# Generalization of Fuzzy SoftBCK/BCI-Algebras

Authors: N. Alam; G. Muhiuddin; S. Obeidat; H. N. Zaidi; A. Altaleb; J. M. Aqib
Journal: Journal of Mathematics
 (2021)

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## Abstract

In this paper, the notions of∈,∈∨q-fuzzy soft BCK/BCI-algebras and ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebras are introduced, and related properties are investigated. Furthermore, relations between fuzzy soft BCK/BCI-algebras and ∈,∈∨q-fuzzy soft BCK/BCI-algebras are displayed. Moreover, conditions for an ∈,∈∨q-fuzzy soft BCK/BCI-algebra to be a fuzzy soft BCK/BCI-algebra are provided. Also, the union, the extended intersection, and the “AND”-operation of two ∈,∈∨q-fuzzy soft (sub-)BCK/BCI-algebras are discussed, and a characterization of an ∈,∈∨q-fuzzy soft BCK/BCI-algebra is established.


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## Body

## 1. Introduction

The uncertainty which appeared in economics, engineering, environmental science, medical science, social science, and so on is too complicated to be captured within a traditional mathematical framework. In order to overcome this situation, a number of approaches including fuzzy set theory [1, 2], probability theory, rough set theory [3, 4], vague set theory [5], and the interval mathematics [6] have been developed. The concept of soft set was introduced by Molodtsov [7] as a new mathematical method to deal with uncertainties free from the errors being occurred in the existing theories. Later, Maji et al. [8, 9] defined fuzzy soft sets and also described how soft set theory is applied to the problem of decision making. Study on the soft set theory is currently moving forward quickly. In [10], Jun et al. discussed the intersection-soft filters in R0-algberas. Roh and Jun [11] studied positive implicative ideals of BCK-algebras based on intersectional soft sets. Roy and Mayi [12] gave results on applying fuzzy soft sets to the problem of decision making. Aygünoǧlu and Aygün [13] proposed and investigated the notion of a fuzzy soft group. Furthermore, Jun et al. [14] applied the theory of fuzzy soft sets to BCK/BCI-algebras and introduced the notion of fuzzy soft BCK/BCI-algebras (briefly, FSB-algebras) and related notions. Moreover, Muhiuddin et al. studied and applied the soft set theory to the different algebraic structures on various aspects (see, e.g., [15–23]). Also, some related concepts based on the present work are studied in [24–33].In this paper, we define the notions of∈,∈∨q-FSB-algebras and ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebras. Further, we investigate related properties and consider relations between fuzzy soft BCK/BCI-algebras and ∈,∈∨q-fuzzy soft BCK/BCI-algebras. Moreover, we prove that every FSB-algebra over X is an ∈,∈∨q-FSB-algebra over X and also show by an example that the converse of the aforesaid statement is not true in general. In fact, we provide a condition for an ∈,∈∨q-FSB-algebra to be a FSB-algebra. In addition, we discuss the union, the extended intersection, and the “AND”-operation of two ∈,∈∨q-FSB-algebras. Finally, we establish a characterization of an ∈,∈∨q-fuzzy soft BCK/BCI-algebra. The paper is organized as follows. Section 2 summarizes some definitions and properties related to BCK/BCI-algebras, fuzzy sets, soft sets, and fuzzy soft sets which are needed to develop our main results. In Section 3, the notions of FSB-algebras are studied and the concepts of θ-identity and θ-absolute FSB-algebras are introduced. Section 4 is devoted to the study of ∈,∈∨q-FSB-algebra. The paper ends with a conclusion and a list of references.


