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An algebraic framework for fuzzy soft subrings, cosets, and ring homomorphisms
*Corresponding author: E-mail address: abdul.razaq@ue.edu.pk (A Razaq)
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Received: ,
Accepted: ,
Abstract
This paper constructs a general algebraic framework for fuzzy soft structures in the context of ring theory. Building upon fuzzy soft set theory, the study introduces and investigates fuzzy soft subrings and fuzzy soft ideals over rings. Characterizations are established through fuzzy soft level subsets, yielding necessary and sufficient conditions that associate fuzzy soft ideals with classical ring ideals. Moreover, fuzzy soft cosets associated with fuzzy soft subrings and ideals are defined and shown to form a ring under properly defined operations. Quotient constructions are further discussed and a fuzzy soft analogue of the classical ring homomorphism theorem is presented. The results extend some classical algebraic notions to the fuzzy soft setting which enriches the theoretical foundation of fuzzy algebra.
Keywords
Fuzzy soft cosets
Fuzzy soft level subsets
Fuzzy soft rings
Ring homomorphisms
1. Introduction
Fuzzy sets (FSs), proposed by Zadeh (Zadeh, 1965), have many applications in fields such as data mining, commerce and economics. They are represented by the notation , where is the membership function for all . As an extension of crisp set theory, FSs are a useful analytical framework. They generalize the characteristic function of classical sets. To overcome the ambiguity and uncertainty, new concepts and methods have been developed from fuzzy set theory. Data representation limitations are offset with the introduction of non-membership value in addition to membership values. Atanassov (Atanassov, 1986) extended FSs to intuitionistic FSs (IFSs), offering greater freedom and flexibility in uncertainty representation while dealing with decision-making contexts (Garg and Kumar, 2018; Garg and Kumar, 2019; Ejegwa and Onyeke, 2021; Ejegwa and Agbetayo, 2023). The introduction of Pythagorean Fuzzy Set (PFS) , where and , are membership and non-membership functions satisfying for all , by Yager (Yager, 2013) extended the applicability of IFSs. This approach seeks to convert an uncertain and confusing setting into mathematical frameworks that reveal more effective strategies for addressing such issues in the physical domain (Naz et al., 2018; Li et al., 2019; Zhou et al., 2020; Hussain et al., 2022). The theory of FSs and its subsequent developments serve as mathematical instruments for addressing uncertainty. However, all these theories possess intrinsic challenges, as noted by Molodtsov (Molodtsov, 1999). The theoretical framework of soft set theory was developed in (Molodtsov, 1999), and the notion of fuzzy soft sets was introduced in (Maji et al., 2001). Additionally, (Roy and Maji, 2007)showed several applications of this concept to decision-making issue.
In classical fuzzy ring theory, a fuzzy coset is generated from a single fuzzy ideal by translating its membership function by a fixed ring element. Although this extends ordinary cosets to uncertain settings, it is non-parameterized and cannot capture parameter-dependent uncertainty. On the other hand, the fuzzy soft cosets presented in this paper are based on fuzzy soft subrings or ideals, in which each parameter generates a fuzzy coset. As a result, fuzzy soft cosets are a parameter-indexed family of fuzzy cosets and become the classical fuzzy coset construction when the parameter set has a single element.
1.1 Literature review
Liu (Liu, 1982) introduced the notion of fuzzy subrings and fuzzy ideals of rings to extend the traditional idea of subrings and ideals of rings. The literature (Wang, 1983) outlines many operations that may be executed on fuzzy ideals within a ring. (Mukherjee and Sen, 1987)) classified regular and Noetherian rings by analyzing their fuzzy ideals and presented a complete enumeration of all fuzzy prime ideals. A work on L-fuzzy semi lattices and associated ideals was discussed in reference (Ying, 1987). Additionally, (Swamy and Swamy, 1988) established the concepts of fuzzy prime and maximal ideals. The primary L-fuzzy ideal and the prime L-fuzzy ideal were first introduced by Yue (Yue, 1988), who derived several basic outcomes (fundamental results) related to these ideas. Malik (Malik, 1990) conducted an influential research on fuzzy ideals in Artinian rings. The classical ring’s ideal correspondence theorem was extended using a fuzzy framework in a study conducted in reference (Kumbhojkar and Bapat, 1991). Kumar presented an overview of the fundamental findings on fuzzy cosets of a fuzzy ideal (Kumar, 1992). In (Dixit et al., 1992), a substantial inquiry was conducted on the topic of fuzzy cosets of fuzzy ideals and fuzzy semi-prime ideals. To obtain additional information on the subject of fuzzy subrings and ideals, the literature recommends a review of (Abou-Zaid, 1993; Ahsan et al., 1993; Sharma and Sharma, 1998; Altassan et al., 2021). In (Razaq and Alhamzi, 2023), Pythagorean fuzzy ideals were introduced and several fundamental properties were established. A comprehensive study of rings in q-rung orthopair fuzzy settings was presented in (Razzaque et al., 2023). The concept of complex fuzzy rings and ideals was developed in (Al-Husban et al., 2025). Moreover, in (Çıtak, 2025), several key properties of Pythagorean fuzzy bi-ideals were investigated.
