7.9
CiteScore
 
3.6
Impact Factor
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
ABUNDANCE ESTIMATION IN AN ARID ENVIRONMENT
Case Study
Correspondence
Corrigendum
Editorial
Full Length Article
Invited review
Letter to the Editor
Original Article
Research Article
Retraction notice
REVIEW
Review Article
SHORT COMMUNICATION
Short review
7.2
CiteScore
3.7
Impact Factor
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
ABUNDANCE ESTIMATION IN AN ARID ENVIRONMENT
Case Study
Correspondence
Corrigendum
Editorial
Full Length Article
Invited review
Letter to the Editor
Original Article
Research Article
Retraction notice
REVIEW
Review Article
SHORT COMMUNICATION
Short review
View/Download PDF

Translate this page into:

Research Article
2026
:38;
12102025
doi:
10.25259/JKSUS_1210_2025

An algebraic framework for fuzzy soft subrings, cosets, and ring homomorphisms

Department of Mathematics, Division of Science and Technology, University of Education, Lahore 54770, Pakistan
Department of Mathematics, King Saud University, Riyadh, 11451, Saudi Arabia
Department of Computer Science, University of the Faroe Islands, Vestara Bryggja 15, FO 100 Torshavn, Faroe Islands, 110, Denmark

*Corresponding author: E-mail address: abdul.razaq@ue.edu.pk (A Razaq)

Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

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 Γ= μΓ(x)xxX , where μΓ(x):X[0,1] is the membership function for all xX. 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) Γ= μΓ x,νΓ x x:xX  , where μΓ:X 0,1 and νΓ:X[0,1], are membership and non-membership functions satisfying μΓ x 2 + νΓ x 2  1 for all xX, 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:

  • i.

    The characterization of fuzzy soft ideals by level subsets in rings.

  • ii.

    The construction and algebraic behavior of fuzzy soft cosets.

  • 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:

  • 1.

    Definition and formalization of fuzzy soft subrings and fuzzy soft ideals of rings.

  • 2.

    Characterization of fuzzy soft ideals in terms of fuzzy soft level subsets and their equivalence with classical ideal properties.

  • 3.

    Construction of fuzzy soft cosets and demonstration that the collection of all such cosets is a ring with respect to suitably defined operations.

  • 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 X represent the universal set, IX denote the set of all fuzzy subsets over X, and ψ be the set of parameters. A mapping ω:ψIX is referred to as a fuzzy soft set (FSS), denoted by (ω,ψ,X), over the universal set X with respect to the parameters ψ. This is formally represented as follows:

ω,ψ,X = α, ωα=Γα :αψ = α, μ Γα x x :xX : αψ .

Definition 2.2 (Maji et al., 2001) Let (ω,ψ,X)={(α,ω(α)= Γα): αψ} and (φ,ψ,X)={(β,φ(β)=Kβ): βψ} be two be two ft set meters then Ri FSSs over X. Suppose that the sets of parameters ψ and ψ have a non-empty intersection, denoted as W=ψψ . The intersection of these two fuzzy soft sets is denoted as (δ,W,X)={(c,{δ(c)=ΓcKc}):cW}, where δ is a new mapping over W, and Γc and Kc are fuzzy subsets of X for each cW.

Formally, the intersection of these fuzzy soft sets is expressed as:

δ,W,X = c, μ ΓcKc x x:xX :cW = c, min μ Γc x,μ Kc x x:xX :cW.

Now, let’s define the union of two fuzzy soft sets.

Definition 2.3 (Maji et al., 2001) Let (ω,ψ,X)={(α,ω(α)= Γα): αψ} and (φ,ψ,X)={(β,φ(β)= Kβ): β ψ} be two be twoft set meters then RiFSSs over X. Suppose that the union of sets of parameters ψ and ψ is denoted as W=ψψ . The union of these two fuzzy soft sets is denoted as (δ,W,X)={(c,{δ(c)= Γc Kc}):cW}, where δ is a new mapping over W, and Γc and Kc are fuzzy subsets of X for each cW.