## 2. Preliminaries

ABCK/BCI-algebra is the most important class of logical algebras which was introduced by K. Iséki.By aBCI-algebra, we mean a system X˜;∗,0, where X˜ be a nonempty set with a constant 0 and a binary operation ∗ if(i)
∀ϖ,ϱ,ϑ∈X˜ϖ∗ϱ∗ϖ∗ϑ∗ϑ∗ϱ=0(ii)
∀ϖ,ϱ∈X˜ϖ∗ϖ∗ϱ∗ϱ=0(iii)
∀ϖ∈X˜ϖ∗ϖ=0(iv)
∀ϖ,ϱ∈X˜ϖ∗ϱ=0,ϱ∗ϖ=0⇒ϖ=ϱIf aBCI-algebra X˜ satisfies(v)
∀ϖ∈X˜0∗ϖ=0,thenX˜ is called a BCK-algebra. Any BCK-algebra X˜ satisfies(a1)∀ϖ∈X˜ϖ∗0=ϖ,(a2)∀ϖ,ϱ,ϑ∈X˜ϖ≤ϱ⟹ϖ∗ϑ≤ϱ∗ϑ,ϑ∗ϱ≤ϑ∗ϖ,(a3)∀ϖ,ϱ,ϑ∈X˜ϖ∗ϱ∗ϑ=ϖ∗ϑ∗ϱ,(a4)∀ϖ,ϱ,ϑ∈X˜ϖ∗ϑ∗ϱ∗ϑ≤ϖ∗ϱwhereϖ≤ϱ if and only if ϖ∗ϱ=0.The following conditions are satisfied in anyBCI-algebra X˜:(a5)∀ϖ,ϱ,ϑ∈X˜0∗0∗ϖ∗ϑ∗ϱ∗ϑ=0∗ϱ∗0∗ϖ.(a6)∀ϖ,ϱ∈X˜0∗0∗ϖ∗ϱ=0∗ϱ∗0∗ϖ.In aBCK/BCI-algebra X˜, a nonempty subset T of X˜ is called a BCK/BCI-subalgebra of X˜ if ϖ∗ϱ∈T∀ϖ,ϱ∈T.In aBCK/BCI-algebra X˜, a fuzzy set μ in X˜ is called a fuzzy BCK/BCI-algebra if it satisfies(1)∀ϖ,ϱ∈X˜μϖ∗ϱ≥minμϖ,μϱ.In a setX˜, a fuzzy set μ in X˜ of the form(2)μϑ≔t∈0,1,ifϑ=ϖ,0,ifϑ≠ϖ,is called a fuzzy point with support ϖ and value t and is denoted by ϖt.For a fuzzy setμ in a set X˜ and a fuzzy point ϖt, Pu and Liu [34] presented the symbol ϖtαμ, where α∈∈,q,∈∨q,∈∧q. If ϖt∈μ (resp. ϖtqμ), then we mean μϖ≥t (resp. μϖ+t>1), and in this case, ϖt is said to belong to (resp. be quasi-coincident with) a fuzzy set μ. If ϖt∈∨qμ (resp. ϖt∈∧qμ), then we mean ϖt∈μ or ϖtqμ (resp. ϖt∈μ and ϖtqμ).For an initial universe setU and a set of parameters E, let PU denote the power set of U and Ω⊂E. Molodtsov [7] defined the soft set as follows.Definition 1.
(see [7]). A pair ζ,Ω is called a soft set over U, where ζ is a function given by(3)ζ:Ω⟶PU.The setζε for ε∈Ω may be considered as the set of ε-approximate elements of the soft set ζ,Ω. Clearly, a soft set is not a set. We refer the reader to [7] for illustration where several examples are presented.LetℱU denote the set of all fuzzy sets in U.Definition 2.
(see [9]). A pair ζ˜,Ω is called a fuzzy soft set over U where ζ˜ is a mapping given by(4)ζ˜:Ω⟶ℱU.For allϖ∈Ω, ζ˜ϖ∈ℱU and it is called fuzzy value set of parameter ϖ. If ζ˜ϖ, for all ϖ∈Ω, is a crisp subset of U, then ζ˜,Ω is degenerated to be the standard soft set. Thus, fuzzy soft sets are a generalization of standard soft sets.We will useFSU to denote the set of all fuzzy soft sets over U.Definition 3.
(see [9]). Let ζ˜,Ω,η˜,Ω∈FSU. The union of ζ˜,Ω and η˜,℧ is defined to be the fuzzy soft set ξ˜,ϒ satisfying the following conditions:(i)
ϒ=Ω∪℧,(ii)
for allθ∈ϒ,(5)ξ˜θ=ζ˜θ,ifθ∈Ω\℧,η˜θ,ifθ∈℧\Ω,ζ˜θ∪η˜θ,ifθ∈Ω∩℧.
In this case, we writeζ˜,Ω∪˜η˜,℧=ξ˜,ϒ.Definition 4.
(see [9]). If ζ˜,Ω,η˜,℧∈FSU, then “ζ˜,Ω AND η˜,℧” denoted by ζ˜,Ω∧˜η˜,℧ is defined by(6)ζ˜,Ω∧˜η˜,℧=ξ˜,Ω×℧,where ξ˜α,β=ζ˜α∩η˜β∀α,β∈Ω×℧.Definition 5.
(see [35]). For two soft sets ζ˜,Ω and η˜,℧, the extended intersection is the soft set ξ˜,ϒ where ϒ=Ω∪℧, and for every θ∈ϒ,(7)ξ˜θ=ζ˜θ,ifθ∈Ω\℧,η˜θ,ifθ∈℧\Ω,ζ˜θ∩η˜θ,ifθ∈Ω∩℧.We writeζ˜,Ω∩˜θη˜,℧=ξ˜,ϒ.Definition 6.
(see [35]). Let ζ˜,Ω,η˜,℧∈FSU such that Ω∩℧≠∅. The restricted intersection of ζ˜,Ω and η˜,℧ is denoted by ζ˜,Ω∩˜rη˜,℧ and is defined as ζ˜,Ω∩˜rη˜,℧=ξ˜,ϒ, where ϒ=Ω∩℧ and for all c∈ϒ, ξ˜c=ζ˜c∩η˜c.