1.2 Motivation, research gap, and major contributions
1.2.1 Motivation
Classical ring theory provides a strict algebraic structure that is often inappropriate to the task of modeling of uncertainty, vagueness, and dependence on parameters of many real systems. Although fuzzy sets, fuzzy ideals and, later on, fuzzy soft sets have been explored individually, their combination into an internally consistent structure of rings is yet to be achieved. In particular, the combination of fuzziness, parameterization, and algebraic operations i.e. cosets, quotients, and homomorphisms needs to be approached in a systematic manner.
1.2.2 Research gap
Existing studies on fuzzy subrings and fuzzy ideals deal mostly with one-parameter fuzzy structures. Moreover, fuzzy soft sets include parametric dependence but do not have an extensive algebraic development in the context of group theory and ring theory. Existing research has not dealt with:
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i.
The characterization of fuzzy soft ideals by level subsets in rings.
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ii.
The construction and algebraic behavior of fuzzy soft cosets.
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iii.
The formation of quotient rings and homomorphism results in the fuzzy soft context.
This study fills these gaps with the coherent and rigorous extension of classical ring-theoretic results to the fuzzy soft environment.
1.2.3 Major contributions
The major findings in this research are:
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1.
Definition and formalization of fuzzy soft subrings and fuzzy soft ideals of rings.
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2.
Characterization of fuzzy soft ideals in terms of fuzzy soft level subsets and their equivalence with classical ideal properties.
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3.
Construction of fuzzy soft cosets and demonstration that the collection of all such cosets is a ring with respect to suitably defined operations.
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4.
Construction of quotient rings induced by fuzzy soft ideals and extension of classical ring homomorphism theorems to the fuzzy soft context.
2. Basic Definitions
This section provides the necessary background and definitions for understanding the major results proved in this paper.
Definition 2.1 (Maji et al., 2001) Let represent the universal set, denote the set of all fuzzy subsets over and be the set of parameters. A mapping is referred to as a fuzzy soft set (FSS), denoted by , over the universal set with respect to the parameters . This is formally represented as follows:
Definition 2.2 (Maji et al., 2001) Let and be two be two ft set meters then Ri FSSs over . Suppose that the sets of parameters and have a non-empty intersection, denoted as . The intersection of these two fuzzy soft sets is denoted as , where is a new mapping over W, and and are fuzzy subsets of for each .
Formally, the intersection of these fuzzy soft sets is expressed as:
Now, let’s define the union of two fuzzy soft sets.
Definition 2.3 (Maji et al., 2001) Let and be two be twoft set meters then RiFSSs over . Suppose that the union of sets of parameters and is denoted as . The union of these two fuzzy soft sets is denoted as , where is a new mapping over , and and are fuzzy subsets of for each
Formally, the union of these fuzzy soft sets is expressed as:
where for all .
Definition 2.4 (Maji et al., 2001) Let and be two FSSs over . Then, the FSS (ω, , X) is said to be a subset of the FSS , denoted as , if the following conditions hold:
i.
ii. If , then for all .
Definition 2.5 (İnan et al., 2012) Let be a ring and for each fixed parameter , is a fuzzy subset of . A fuzzy soft set is referred to as a fuzzy soft subring (FSSR) over in terms of , if for , the following two conditions are satisfied:
i.
ii.
In other words, the fuzzy soft set is classified as an FSSR over the ring if and only if each fuzzy subset , corresponding to every parameter , qualifies as a fuzzy subring of .
Example 2.1 Consider , then the power set of , that is,
Forms a ring under symmetric difference and intersection . We choose as a set of parameters, then we construct an FSSR over in terms of as follows;
Definition 2.6 (İnan et al., 2012) An FSS is a fuzzy soft ideal (FSI) over if for every and , the following axioms are true:
i.
ii.
Theorem 2.1 (İnan et al., 2012) The intersection of two FSIs over is an FSI over .
3. Characterization of Fuzzy Soft Subrings and Ideals in Rings
The section examines fuzzy soft level subsets and the developed theorems in this context provide insights into the relationships between fuzzy soft ideals and their corresponding fuzzy level sets.
