complement, union, intersection, AND, and OR, and they investigated their basic properties.
The following sections in our work are organized in the following manner: Section 2 provides the essential conceptual framework concerning symmetry categories of sets, including soft sets, bipolar soft sets, and binary soft sets, to familiarize the reader with the underlying principles. After that, in Section 3, our main idea is to define the binary bipolar soft points using binary bipolar soft sets and some of their properties. Section 4 introduces the binary bipolar soft topological spaces using binary bipolar soft sets, which is an extension of the bipolar soft topological spaces and binary soft topological spaces, accompanied by an exploration of the associated topological operators binary bipolar soft closure, binary bipolar soft interior and binary bipolar soft boundary. Some results and counterexamples are given to explain this work. Section 5 serves as the concluding section of our presentation.
2. Preliminaries
Throughout this paper, we recall some notions in bipolar soft sets via binary bipolar soft sets, and we will give some preliminary information about binary bipolar soft sets, binary soft topological spaces, and bipolar soft topological spaces.
2.1. Bipolar Soft Sets
This subsection investigates the concepts of bipolar soft set operations. Let ℧ be an initial universe set and ℜ be a set of parameters. Let ⊆ ℜ and denote the collection of all subsets of ℧. The set of all bipolar soft sets over ℧ with will be denoted by . Let be a subset of ℜ.
Definition 1.
The Not set of Ψ is denoted by , where Not for each i.
The following definitions can be found in [3].
Definition 2.
A triple is called a bipolar soft set on ℧, denoted by , where and are mappings defined by and such that ∩ for each and .
Definition 3.
The is said to be a subset of if and for each .
Definition 4.
The complement of a is denoted by where and for each .
Definition 5.
A is said to be an absolute denoted by with the property that for each , and .
Definition 6.
A is said to be a null is denoted by with the property that for each , and .
Definition 7.
The restricted intersection of two and on the common universe ℧ is the , denoted by , where for each , and .
Definition 8.
The extended intersection of two and on the common universe ℧ is the , denoted by , where
and
Definition 9.
The restricted union of two and on the common universe ℧ is the , denoted by , where for each , and .
Definition 10.
The extended union of two and on the common universe ℧ is the , denoted by , where [3]
and
Shabir and Naz [3] introduced bipolar soft topological spaces () , where is the collection of over ℧ containing and , the union of any member of in belongs to , and the intersection of any two in belongs to . The members of are said to be open sets in ℧. The is said to be closed in ℧ if its relative complement belongs to .
2.2. Binary Bipolar Soft Sets
This subsection investigates the concepts of binary soft set operations and binary bipolar soft set operations. In 2016, Açıkgöz and Taş [24] introduced the binary soft set theory and investigated some of its related operations. After that, in 2017, Benchalli et al. [25] defined a topology on , namely binary soft topological space (), where is the collection of over and , which contains a null and an absolute , and it is closed under finite intersections and closed under unions.
The following definitions can be found in [26]. Let and be two initial universe sets and ℜ be a set of parameters. Let and denote the power set of and , respectively. Let .
Definition 11.
A triple is called a binary bipolar soft set over and , where and are mappings defined by and such that , and ∩ for each .
Definition 12.
The is said to be a subset of if , and such that and for each . This relationship is denoted by . Similarly, is said to be a superset of if is a subset of and denoted by .
Definition 13.
Two s and over universes and are said to be equal s if is a subset of and is a subset of .
Definition 14.
The complement of a is denoted by where and are mapping given by and for each .
Definition 15.
A is said to be absolute is denoted by with the property that for each , and .
Definition 16.
A is said to be a null is denoted by with the property that for each , and .
Definition 17.
The difference of two s and over universes and is the , where and for each .
Definition 18.
The restricted intersection of two s and over universes and is the with , denoted by , where for each , and such that , for each and , for each .
Definition 19.
The extended intersection of two s and over universes and is the , denoted by , where for each
and
Definition 20.
The restricted union of two s and over universes and is the with , denoted by , where for each , and such that , for each and , for each .
Definition 21.
The extended union of two s and on the universes and is the , denoted by , where for each ,
and
3. Binary Bipolar Soft Points
In this section, we present the binary bipolar soft points and some related properties.
Definition 22.