## 3.∈,∈∨q-Fuzzy Soft BCK/BCI-Algebras

Definition 7.
(see [36]). A fuzzy set μ in X˜ is said to be an ∈,∈∨q-fuzzy subalgebra of X˜ if(8)∀ℏ,κ∈X˜∀ω1,ω2∈0,1ℏω1,κω2∈μ⟹ℏ∗κminω1,ω2∈∨qμ.Definition 8.
Letζ˜,Ω∈FSX˜ where Ω⊆E. If there exists a parameter υ∈Ω such that ζ˜υ is an ∈,∈∨q-fuzzy subalgebra of X˜, we say that ζ˜,Ω is an ∈,∈∨q-fuzzy soft BCK/BCI-algebra over X˜ based on a parameter υ. If ζ˜,Ω is an ∈,∈∨q-fuzzy soft BCK/BCI-algebra over X˜ based on all parameters, we say that ζ˜,Ω is an ∈,∈∨q-fuzzy soft BCK/BCI-algebra over X˜.The notionℱSBCK/BCIAX˜ will be used for the set of all ∈,∈∨q-fuzzy soft BCK/BCI-algebras.Example 1.
LetX˜=0,i,J,ℓ be a BCI-algebra with the following table.
LetΩ=e1,e2,e3 and let ζ˜,Ω∈FSX˜. Then, ζ˜e1, ζ˜e2, and ζ˜e3 are fuzzy sets in X˜. We define them as follows:
Then,ζ˜,Ω is an ∈,∈∨q-fuzzy soft BCI-algebra over X˜.Proposition 1.
Ifζ˜,Ω∈ℱSBCK/BCIAX˜, then(9)∀ℏ∈X˜ζ˜υ0≥minζ˜υℏ,0.5,where υ is any parameter in Ω.Proof.
Forℏ∈X˜ and υ∈Ω, we have(10)ζ˜υ0=F˜υℏ∗ℏ≥minζ˜υℏ,F˜υℏ,0.5=minζ˜υℏ,0.5.
Hence,ζ˜υ0≥minF˜υℏ,0.5 for all ℏ∈X˜ and any parameter υ in Ω.Theorem 1.
Letζ˜,Ω∈ℱSBCK/BCIAX˜. If ρ⊆Ω, then ζ˜|ρ,℧∈ℱSBCK/BCIAX˜.Proof.
(straightforward).The following example shows that there existsζ˜,Ω∈FSX˜ such that(i)
ζ˜,Ω is not an ∈,∈∨q-fuzzy soft BCK/BCI-algebra over X˜(ii)
There exists a subsetρ of Ω such that ζ˜|ρ,℧ is an ∈,∈∨q-fuzzy soft BCK/BCI-algebra over X˜Example 2.
Consider aBCK-algebra X˜=0,i,J,κ,ℓ with the following table.
LetΩ=e1,e2,e3,e4,e5 and let ζ˜,Ω∈FSX˜. Then, ζ˜e1, ζ˜e2, ζ˜e3, ζ˜e4, and ζ˜e5 are fuzzy sets in X˜. We define them as follows:
Then,ζ˜,Ω is not an ∈,∈∨q-fuzzy soft BCK-algebra over X˜ since it is not an ∈,∈∨q-fuzzy soft BCK-algebra over X˜ based on two parameters e2 and e4. However, if we take ρ=e1,e3,e5, then ζ˜|ρ,℧ is described as follows:
and it is an∈,∈∨q-fuzzy soft BCK-algebra over X˜.Theorem 2.
Every fuzzy softBCK/BCI-algebra over X˜ is an ∈,∈∨q-fuzzy soft BCK/BCI-algebra over X˜.Proof.
(straightforward).The converse of Theorem2 is not true as follows.Example 3.
Considerζ˜,Ω∈ℱSBCIAX˜ in Example 1. We know that ζ˜,Ω is not a fuzzy soft BCI-algebra over X˜ since ζ˜,Ω is not a fuzzy soft BCI-algebra over X˜ based on the parameter e1 as ζ˜e10=0.6<0.7=ζ˜e1a.Lemma 1.