Formally, the union of these fuzzy soft sets is expressed as:

δ,W,X = c,δc=ΓcKc :cW = c, μ ΓcKc x x:xX :cW ,

where μ ΓcKc x= max μ Γc x,μ Kc x     if cψψ μ Γc x,                                 if cψψ μ Kc x,                                if cψψ for all xX.

Definition 2.4 (Maji et al., 2001) Let ω,ψ,X = α,ωα = Γα :αψ and φ,ψ,X = α,φα=Kα :αψ be two FSSs over X. Then, the FSS (ω, ψ, X) is said to be a subset of the FSS φ, ψ, X , denoted as ω, ψ, X    φ,ψ, X , if the following conditions hold:

  • i. ψψ

  • ii. If αψ,ψ , then μ Γα (x)μ Kα (x) for all xX.

Definition 2.5 (İnan et al., 2012) Let Υ be a ring and for each fixed parameter αψ, Γα is a fuzzy subset of Υ. A fuzzy soft set (ω,ψ,Υ)={(α,ω(α)=Γα):αψ} is referred to as a fuzzy soft subring (FSSR) over Υ in terms of ψ, if for κ1 ,κ2 Υ, the following two conditions are satisfied:

  • i. μ Γα κ1 κ2 min μ Γα κ1 ,μ Γα κ2

  • ii. μ Γα κ1 .κ2 min μ Γα κ1 ,μ Γα κ2

In other words, the fuzzy soft set ω,ψ,Υ = α,ωα=Γα :αψ is classified as an FSSR over the ring Υ if and only if each fuzzy subset Γα , corresponding to every parameter αψ, qualifies as a fuzzy subring of Υ.

Example 2.1 Consider X={0,1,2,3}, then 2X the power set of X, that is,

2X= φ,X, 0 , 1 , 2 , 3 , 0,1 , 0,2 , 0,3 , 1,2 , 1,3 , 2,3 , 0,1,2 , 0,1,3 , 0,2,3 , 1,2,3

Forms a ring under symmetric difference Δ and intersection . We choose P={α,β} as a set of parameters, then we construct an FSSR (ω,P, 2X) over 2X in terms of P as follows;

ω,P, 2X = α, 0.90 φ, 0.65 X, 0.75 0 , 0.90 1 , 0.90 2 , 0.65 3 , 0.75 0,1 , 0.75 0,2 , 0.65 0,3 , 0.90 1,2 , 0.65 1,3 , 0.65 2,3 , 0.75 0,1,2 , 0.65 0,1,3 , 0.65 0,2,3 , 0.65 1,2,3 , β, 0.85 φ, 0.55 X, 0.70 0 , 0.85 1 , 0.85 2 , 0.55 3 , 0.70 0,1 , 0.70 0,2 , 0.55 0,3 , 0.85 1,2 , 0.55 1,3 , 0.55 2,3 , 0.70 0,1,2 , 0.55 0,1,3 , 0.55 0,2,3 , 0.55 1,2,3.

Definition 2.6 (İnan et al., 2012) An FSS ω,ψ,Υ = α,Γα= μ Γα κ κ:κΥ :αψ is a fuzzy soft ideal (FSI) over Υ if for every κ1 ,κ2 Υ and αψ, the following axioms are true:

  • i. μ Γα κ1 κ2 min μ Γα κ1 ,μ Γα κ2 .

  • ii. μ Γα κ1 .κ2 max μ Γα κ1 ,μ Γα κ2 .

Theorem 2.1 (İnan et al., 2012) The intersection of two FSIs over Υ is an FSI over Υ.

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 Υ is an FSSR of Υ. However, the converse is not always true. Example 2.1 illustrates a fuzzy soft subring (ω,ψ, 2X) over the power set 2X that is not an FSI over 2X , where X={0,1,2,3}.