A is said to be binary bipolar soft point if there exists , ∈ (it possible to = ) and such that
and
We denote by the , where and in . The collection of is denoted by .
Definition 23.
Let , . Then, and are called different , denoted by ≠ if or .
Definition 24.
Let and . Then, is said to be contained in if and . It is denoted by .
Proposition 1.
Let . Then, is the union of its . That is,
Proof.
It is sufficient to show the following:
For each ,
and for each ,
Since the proof of these two equalities are easy and hence it is completed. □
Example 1.
Let and be two universe sets and be a set of parameters. The
We can write this as a union of its ,
where , , , , , , , and defines as
Proposition 2.
Let be a family of . Then,
- 1.
- if and only if for each . That is, and for every , .
- 2.
- if and only if there exists , such that . That is, there exists , such that and for every and .
Proof.
Straightforward. □
Proposition 3.
Let , . Then, , if and only if for each implies that .
Proof.
Suppose is an arbitrary and . Then, ∈⊆ and ∈⊆ for all and . Therefore, . □
Proposition 4.
Let and . If the belongs to , then the also belongs to the .
Proof.
Obvious. □
4. Topology on Binary Bipolar Soft Sets
In this section, we introduce the concept of binary bipolar soft topological spaces over two universal sets and a set of parameters. We investigate some topological structures of binary bipolar soft sets such as binary bipolar soft open, binary bipolar soft closed, binary bipolar soft closure, binary bipolar soft interior, and binary bipolar soft boundary.
Definition 25.
Let be a collection of s over and , then is said to be a binary bipolar soft topology, denoted by , on and if
- Ax.1.
- , .
- Ax.2.
- The union of any member of s in belongs to .
- Ax.3.
- The intersection of any two s in belongs to .
The quintuple is called a binary bipolar soft topological space () over and .
Example 2.
Let and be two universe sets and be a set of parameters. Let , where
Therefore, is a over and .
Example 3.
Let and be two universe sets and be a set of parameters. Let , where
Hence, is not a over and because .
Definition 26.
Let be a over and , then the members of are said to be open sets in and . The is said to be closed in and if its relative complement is belong to .
Definition 27.
Let and be the two initial universe sets and Ψ be a set of parameters. If , , then is called the indiscrete space over and . If is the collection of all s, which can be defined over and , then is called the discrete space over and .
Proposition 5.
Let be a over and . Then, the following properties hold:
- 1.
- and are closed sets.
- 2.
- The union of any two closed sets is closed.
- 3.
- The arbitrary intersection of closed sets is closed.
Proof.
It is obvious. □
Theorem 1.
Let and be over and , then is a over and .
Proof.
Let and be a over and
- Ax.1.
- Clearly , .
- Ax.2.
- Let be a family of s in , Then, and for each , so and . Thus, .
- Ax.3.
- Let and be two s in . Then, , and , . Since and . Therefore, . Hence, is a over and .
□
Remark 1.
Let and be over and , then may not be over and in general.
Example 4.
Let and be two universe sets and be a set of parameters. Let and , , where and , , are s over and defined as
Then, and are over and . Now, , , , is not because , , , , .
Theorem 2.
Let be a over and , then is over and .
Proof.
Suppose that is a over and . Then,
- Ax.1.
- implies that , also implies that .
- Ax.2.
- Let belong to . Since for all , then . Thus, .
- Ax.3.
- Let , . Since , , . Therefore,
Hence, defines a over and . □
Theorem 3.
Let be a over and , then is over and .
Proof.
Similar to Theorem 2. □
Remark 2.
The following example shows that the converse of Theorems 2 and 3 are not true.
Example 5.
Let , and . Suppose that
are two defined on and , where
and
Thus, , , , , , where , and defined as follows:
Hence, ,, . Therefore, is not over and .
However, the following theorem shows that the converse of Theorem 2 is true under some conditions.
Theorem 4.
Let be a over and . Then, the collection consisting of s such that and for all defines a topology on and .
Proof.
- Ax.1.
- Since , , then and hence . Also, , thus .
- Ax.2.
- Let . Then, and . Now, since is a topology, then . Let , thus . Therefore, .
- Ax.3.
- Let , . Then, , and so . Now, since is a topology, . Let , hence . Therefore, . The proof is completed.
□
Theorem 5.