Remark 3.1 Every fuzzy soft Ideal (FSI) of a ring is an FSSR of . However, the converse is not always true. Example 2.1 illustrates a fuzzy soft subring over the power set that is not an FSI over , where .
Definition 3.1 Let be an FSS over a ring . For a given let represent the fuzzy level subset of the fuzzy set . Then, the set is called fuzzy soft level subset (FSLSS) of .
Theorem 3.1 An FSSR is an FSI over if and only if for every the fuzzy level set is an ideal of .
Proof Suppose that is an FSI. For a fixed and , let Then . By Definition 2.6 of an FSI:
Hence, , proving is an ideal of .
Conversely, let be an ideal of for every . Suppose that and , . Then
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i.
. Since is an ideal of , therefore , which yields .
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1.
ii. either or . Since is an ideal of , therefore, we yield . Thus, .
Therefore, is an FSI.
Theorem 3.2 If is an FSI over , then the fuzzy soft level subset of is an ideal of , where for every .
Proof: Let be a fuzzy soft ideal of . Then is a fuzzy ideal of implying that is an ideal of for every . Since , therefore is an ideal of .
Theorem 3.3 Let be a division ring. An FSS is an FSI of if and only if the following condition holds for every nonzero element and :
Proof Assume that is an FSI over . Let such that . Since is an FSI of , therefore is an FI of .
Now, for , consider
Furthermore, it is well known that if is fuzzy ideal of , then for every .
Conversely, suppose that for every . Let .
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ii.
1. If , then so JK1210_179 - Copy.eps]. If , then and .
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2.
If either or , then , and If both are non-zero, , and by assumption,
Hence, is an FSI.
Definition 3.2 Let be an FSS over a ring . Then, the subset of is defined as follows:
Theorem 3.4 If is an FSI over , then the subset of is an ideal of .
Proof Since is an FSI, Theorem 3.2 shows that every fuzzy soft level subset is an ideal of for . In particular, choosing = yields =, which is therefore an ideal of .
Theorem 3.5 Let be an FSI over . Consider the quotient ring , and let
be an FSS over . Then, is an FSI over .
Proof Since is an FSI, Theorem 3.4 ensures is an ideal of so the quotient ring is well defined. To prove the required results, we have to show that, for every , is an FI of . For this, firstly we will prove that the mapping defined by is well-defined.
Let
FSI conditions:
Let
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1.
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2.
Thus, each is fuzzy ideal, making an FSI.
Theorem 3.6 Suppose that is an Ideal of and is an FSI of such that , then there exists an FSI of such that for every .
Proof Define an FSS by setting for all .
Since is an FSI of , for any and
Hence is an FSI of .
Now, for any . Therefore, .
4. Fuzzy Soft Cosets of Fuzzy Soft Subrings/Ideals
This section delves into fuzzy soft cosets, defining and characterizing them in the context of fuzzy soft rings. We explore the properties of these cosets, leading to the establishment of a new ring formed by fuzzy soft cosets, under specific binary operations. Moreover, it presents the concept of fuzzy soft quotient ideal, associated with the ring formed by fuzzy soft cosets. Finally, the classical ring homomorphism theorem is proved in the context of fuzzy soft rings.
Definition 4.1 Let be an FSSR over and . Then fuzzy soft coset (FSC) of associated with is denoted by and is defined as
Theorem 4.1 Let be an FSSR over . Then for every and .
Proof Consider an FSSR, , over . Assume that .
For each and , we have
Conversely, suppose that for every , then and . Therefore, and .
Consider,
Also,
Therefore, for each .
for every . Thus, for every .
Theorem 4.2 Let be an FSI of . Then the set of all FSCs of over , forms a ring with the following binary operations;
and , for every .
Proof. First, we show that addition and multiplication defined on are well-defined. For this suppose that and . Then for every Eqs. (4.1-4.7)
So,
Putting in Eq. (4.3) in Eq. (4.4) and in Eq. (4.3), we have
Now
So,
Similarly, we can prove that
The inequalities in Eqs. (4.8) and (4.9) yield the following:
Furthermore, the application of the similar approach results in
The Eqs. (4.10) and (4.11) yield that and holds for every .
Hence, both addition and multiplication defined on are well-defined. Next, acts as zero of and for each there exists such that . Associativity and distributivity follow directly from the corresponding properties of the ring .
Hence, forms a ring under the defined operations.
Example 4.1 Consider an FSI,
of the ring with respect to the set . We want to find the set which consists of all FSCs of in . Upon calculation, we obtain the set .