Definition 3.1 Let ω,ψ,Υ = α,Γα= μ Γα κ κ:κΥ :αψ be an FSS over a ring Υ. For a given σ 0,1 , let Γα σ represent the fuzzy level subset of the fuzzy set Γα . Then, the set ω,ψ,Υ (σ) = κΥ:μ Γα κσ for every αψ = κΥ:κ Γα (σ) is called fuzzy soft level subset (FSLSS) of ω,ψ,Υ .

Theorem 3.1 An FSSR ω,ψ,Υ is an FSI over Υ if and only if for every σ 0,μ Γα 0 , the fuzzy level set Γα σ is an ideal of Υ.

Proof Suppose that ω,ψ,Υ = α,Γα:αψ is an FSI. For a fixed αψ and σ 0,μ Γα 0 , let κ1 ,κ2 Γα σ . Then μ Γα κ1 ,μ Γα κ2 σ. By Definition 2.6 of an FSI:

μ Γα κ1 κ2 min μ Γα κ1 ,μ Γα κ2 σ, μ Γα κ1 .κ2 max μ Γα κ1 ,μ Γα κ2 σ.

Hence, κ1 κ2 Γα σ , κ1 .κ2 Γα σ , proving Γα σ is an ideal of Υ.

Conversely, let Γα σ be an ideal of Υ for every σ 0,μ Γα 0 . Suppose that κ1 ,κ2 Υ and μ Γα κ1 =σ1 , μ Γα κ2 =σ2 . Then

  • i.

    κ1 ,κ2 Γα min σ1 ,σ2 . Since Γα min σ1 ,σ2 is an ideal of Υ, therefore  κ1 κ2 Γα min σ1 ,σ2 , which yields μ Γα κ1 κ2 min σ1 ,σ2 =min μ Γα κ1 ,μ Γα κ2 .

  • 1.

    ii. either κ1 Γα max σ1 ,σ2 or κ2 Γα max σ1 ,σ2 . Since Γα max σ1 ,σ2 is an ideal of Υ, therefore, we yield κ1 κ2 Γα max σ1 ,σ2 . Thus, μ Γα κ1 .κ2 max σ1 ,σ2 = μ Γα κ1 ,μ Γα κ2 .

Therefore, ω,ψ,Υ is an FSI.

Theorem 3.2 If ω,ψ,Υ = α, μ Γα κ κ κΥ :αψ is an FSI over Υ, then the fuzzy soft level subset ω,ψ,Υ σ of ω,ψ,Υ is an ideal of Υ, where σμ Γα 0 for every αψ.

Proof: Let ω,ψ,Υ be a fuzzy soft ideal of Υ. Then Γα is a fuzzy ideal of Υ, implying that Γα σ is an ideal of Υ for every αψ. Since ω,ψ,Υ σ= αψ  Γα σ , therefore ω,ψ,Υ σ is an ideal of Υ.

Theorem 3.3 Let Υ be a division ring. An FSS ω,ψ,Υ = α, μ Γα (κ)κ:κΥ :αψ is an FSI of Υ if and only if the following condition holds for every nonzero element κΥ and αψ:

μ Γα κ=μ Γα 1 μ Γα 0 .

Proof Assume that ω,ψ,Υ = α, μ Γα κ κ:κΥ :αψ is an FSI over Υ. Let αψ such that ωα=Γα . Since ω,ψ,Υ is an FSI of Υ, therefore Γα is an FI of Υ.

Now, for 0κΥ, consider

μ Γα κ=μ Γα κ.1 max μ Γα κ,μ Γα 1 μ Γα 1 =μ Γα κ1 κ max μ Γα κ,μ Γα κ1 μ Γα κ μ Γα κ=μ Γα 1

Furthermore, it is well known that if Γα is fuzzy ideal of Υ, then μ Γα (0)μ Γα (κ) for every κΥ.

Conversely, suppose that μ Γα (κ)=μ Γα (1)μ Γα (0) for every κΥ{0}. Let κ1 ,κ2 Υ.

  • ii.