(see [36]). A fuzzy set μ in X˜ is an ∈,∈∨q-fuzzy subalgebra of (11)X˜⇔∀ℏ,κ∈X˜μℏ∗κ≥minμℏ,μκ,0.5.According to the Lemma1, the following theorem is straightforward.Theorem 3.
A fuzzy soft setζ˜,Ω∈ℱSBCK/BCIAX˜ if and only if(12)∀ℏ,κ∈X˜∀u∈Ωζ˜uℏ∗κ≥minζ˜uℏ,ζ˜uκ,0.5.Theorem 4.
Ifζ˜,Ω∈ℱSBCK/BCIAX˜ such that(13)∀υ∈Ω∀ℏ∈X˜ζ˜υℏ<0.5,then ζ˜,Ω is a fuzzy soft BCK/BCI-algebra over X˜.Proof.
Letℏ,κ∈X˜ and υ∈Ω. Since ζ˜,Ω∈ℱSBCK/BCIAX˜, it follows from Theorem 3 and (13) that(14)ζ˜υℏ∗κ≥minζ˜υℏ,ζ˜υκ,0.5=minζ˜υℏ,ζ˜υκ.
Therefore,ζ˜,Ω is a fuzzy soft BCK/BCI-algebra over X˜.Theorem 5.
Ifζ˜,Ω,η˜,℧∈ℱSBCK/BCIAX˜, then the extended intersection of ζ˜,Ω and η˜,℧ is an ∈,∈∨q-fuzzy soft BCK/BCI-algebra over X˜.Proof.
Letζ˜,Ω∩˜eη˜,℧=ξ˜,ϒ be the extended intersection of ζ˜,Ω and η˜,℧. Then, ϒ=Ω∪℧. For any υ∈ϒ, if υ∈Ω∖℧ (resp. υ∈℧∖Ω), then ξ˜υ=ζ˜υ∈ℱSBCK/BCIAX˜ (resp. ξ˜υ=η˜υ∈ℱSBCK/BCIAX˜). If Ω∩℧≠∅, then ξ˜υ=ζ˜υ∩η˜υ∈ℱSBCK/BCIAX˜ for all υ∈Ω∩℧ since the intersection of two ∈,∈∨q-fuzzy BCK/BCI-algebras is an ∈,∈∨q-fuzzy BCK/BCI-algebra. Therefore, ξ˜,ϒ∈ℱSBCK/BCIAX˜.Corollary 1.
The restricted intersection of two∈,∈∨q-fuzzy soft BCK/BCI-algebras is an ∈,∈∨q-fuzzy soft BCK/BCI-algebra.Theorem 6.
Letζ˜,Ω,η˜,℧∈ℱSBCK/BCIAX˜. If Ω∩℧=∅, then the union ζ˜,Ω∪˜η˜,℧∈ℱSBCK/BCIAX˜.Proof.
By Definition3, we can write ζ˜,Ω∪˜η˜,℧=ξ˜,ϒ, where ϒ=Ω∪℧ and for all e∈ϒ,(15)ξ˜e=ζ˜e,ife∈Ω\℧,η˜e,ife∈℧\Ω,ζ˜e∪η˜e,ife∈Ω∩℧.
Sinceυ∈Ω\℧ or υ∈℧\Ω for all υ∈ϒ. If υ∈Ω\℧, then ξ˜υ=ζ˜υ∈ℱSBCK/BCIAX˜ because ζ˜,Ω∈ℱSBCK/BCIAX˜. If υ∈℧\Ω, then ξ˜υ=η˜υ∈ℱSBCK/BCIAX˜ because η˜,℧∈ℱSBCK/BCIAX˜. Hence, ξ˜,ϒ=ζ˜,Ω∪˜η˜,℧∈ℱSBCK/BCIAX˜.The following illustration shows that Theorem6 is not valid if Ω∩℧≠∅.Example 4.
LetX˜=0,1,i,J,κ be a BCI-algebra with the following table.
Consider sets of parameters as follows:(16)Ω=α1,α2,α3,α4,℧=α3,α4,β5.
Then,Ω and ℧ are not disjoint sets of parameters. Let ζ˜,Ω be a fuzzy soft set over X˜. Then, ζ˜α1, ζ˜α2, ζ˜α3, and ζ˜α4 are fuzzy sets in X˜. We define them as follows:
Then,ζ˜,Ω∈ℱSBCK/BCIAX˜. Let η˜,℧∈FSX˜. Then, η˜α3, η˜α4, and η˜β5 are fuzzy sets in X˜. We define them as follows:
Then,η˜,℧∈ℱSBCK/BCIAX˜, and the union(17)ζ˜,Ω∪˜η˜,℧=ξ˜,ϒ,of ζ˜,Ω and η˜,℧ is described as follows:
For a parameterα3∈Ω∩℧, we have(18)ξ˜α3J∗i=ζ˜α3∪η˜α3J∗i=ζ˜α3∪η˜α3κ=maxζ˜α3κ,η˜α3κ=max0.1,0.1=0.1,minξ˜α3J,ξ˜α3i,0.5=minζ˜α3∪η˜α3J,ζ˜α3∪η˜α3i,0.5=minmaxζ˜α3J,η˜α3J,maxζ˜α3J,η˜α3J,0.5=minmax0.3,0.1,max0.1,0.3,0.5=0.3.