We now examine the Cayley tables shown in Table 1 and Table 2 for the ring , employing the binary operations defined in Theorem 4.2:
| + | |||
|---|---|---|---|
+ : Denotes the addition operation defined on the set of fuzzy soft cosets, as introduced in Theorem 4.2. It represents the binary operation under which the collection of fuzzy soft cosets forms a ring.
* : Represents the multiplication operation defined on the fuzzy soft cosets. As described in Theorem 4.2, this operation, together with “+”, establishes the ring structure of the set of fuzzy soft cosets.
From the above Cayley tables, we see that is a ring under the defined binary operations.
Remark 4.1 Above example illustrates the theoretical construction developed in Section 4. In particular, the fuzzy soft ideal satisfies the conditions of Theorem 4.2, ensuring that the collection of all fuzzy soft cosets is well defined. The computation shows that distinct representatives in generate only three distinct fuzzy soft cosets, which reflects the role of the induced crisp ideal in identifying equivalent elements. The Cayley tables further confirm that the fuzzy soft coset system forms a ring, as described by the theoretical results.
Remark 4.2 If is an FSI of a ring such that is constant function, then for every .
Definition 4.2 Let be an FSI of , then the FSI
of defined by for every , is called fuzzy soft quotient ideal (FSQI) associated with .
Now, we are in a position to prove the classical ring homomorphism theorem in fuzzy soft environment.
Theorem 4.3 If is an FSI of a ring , then a mapping defined by for every is a ring homomorphism such that Ker
Proof. Let , then
and
Now, we prove that, for every , if and only if .
Suppose that . Then, for every , we have .
Now if .
On the other hand, if
Hence, in both cases, the equality holds for every and . Thus, .
Conversely, suppose that . So, for every we have
For each ,
Thus, .
Consider,
Ker
Example 4.2 Consider an FSI,
,
of the ring with respect to the set . We want to find the set , which consists of all FSCs of in . Upon calculation, we obtain the set =. Utilizing the approach outlined in Theorem 4.3, we define as follows;
Now, . This demonstrates that . Also, and show that . S In a similar way, it can be readily confirmed that and for every . Consequently, represents a ring homomorphism. Finally, is 0 of , therefore Ker thus fulfilling Theorem 4.3.
4.1 Remarks on the main results and their relation to classical ring theory
The findings in this paper generalize various classical ring theory concepts into the fuzzy soft environment retaining their basic algebraic properties. Specifically, the definition of fuzzy soft ideals through fuzzy soft level subsets demonstrates that various structural properties of classical ideals can be preserved when uncertainty and parameterization are added. The result is a classical conformance between ideals and their level cuts in fuzzy ring theory, and it proves that fuzzy soft ideals are natural extensions of crisp ideals, rather than unrelated constructions.
Construction of fuzzy soft cosets and construction of a ring consisting of such cosets are parallel to classical construction of quotient rings. It is shown in theorem 4.2 that the collection of all the fuzzy soft cosets of a fuzzy soft ideal is a ring with appropriately defined operations. It demonstrates that even in the case with graded membership and parameters, quotient structure will be meaningful.
Moreover, the homomorphism outcome of Theorem 4.3 is regarded as a fuzzy soft version of the fundamental homomorphism theorem of rings. The fact that the kernel is identified with the induced fuzzy soft ideal supports the concept that kernels still have the same structural role as in classical ring theory. Consequently, the presented framework maintains the fundamental algebraic intuition of homomorphisms of rings in addition to generalizing them to settings that are characterized by uncertainty and numerous parameters.
These findings indicate that fuzzy soft ring theory is not a formal extension but an extension that is uniform with classical ring theory.
5. Conclusions
This paper generalizes basic concepts of ring theory into the fuzzy soft environment by combining fuzziness and parameterization in a unified algebraic context. Through the definition of fuzzy soft subrings, fuzzy soft ideals and fuzzy soft cosets, the study provides strong linkages between fuzzy soft structures and classical ring ideals through level subset characterizations. The construction of rings formed by fuzzy soft cosets and the development of quotient and homomorphism results demonstrate that many classical algebraic properties persist in the fuzzy soft setting under suitable conditions. These results not only generalize the known theories of fuzzy ring but also serve as a solid foundation for further research in fuzzy algebra, soft computing and uncertainty aware algebraic systems.
Acknowledgement
This project is supported by the Ongoing Research Funding program (ORF-2026-317) King Saud University, Riyadh, Saudi Arabia.
CRediT authorship contribution statement
Abdul Razaq: Conceptualization, writing – review & editing, supervision. Muhammad Safeer: Methodology, writing – original draft, data curation, writing – review & editing. Hanan Alolaiyan: Validation, funding, writing – review & editing. Qin Xin: Formal analysis, validation, writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Declaration of generative AI and AI-assisted technologies in the writing process
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.
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