    1. If κ1 κ2 , then κ1 κ2 0, so μ Γα (κ1 κ2 )=μ Γα (1)min{μ Γα (κ1 ),μ Γα (κ2 )}JK1210_179 - Copy.eps]. If κ1 =κ2 , then   κ1 κ2 =0, and μ Γα (0)min{μ Γα (κ1 ),μ Γα (κ2 )}.

  • 2.

    If either κ1 =0 or κ2 =0 , then κ1 κ2 =0 , and μ Γα 0 max μ Γα κ1 ,μ Γα κ2 . If both are non-zero, κ1 κ2 0 , and by assumption, μ Γα κ1 .κ2 =μ Γα 1 =max μ Γα κ1 ,μ Γα κ2 .

Hence, ω,ψ,Υ is an FSI.

Definition 3.2 Let ω,ψ,Υ = α,Γα= μ Γα κ κ:κΥ :αψ be an FSS over a ring Υ. Then, the subset ω,ψ,Υ * of Υ is defined as follows:

ω,ψ,Υ * = κΥ:μ Γα κ=μ Γα 0  for every αψ.

Theorem 3.4 If ω,ψ,Υ = α, μ Γα κ κ κΥ :αψ is an FSI over Υ, then the subset ω,ψ,Υ * of Υ is an ideal of Υ.

Proof Since ω,ψ,Υ is an FSI, Theorem 3.2 shows that every fuzzy soft level subset ω,ψ,Υ σ is an ideal of Υ for σμ Γα 0 . In particular, choosing σ= μ Γα 0 yields ω,ψ,Υ * = ω,ψ,Υ μ Γα 0   , which is therefore an ideal of Υ.

Theorem 3.5 Let ω,ψ,Υ = α, μ Γα κ κ κΥ :αψ be an FSI over Υ. Consider the quotient ring Υ ω,ψ,Υ * , and let

φ,ψ,Υ ω,ψ,Υ * = α,φα= Πα= μ Πα κ+ ω,ψ,Υ * =μ Γα κ κ+ ω,ψ,Υ * :κ+ ω,ψ,Υ *Υ ω,ψ,Υ * :αψ

be an FSS over Υ [ω,ψ,Υ]* . Then, φ,ψ,Υ [ω,ψ,Υ]* is an FSI over Υ ω,ψ,Υ * .

Proof Since ω,ψ,Υ = α,ωα=Γα :αψ is an FSI, Theorem 3.4 ensures ω,ψ,Υ * is an ideal of Υ, so the quotient ring Υ ω,ψ,Υ * is well defined. To prove the required results, we have to show that, for every αψ, Πα= μ Πα κ+ ω,ψ,Υ * =μ Γα κ,   κ+ ω,ψ,Υ * :κ+ ω,ψ,Υ *Υ ω,ψ,Υ * is an FI of Υ [ω,ψ,Υ]* . For this, firstly we will prove that the mapping μ Πα : Υ [ω,ψ,Υ]* [0,1] defined by μ Πα (κ+ [ω,ψ,Υ]*)=μ Γα (κ) is well-defined.

Let

κ1 + ω,ψ,Υ * =  κ2 + ω,ψ,Υ * κ1 κ2 ω,ψ,Υ * μ Γα κ1 κ2 =μ Γα 0 μ Γα κ1 =μ Γα κ2 .

FSI conditions:

Let κ1 + ω,ψ,Υ *,  κ2 + ω,ψ,Υ *Υ ω,ψ,Υ * .

  • 1.

    μ Πα (κ1 + [ω,ψ,Υ]*) κ2 + [ω,ψ,Υ]* = μ Πα (κ1 κ2 )+ [ω,ψ,Υ]* =μ Γα (κ1 κ2 )min μ Πα κ1 + [ω,ψ,Υ]* ,μ Πα κ2 + [ω,ψ,Υ]* .

  • 2.

    μ Πα κ1 + ω,ψ,Υ * κ2 + ω,ψ,Υ * = μ Πα κ1 κ2 + ω,ψ,Υ * =μ Γα κ1 κ2 max μ Πα κ1 + ω,ψ,Υ * ,μ Πα κ2 + ω,ψ,Υ * .