Thus, from Theorem3, ξ˜,ϒ∉ℱSBCK/BCIAX˜ based on the parameter α3 and so that ξ˜,ϒ∉ℱSBCK/BCIAX˜.Theorem 7.
Ifζ˜,Ω,η˜,℧∈ℱSBCK/BCIAX˜, then ζ˜,Ω∧˜η˜,℧∈ℱSBCK/BCIAX˜.Proof.
By Definition4, we have(19)ζ˜,Ω∧˜η˜,℧=ξ˜,Ω×℧,where ξ˜u,v=ζ˜u∩η˜v,∀u,v∈Ω×ρ. For any ℏ,κ∈X˜, we have(20)ξ˜u,vℏ∗κ=ζ˜u∩η˜vℏ∗κ=minζ˜uℏ∗κ,η˜vℏ∗κ≥minminζ˜uℏ,ζ˜uκ,0.5,minη˜vℏ,η˜vκ,0.5=minminζ˜uℏ,η˜vℏ,minζ˜uκ,η˜vκ,0.5=minζ˜u∩η˜vℏ,ζ˜u∩η˜vκ,0.5=minξ˜u,vℏ,ξ˜u,vκ,0.5.
Hence,ξ˜,Ω×℧=ζ˜,Ω∧˜η˜,℧∈ℱSBCK/BCIAX˜ based on u,v by using Theorem 3. Since u,v is arbitrary,(21)ξ˜,Ω×℧=ζ˜,Ω∧˜η˜,℧∈ℱSBCK/BCIAX˜.Definition 9.
Letζ˜,Ω,η˜,℧∈ℱSBCK/BCIAX˜. We say that ζ˜,Ω is an ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebra of η˜,℧ if(1)
Ω⊆ρ,(2)
ζ˜u is an ∈,∈∨q-fuzzy sub-BCK/BCI-algebra of η˜u for all u∈Ω; that is, ζ˜u is an ∈,∈∨q-fuzzy BCK/BCI-algebra satisfying the following condition:(22)∀ℏ∈X˜ζ˜uℏ≤η˜uℏ.Example 5.
Letζ˜,Ω∈ℱSBCK/BCIAX˜ in Example 1. For a subset ρ=e1,e3 of Ω, let η˜,℧ be fuzzy soft set over X˜ which is defined as follows:
Then,η˜,℧ is an ∈,∈∨q-fuzzy soft sub-BCI-algebra of ζ˜,Ω.Example 6.
LetX˜=0,1,2,3,4 be a BCK-algebra with the following Cayley table.
Letρ=e1,e2,e3,e4,e5 be a set of parameters and let η˜,℧∈FSX˜ which is defined as follows:
Then,η˜,℧∈ℱSBCKAX˜. For a subset Ω=e1,e3,e4 of ρ, let ζ˜,Ω∈FSX˜ defined by
Then,ζ˜,Ω is an ∈,∈∨q-fuzzy soft sub-BCK-algebra of η˜,℧.Theorem 8.
Letζ˜,Ω,η˜,Ω∈ℱSBCK/BCIAX˜. If ζ˜u⊆η˜u for all u∈Ω, then ζ˜,Ω is an ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebra of η˜,Ω.Proof.
(straightforward).Theorem 9.
Letξ˜,Υ∈ℱSBCK/BCIAX˜. If ζ˜,Ω and η˜,℧ are ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebras of ξ˜,ϒ, then so is the extended intersection of ζ˜,Ω and η˜,℧.Proof.
The proof is followed from Theorem5 and Definition 9.Theorem 10.
Letξ˜,Υ∈ℱSBCK/BCIAX˜. If ζ˜,Ω and η˜,℧ are ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebras of ξ˜,ϒ, then so is the union of ζ˜,Ω and η˜,℧ whenever Ω and ρ are disjoint.Proof.
The proof is followed from Theorem6 and Definition 9.Theorem 11.
Letξ˜,Υ∈ℱSBCK/BCIAX˜. If ζ˜,Ω and η˜,℧ are ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebras of ξ˜,ϒ, then ζ˜,Ω∧˜η˜,℧ is an ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebra of ξ˜,ϒ∧˜ξ˜,ϒ.Proof.
The proof is followed from Theorem7 and Definition 9.