If is a over and , then and are over and , respectively.
Proof.
Let be a over and and by Definition 4, we have , in with , then , in and , in . So, let and , where , then , from is a over and , thus . Therefore, and . Now, let , . Then, , thus and . Hence, and are over and , respectively. □
Definition 28.
Let be a over and be a over universe sets and . Then, the closure of , denoted by -, is the intersection of all closed sets, which contains . Thus, - is the smallest closed set over and , which contains .
Theorem 6.
Let be a over and , and be over universe sets and . Then
- 1.
- - and - .
- 2.
- - implies - is a closed set and contains .
- 3.
- is a closed set if and only if =-.
- 4.
- --=-.
- 5.
- implies - -.
- 6.
- - =- -.
- 7.
- - - -.
Proof.
- Follows directly from Definitions 26 and 28.
- Let be a family of closed sets containing -. Then, from Definition 28,Since is a closed set for each , it implies that is also closed set by Proposition 5. Thus, - is a closed set by (i). Now, to prove that -, we have for each , , implying , using (i) to obtain - . Therefore, - contains . Hence, - is a closed set and contains .
- Let be a closed set, to prove =-. Since a closed and from Part (2), -. Thus, is a closed set containing , and is the smallest closed set containing . Therefore, - is smaller than that is - . Conversely, if is closed, then -=.
- Since - is a closed set, by Part (3), it implies that --=-.
- Suppose , then - and -, Since - is a closed set containing . But, - is the smallest closed containing . Therefore, - is smaller than -. Hence, - -.
- Since and .Then, - - and- - , by Part (5). Therefore,- - - .Also, from the closure property, we obtain - -. But, - is the smallest closed set containing . Thus, - is the smallest than - -. Hence, - =- -.
- Since and , by Part (5) - - and - -. Hence, - - -.
□
Remark 3.
The next example shows that the equality of Part (7) in Theorem 6 does not hold in general.
Example 6.
Let be defined as in Example 2. If and are two defined as
Thus, -=-=, while - , . Hence, - -
-
- Definition 29.
Let be a of a , , over and . Then, we associate pointwise the closure of over and is denoted by - and defined as - -, where - is the closure of in , , for each .
Theorem 7.
Let be a over and , be a over universe sets on and , then - -
Proof.
For any parameter , - is the smallest closed set in , which contains . Then, if - , is a closed set in containing . This implies that - - . Therefore, -, -. □
Theorem 8.
Let be a and be a over and , then - -.
Proof.
Let be a over and , if - -, then - is a closed set, thus - . Conversely, if - , then - is a closed set containing . By Theorem 7, and from the definition of closure of , any closed set, which contains will contains -. Thus, - -. Hence, -=-. □
Definition 30.
Let be a over and . Let , , the is said to be binary bipolar soft neighborhood set of , denoted by the neighborhood of if there exists a open set such that
That is ∈ ⊆ and ∈ ⊆ , for each and .
Theorem 9.
Let be a over and . Then
- 1.
- Each has a neighborhood set.
- 2.
- The intersection of two neighborhood sets of is a neighborhood.
- 3.
- Every superset of a neighborhood set of is a neighborhood of .
Proof.
- Clearly from .
- Let , if and are neighborhood sets of . Then, there exist two open sets , such that and . Since . Therefore, . Hence, is a neighborhood of .
- Let and . Since is neighborhood of , then with . Therefore, . Hence, is a neighborhood of .
□
Definition 31.
Let be a over and . A is said to be binary bipolar soft interior point of , denoted by the interior point if there exists a open set such that for each and .
The interior set of , denoted by -, is the union of all open sets contained in .
Theorem 10.
Let be a over and , and be . Then, the following properties hold:
- 1.
- - is a open set contained in .
- 2.
- - is the largest open set over and , which contained in .
- 3.
- is open if and only if -.
Proof.
- Obvious from Definition 31.
- From Part (1) and Definition 31.
- Suppose that is open. Then, is a open set contained in but from Part (2), - is the largest open set contained in . Therefore, -. Conversely, if -, then is a open set from Part (1).
□
Theorem 11.
Let be a over and , and and be . Then, the following properties hold:
- 1.
- - and -
- 2.
- --=-.
- 3.
- implies - -.
- 4.
- - =- -.
- 5.