Thus, each Πα is fuzzy ideal, making φ,ψ,Υ ω,ψ,Υ * an FSI.

Theorem 3.6 Suppose that I is an Ideal of Υ and ω,ψ,  ΥI = α,ωα=Γα :αψ is an FSI of ΥI such that μ Γα (κ+I)=μ Γα (I) κI, then there exists an FSI φ,ψ,Υ of Υ such that φ,ψ,Υ *=I for every αψ.

Proof Define an FSS φ,ψ,Υ = α,φα=Kα :αψ by setting μ Kα (κ)=μ Γα (κ+I) for all κΥ.

Since ω,ψ,  ΥI is an FSI of ΥI , for any κ1 ,κ2 Υ and αψ:

μ Kα κ1 κ2 =μ Γα κ1 κ2 +I =μ Γα κ1 +I κ2 +I min μ Γα κ1 +I ,μ Γα κ2 +I =min μ Kα κ1 ,μ Kα κ2 , μ Kα κ1 κ2 =μ Γα κ1 κ2 +I =μ Γα κ1 +I κ2 +I max μ Γα κ1 +I ,μ Γα κ2 +I =max μ Kα κ1 ,μ Kα κ2 .

Hence φ,ψ,Υ is an FSI of Υ.

Now, for any κ1 φ,ψ,Υ * μ Kα κ1 =μ Kα 0 αμ Γα κ1 +I =μ Γα I  α κ1 I. Therefore, φ,ψ,Υ *=I.

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 ω,ψ,Υ = α,ωα=Γα :αψ be an FSSR over Υ and κΥ. Then fuzzy soft coset (FSC) of ω,ψ,Υ associated with κ is denoted by ω,ψ,Υ κ and is defined as

ω,ψ,Υ  κ = α, Γα κ = μ Γα κ (x)x = μ Γα (xκ),x :xΥ :αψ .

Theorem 4.1 Let ω,ψ,Υ = α,ωα=Γα :αψ be an FSSR over Υ. Then ω,ψ,Υ κ1 = ω,ψ,Υ κ2 κ1 + Γα *=κ2 + Γα * for every αψ and κ1 ,κ2 Υ.

Proof Consider an FSSR, ω,ψ,Υ = α,ωα=Γα :αψ , over Υ. Assume that ω,ψ,Υ κ1 = ω,ψ,Υ κ2 .

For each κ1 ,κ2 Υ and αψ, we have

μ Γα κ1 κ=μ Γα κ2 κ  μ Γα κκ1 =μ Γα κκ2 μ Γα κ2 κ1 =μ Γα κ2 κ2 =μ Γα 0 (Takingκ=κ2 )   κ2 κ1 Γα *   κ2 κ1 + Γα * = Γα * κ1 + Γα * =κ2 + Γα *

Conversely, suppose that κ1 + Γα *=κ2 + Γα * for every κ1 ,κ2 Υ, then κ1 κ2 + Γα *= Γα * and κ2 κ1 + Γα *= Γα * . Therefore, κ1 κ2 Γα * and κ2 κ1 Γα * .

Consider,

μ Γα κκ1 =μ Γα κκ2 +κ2 κ1 for eachκΥ min μ Γα κκ2 ,μ Γα κ2 κ1 =min μ Γα κκ2 ,μ Γα 0 =μ Γα κκ2 μ Γα κκ1 μ Γα κκ2

Also,

μ Γα κκ2 =μ Γα κκ1 +κ1 κ2 for eachκΥ min μ Γα κκ1 ,μ Γα κ1 κ2 =min μ Γα κκ1 ,μ Γα 0 =μ Γα κκ1 μ Γα κκ2 μ Γα κκ1

Therefore, μ Γα κκ1 =μ Γα κκ2 for each κΥ.

μ Γα κ1 κ=μ Γα κ2 κ for every αψ. Thus, ω,ψ,Υ κ1 = ω,ψ,Υ κ2 for every κ1 ,κ2 Υ.