## 4. Conclusion

In this paper, we introduced the notions of∈,∈∨q-fuzzy soft BCK/BCI-algebras and ∈,∈∨q-fuzzy soft sub-BCK/BCI-algebras and investigated their related properties. Also, we discussed relations between fuzzy soft BCK/BCI-algebras and ∈,∈∨q-fuzzy soft BCK/BCI-algebras. Moreover, conditions for an ∈,∈∨q-fuzzy soft BCK/BCI-algebra to be a fuzzy soft BCK/BCI-algebra are provided. Moreover, the union, the extended intersection, and the “AND”-operation of two ∈,∈∨q-fuzzy soft (sub-) BCK/BCI-algebras are discussed, and a characterization of an ∈,∈∨q-fuzzy soft BCK/BCI-algebra is established.We hope that this work will provide a deep impact on the upcoming research in this field and other soft algebraic studies to open up new horizons of interest and innovations. To extend these results, one can further study these notions on different algebras such as rings, hemirings,LA-semigroups, semihypergroups, semihyperrings, BL-algebras, MTL-algebras, R0-algebras, MV-algebras, EQ-algebras, d-algebras, Q-algebras, and lattice implication algebras. Some important issues for future work are (1) to develop strategies for obtaining more valuable results and (2) to apply these notions and results for studying related notions in other algebraic (soft) structures.



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