- - - - .
Proof.
- Obvious.
- Since - is a open set. Then, by Theorem 10 (3), --=-.
- Suppose , then - , thus - and from - is the largest open contained in . Hence, - -.
- Since and . From (3), - - and - -. Implies - - -. Also, from - and - , then - - , which implies- - is a open set contained in . But - - is the largest open set contained in . Therefore, - - - . Hence, - =- -.
- Since and . Then, by (3), - - and - - . Hence, - - - .
□
Remark 4.
The following example shows the equality of Theorem 11 (5) does not hold in general.
Example 7.
Let be defined as in Example 2. If and are defined as
Therefore, -=-=, while - , . Hence, - -,
- .
- .Theorem 12.
Let be a and be a over and . Then
- 1.
- -=-.
- 2.
- -=-.
Proof.
- Let , , , then
- Similar to Part (1).
□
Definition 32.
Let be a over and . A binary bipolar soft boundary set of , denoted by boundary and defined as -=- -.
Remark 5.
Let be a over and . For any , we have
- 1.
- -=-.
- 2.
- - -.
Theorem 13.
Let be a over and . For any , we have
- 1.
- -=- -.
- 2.
- - -.
Proof.
- We start the proof by using Definition 32
- By using Remark 5, we have
□
Remark 6.
Let be a over and . Then, in general
- 1.
- -≠ -.
- 2.
- ≠- - -.
Example 8.
Let and be two universe sets and be a set of parameters. Let , where
If we take a as
Then, - and - , so - and - , then - . Therefore, - = -. Also, ≠ , , , - - -.
Theorem 14.
Let be a over and . If is a open set, then and - are disjoint .
Proof.
Suppose that is a open set. By Theorem 13 (2), - - and - . Therefore, -. Hence, and - are disjoint . □
Theorem 15.
Let be a over and . If is a closed set, then - .
Proof.
Suppose that is a closed set. Then, - - and - . Therefore, - . □
Theorem 16.
Let be a over and , and is a . If is both a open and closed set, then - .
Proof.
Suppose that is a open set. By Theorem 14, and - are disjoint . So, - . Now, is a closed set. By Theorem 14, - . Therefore, -=-. Hence, - . □
Remark 7.
The converse of Theorems 14–16 in general is not hold.
Example 9.
Consider and in Example 8. Clearly, - and are disjoint , but is not open.
If we take , then - but is not closed.
Again, if we take , then - , but is neither closed nor open.
Theorem 17.
Let be a over and , and be a . Then,
- 1.
- - - .
- 2.
- - - .
Proof.
- We start the proof by using Definition 32
- Similar to Part (1).
□
5. Conclusions and Future Research
In this work, binary bipolar soft points using binary bipolar soft sets have been defined, and their properties have been given. The binary bipolar soft set is the binary bipolar soft union of its binary bipolar soft points. In addition, we continue studying the essential ideas in the frame bipolar soft topological spaces over two initial universal sets and with a set of parameters , namely binary bipolar soft topological spaces, which are related to binary bipolar soft sets. Some topological concepts such as open (closed), closure, interior and boundary have been discussed. The comparison among binary bipolar soft topological space, binary soft topological space, and bipolar soft topological space have been presented, and the converse holds under special conditions. According to the obtained results, many classical properties of these concepts are still valid for structures. In forthcoming works, we will add some other concepts of binary bipolar soft topological properties such as binary bipolar soft continuous mappings, binary bipolar soft compactness, binary bipolar soft connectedness, and binary bipolar soft separation axioms in terms of binary bipolar soft open sets and binary bipolar soft points. We extend this work to define some types of symmetrical soft sets such as N-soft sets [27,28,29], N-hypersoft sets [30], N-bipolar soft sets [31], and fuzzy N-soft sets [17,32,33,34]. Furthermore, we can introduce the concepts as defined in [35,36,37,38] by using the binary bipolar soft sets.