Theorem 4.2 Let ω,ψ,Υ be an FSI of Υ. Then S ω,ψ,Υ κ the set of all FSCs of ω,ψ,Υ over Υ, forms a ring with the following binary operations;

ω,ψ,Υ κ1 + ω,ψ,Υ κ2 = ω,ψ,Υ κ1 +κ2 and ω,ψ,Υ κ1 ω,ψ,Υ κ2 = ω,ψ,Υ κ1 κ2 , for every κ1 ,κ2 Υ.

Proof. First, we show that addition and multiplication defined on S ω,ψ,Υ κ are well-defined. For this suppose that ω,ψ,Υ κ1 = ω,ψ,Υ κ2 and ω,ψ,Υ κ3 = ω,ψ,Υ κ4 . Then for every Eqs. (4.1-4.7) κΥ

(4.1)
μ Γα κ1 κ=μ Γα κ2 κ

(4.2)
μ Γα κ3 κ=μ Γα κ4 κ

So,

(4.3)
μ Γα κκ1 =μ Γα κκ2

(4.4)
μ Γα κκ3 =μ Γα κκ4

Putting κ=κ1 +κ3 κ4 in Eq. (4.3) κ=κ3 in Eq. (4.4) and κ=κ1 in Eq. (4.3), we have

(4.5)
μ Γα κ3 κ4 =μ Γα κ1 +κ3 κ4 κ2

(4.6)
μ Γα 0 =μ Γα κ3 κ4

(4.7)
μ Γα 0 =μ Γα κ1 κ2

Now

μ Γα κ1 κ+μ Γα κ3 κ=μ Γα κ1 +κ3 κ=μ Γα κ κ1 +κ3 =μ Γα κκ2 κ4 κ1 κ2 +κ3 κ4 min μ Γα κ κ2 +κ4 ,μ Γα κ1 κ2 +κ3 κ4

min μ Γα κ κ2 +κ4 ,μ Γα 0 by using Eqs. (4.6) and (4.7)

=μ Γα κ κ2 +κ4 =μ Γα κ2 +κ4 κ=μ Γα κ2 κ+μ Γα κ4 κ

So,

(4.8)
μ Γα κ1 κ+μ Γα κ3 κμ Γα κ2 κ+μ Γα κ4 κ

Similarly, we can prove that

(4.9)
μ Γα κ2 κ+μ Γα κ4 κμ Γα κ1 κ+μ Γα κ3 κ

The inequalities in Eqs. (4.8) and (4.9) yield the following:

(4.10)
μ Γα κ1 κ+μ Γα κ3 κ=μ Γα κ2 κ+μ Γα κ4 κ

Furthermore, the application of the similar approach results in

(4.11)
  μ Γα κ1 κ μ Γα κ3 κ=μ Γα κ2 κ μ Γα κ4 κ

The Eqs. (4.10) and (4.11) yield that ω,ψ,Υ κ1 + ω,ψ,Υ κ2 = ω,ψ,Υ κ1 +κ2 and ω,ψ,Υ κ1 ω,ψ,Υ κ2 = ω,ψ,Υ κ1 κ2 holds for every αψ.

Hence, both addition and multiplication defined on ω,ψ,Υ κ are well-defined. Next, ω,ψ,Υ 0 = ω,ψ,Υ acts as zero of ω,ψ,Υ κ and for each ω,ψ,Υ κ2 ω,ψ,Υ κ there exists ω,ψ,Υ κ2 ω,ψ,Υ κ such that ω,ψ,Υ κ2 + ω,ψ,Υ κ2 = ω,ψ,Υ 0 = ω,ψ,Υ κ2 + ω,ψ,Υ κ2 . Associativity and distributivity follow directly from the corresponding properties of the ring Υ.

Hence, S ω,ψ,Υ κ forms a ring under the defined operations.