Author Contributions
Conceptualization, H.Y.S., A.A.S., and B.A.A.; methodology, H.Y.S., A.A.S., B.A.A., and R.A.M.; formal analysis, H.Y.S., A.A.S., B.A.A., and R.A.M.; investigation, H.Y.S., A.A.S., B.A.A., and R.A.M.; writing—original draft preparation, H.Y.S., A.A.S. and B.A.A.; writing—review and editing, B.A.A. and R.A.M.; funding acquisition, A.A.S., B.A.A., and R.A.M. All 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
Data are contained within the article.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| binary bipolar soft | |
| binary bipolar soft set | |
| binary bipolar soft point | |
| binary soft topological space | |
| bipolar soft topological space | |
| binary bipolar soft topological space |
References
- Molodtsov, D. Soft set theory-first results. Comput. Math. Appl. 1999, 37, 19–31. [Google Scholar] [CrossRef] [Scilit]
- Molodtsov, D.; Leonov, V.; Kovkov, D.V. Soft sets technique and its application. Nechetkie Sist. Myagkie Vychisleniya 2006, 1, 8–39. [Google Scholar]
- Shabir, M.; Naz, M. On bipolar soft sets. arXiv 2013, arXiv:1303.1344. [Google Scholar]
- Naz, M.; Shabir, M. On bipolar fuzzy soft sets, their algebraic structures and applications. J. Intell. Fuzzy Syst. 2014, 26, 1645–1656. [Google Scholar] [CrossRef] [Scilit]
- Dubois, D.; Prade, H. An introduction to bipolar representations of information and preference. Int. J. Intell. Sys. 2008, 23, 866–877. [Google Scholar] [CrossRef] [Scilit]
- Shabir, M.; Bakhtawar, A. Bipolar soft connected, bipolar soft disconnected and bipolar soft compact spaces. Songklanakari J. Sci. Technol. 2017, 39, 359–371. [Google Scholar]
- Fadel, A.; Dzul-Kifli, S.C. Bipolar soft topological spaces. Eur. J. Pure Appl. Math. 2020, 13, 227–245. [Google Scholar] [CrossRef] [Scilit]
- Öztürk, T.Y. On bipolar soft topological spaces. J. New Theory 2018, 20, 64–75. [Google Scholar]
- Öztürk, T.Y. On bipolar soft points. TWMS J. App. Eng. Math. 2020, 10, 877–885. [Google Scholar]
- Dizman, T.S.; Öztürk, T.Y. Fuzzy bipolar soft topological spaces. TWMS J. App. Eng. Math. 2021, 11, 151–159. [Google Scholar]
- Abdullah, S.; Aslam, M.; Ullah, K. Bipolar fuzzy soft sets and its applications in decision making problem. J. Intell. Fuzzy Syst. 2014, 27, 729–742. [Google Scholar] [CrossRef] [Scilit]
- Al-Shami, T.M. Bipolar soft sets: Relations between them and ordinary points and their applications. Complexity 2021, 2021, 6621854. [Google Scholar] [CrossRef] [Scilit]
- Karaaslan, F.; Karataş, S. A new approach to bipolar soft sets and its applications. Discret. Math. Algorithms Appl. 2015, 7, 1550054. [Google Scholar] [CrossRef] [Scilit]
- Karaaslan, F.; Ullah, A.; Ahmad, I. Normal bipolar soft subgroups. Fuzzy Inf. Eng. 2021, 13, 79–98. [Google Scholar] [CrossRef] [Scilit]
- Mahmood, T. A novel approach towards bipolar soft sets and their applications. J. Math. 2020, 2020, 4690808. [Google Scholar] [CrossRef] [Scilit]
- Wang, J.Y.; Wang, Y.P.; Liu, L. Hesitant bipolar-valued fuzzy soft sets and their application in decision making. Complexity 2020, 2020, 6496030. [Google Scholar] [CrossRef] [Scilit]
- Rehman, U.U.; Mahmood, T. Picture fuzzy N-soft sets and their applications in decision-making problems. Fuzzy Inf. Eng. 2021, 13, 335–367. [Google Scholar] [CrossRef] [Scilit]
- Hussain, S. Binary soft connected spaces and an application of binary soft sets in decision making problem. Fuzzy Inf. Eng. 2019, 11, 506–521. [Google Scholar] [CrossRef] [Scilit]