Example 4.1 Consider an FSI,

ω,ψ,6 = α, 0.95 0 , 0.65 1 , 0.65 2 , 0.95 3 , 0.65 4 , 0.65 5 , β, 0.90 0 , 0.70 1 , 0.70 2 , 0.90 3 , 0.70 4 , 0.70 5 , γ, 0.85 0 , 0.75 1 , 0.75 2 , 0.85 3 , 0.75 4 , 0.75 5 ,

of the ring 6 with respect to the set ψ. We want to find the set S (ω,ψ,6 )κ, which consists of all FSCs of (ω,ψ,6 ) in 6 . Upon calculation, we obtain the set S (ω,ψ,6 )κ={ (ω,ψ,6 ) 0 , (ω,ψ,6 ) 1 , (ω,ψ,6 ) 2 }.

We now examine the Cayley tables shown in Table 1 and Table 2 for the ring (ω,ψ,6 )κ , employing the binary operations defined in Theorem 4.2:

Table 1. Cayley’s table for addition modulo 6.
+ ω,P,6 0 ω,P,6 1 ω,P,6 2
ω,P,6 0 ω,ψ,6 0 ω,ψ,6 1 ω,ψ,6 2
ω,P,6 1 ω,ψ,6 1 ω,ψ,6 2 ω,ψ,6 0
ω,P,6 2 ω,ψ,6 2 ω,ψ,6 0 ω,ψ,6 1

+ : 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.

Table 2. Cayley’s table for multiplication modulo 6.
* ω,ψ,6 0 ω,ψ,6 1 ω,ψ,6 2
ω,ψ,6 0 ω,ψ,6 0 ω,ψ,6 0 ω,ψ,6 0
ω,ψ,6 1 ω,ψ,6 0 ω,ψ,6 1 ω,ψ,6 2
ω,ψ,6 2 ω,ψ,6 0 ω,ψ,6 2 ω,ψ,6 1

* : 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 S (ω,ψ,6 )κ 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 (ω,ψ,6 ) satisfies the conditions of Theorem 4.2, ensuring that the collection of all fuzzy soft cosets S (ω,ψ,6 )κ ​ is well defined. The computation shows that distinct representatives in 6 ​ generate only three distinct fuzzy soft cosets, which reflects the role of the induced crisp ideal (Γα)* 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 ω,ψ,Υ = α,Γα= μ Γα κ κ:κΥ :αψ is an FSI of a ring Υ such that μ Γα κ is constant function, then S ω,ψ,Υ κ= ω,ψ,Υ 0 for every κΥ.

Definition 4.2 Let ω,ψ,Υ = α,Γα= μ Γα κ κ:κΥ :αψ be an FSI of Υ, then the FSI

φ,ψ,S ω,ψ,Υ κ   = α,Kα= μ Kα ω,ψ,Υ κ    ω,ψ,Υ κ   : ω,ψ,Υ κS ω,ψ,Υ κ :αψ

of S (ω,ψ,Υ)κ defined by μ Kα ( (ω,ψ,Υ)κ)=μ Γα  (κ),  for every (ω,ψ,Υ)κS (ω,ψ,Υ)κ , is called fuzzy soft quotient ideal (FSQI) associated with S(ω,ψ,Υ).

Now, we are in a position to prove the classical ring homomorphism theorem in fuzzy soft environment.

Theorem 4.3 If ω,ψ,Υ = α,Γα= μ Γα κ κ:κΥ :αψ is an FSI of a ring Υ, then a mapping θ:ΥS ω,ψ,Υ κ defined by θ κ1 = ω,ψ,Υ κ1 for every κ1 Υ is a ring homomorphism such that Ker θ= ω,ψ,Υ *.

Proof. Let κ1 ,κ2 Υ, then

θ κ1 +κ2 = ω,ψ,Υ κ1 +κ2 = ω,ψ,Υ κ1 + ω,ψ,Υ κ2 =θ κ1 +θ κ2

and

θ κ1 κ2 = ω,ψ,Υ κ1 κ2 = ω,ψ,Υ κ1 ω,ψ,Υ κ2 =θ κ1 θ κ2.