- Sathiyaseelan, N.; Vijayabalaji, S.; Alcantud, J.C.R. Symmetric matrices on inverse soft expert sets and their applications. Symmetry 2023, 15, 313. [Google Scholar] [CrossRef] [Scilit]
- Musa, S.Y.; Asaad, B.A. Bipolar hypersoft sets. Mathematics 2021, 9, 1826. [Google Scholar] [CrossRef] [Scilit]
- Musa, S.Y.; Asaad, B.A. Topological structures via bipolar hypersoft sets. J. Math. 2022, 2022, 2896053. [Google Scholar] [CrossRef] [Scilit]
- Saleh, H.Y.; Asaad, B.A.; Mohammed, R.A. Bipolar soft generalized topological structures and their application in decision making. Eur. J. Pure Appl. Math. 2022, 15, 646–671. [Google Scholar] [CrossRef] [Scilit]
- Saleh, H.Y.; Asaad, B.A.; Mohammed, R. A Bipolar soft limit points in bipolar soft generalized topological spaces. Math. Stat. 2022, 10, 1264–1274. [Google Scholar] [CrossRef] [Scilit]
- Açıkgöz, A.; Taş, N. Binary soft set theory. Eur. J. Pure Appl. Math. 2016, 9, 452–463. [Google Scholar]
- Benchalli, S.S.; Patil, P.G.; Dodamani, A.S.; Pradeepkumar, J. On binary soft topological spaces. In. J. Appl. Math. 2017, 30, 437–453. [Google Scholar]
- Orhan, D.; Naime, D. Binary bipolar soft sets. Bol. Soc. Parana. MatemáTica 2023, 41, 1–12. [Google Scholar]
- Fatimah, F.; Rosadi, D.; Hakim, R.; Alcantud, J.C.R. N-soft sets and their decision making algorithms. Soft Comput. 2018, 22, 3829–3842. [Google Scholar] [CrossRef] [Scilit]
- Alcantud, J.C.R. The semantics of N-soft sets, their applications, and a coda about three-way decision. Inf. Sci. 2022, 606, 837–852. [Google Scholar] [CrossRef] [Scilit]
- Kamacı, H. Introduction to N-soft algebraic structures. Turk. J. Math. 2020, 44, 2356–2379. [Google Scholar] [CrossRef] [Scilit]
- Musa, S.Y.; Mohammed, R.A.; Asaad, B.A. N-hypersoft sets: An innovative extension of hypersoft sets and their applications. Symmetry 2023, 15, 1795. [Google Scholar] [CrossRef] [Scilit]
- Shabir, M.; Fatima, J. N-bipolar soft sets and their application in decision making. Res. Sq. 2021, 1–24. [Google Scholar] [CrossRef] [Scilit]
- Akram, M.; Adeel, A.; Alcantud, J.C.R. Fuzzy N-soft sets: A novel model with applications. J. Intell. Fuzzy Syst. 2018, 35, 4757–4771. [Google Scholar] [CrossRef] [Scilit]
- Akram, M.; Adeel, A.; Alcantud, J.C.R. Group decision-making methods based on hesitant N-soft sets. Expert Syst. Appl. 2019, 115, 95–105. [Google Scholar] [CrossRef] [Scilit]
- Mahmood, T.; Rehman, U.U.; Ahmmad, J. Complex picture fuzzy N-soft sets and their decision-making algorithm. Soft Comput. 2021, 25, 13657–13678. [Google Scholar] [CrossRef] [Scilit]
- Mahmood, T.; Rehman, U.U.; Jaleel, A.; Ahmmad, J.; Chinram, R. Bipolar Complex Fuzzy Soft Sets and Their Applications in Decision-Making. Mathematics 2022, 10, 1048. [Google Scholar] [CrossRef] [Scilit]
- Ahmmad, J.; Mahmood, T. Picture fuzzy soft prioritized aggregation operators and their applications in medical diagnosis. Symmetry 2023, 15, 861. [Google Scholar] [CrossRef] [Scilit]
- Yolcu, A. Bipolar Spherical Fuzzy Soft Topology with Applications to Multi-Criteria Group Decision-Making in Buildings Risk Assessment. Symmetry 2022, 14, 2362. [Google Scholar] [CrossRef] [Scilit]
- Ali, G.; Alolaiyan, H.; Pamučar, D.; Asif, M.; Lateef, N. A Novel MADM Framework under q-Rung Orthopair Fuzzy Bipolar Soft Sets. Symmetry 2021, 9, 2163. [Google Scholar] [CrossRef] [Scilit]
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