Now, we prove that, for every αψ, μ Γα κ1 =μ Γα 0 if and only if ω,ψ,Υ κ1 = ω,ψ,Υ 0 .

Suppose that μ Γα κ1 =μ Γα 0 . Then, for every κΥ, we have μ Γα κμ Γα κ1 =μ Γα 0 .

Now if μ Γα κ<μ Γα κ1 μ Γα κκ1 =min μ Γα κ,μ Γα κ1 =μ Γα κ .

On the other hand, if μ Γα κ=μ Γα κ1

κ,κ1 κ2 Υ:μ Γα κ2 =μ Γα 0 , for every αψ = ω,ψ,Υ * κκ1 ω,ψ,Υ *μ Γα κκ1 =μ Γα 0 =μ Γα κ.

Hence, in both cases, the equality μ Γα κκ1 =μ Γα κ holds for every κΥ and αψ. Thus, ω,ψ,Υ κ1 = ω,ψ,Υ 0 .

Conversely, suppose that ω,ψ,Υ κ1 = ω,ψ,Υ 0 . So, for every κΥ we have

μ Γα κ1 κ=μ Γα 0 κμ Γα κκ1 =μ Γα κ0

For each κ1 Υ,

μ Γα κ1 =μ Γα κ κκ1 min μ Γα κ,μ Γα κκ1 =μ Γα κ μ Γα κ1 μ Γα κμ Γα κ1 =μ Γα 0

Thus, μ Γα κ1 =μ Γα 0 ω,ψ,Υ κ1 = ω,ψ,Υ 0 .

Consider,

Ker θ= κ1 K:θ κ1 = ω,ψ,Υ 0

= κ1 Υ: ω,ψ,Υ κ1 = ω,ψ,Υ 0 = κ1 Υ:μ Γα κ1 =μ Γα 0  for every αψ = ω,ψ,Υ * .

Example 4.2 Consider an FSI,

ω,ψ,6 = α, 0.95 0 , 0.65 1 , 0.65 2 , 0.95 3 , 0.65 4 , 0.65 5 , β, 0.90 0 , 0.70 1 , 0.70 2 , 0.90 3 , 0.70 4 , 0.70 5 , γ, 0.85 0 , 0.75 1 , 0.75 2 , 0.85 3 , 0.75 4 , 0.75 5 ,

of the ring 6 with respect to the set ψ. We want to find the set S ω,ψ,6 κ , which consists of all FSCs of ω,ψ,6 in 6 . Upon calculation, we obtain the set S ω,ψ,6 κ = ω,ψ,6 0  , ω,ψ,6 1  , ω,ψ,6 2 . Utilizing the approach outlined in Theorem 4.3, we define θ:6 S ω,6 ,ψ κ as follows;

θ 0 =θ 3 = ω,ψ,6 0 ,θ 1 =θ 4 = ω,ψ,6 1 and θ 2 =θ 5 = ω,ψ,6 2

Now, θ 2+3 =θ 5 = ω,ψ,6 2 and θ 2 +θ 3 = ω,ψ,6 2 + ω,ψ,6 0 = ω,ψ,6 2 . This demonstrates that θ 2+3 =θ 2 +θ 3 . Also, θ 2.3 =θ 0 = ω,ψ,6 0 and θ 2 θ 3 = ω,ψ,6 2 ω,ψ,6 0 = ω,ψ,6 0 show that θ 2.3 =θ 2 .θ 3 . S In a similar way, it can be readily confirmed that θ n1 +n2 =θ n1 +θ n2 and θ n1 .n2 =θ n1 .θ n2 for every n1 ,n2 6 . Consequently, θ represents a ring homomorphism. Finally, ω,ψ,6 0 is 0 of ω,ψ,6 κ , therefore Ker θ= x6 :θx= ω,ψ,6 0 0,3 = ω,ψ,6 * 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.

References

  1. . On fuzzy ideals and fuzzy quotient rings of a ring. Fuzzy Sets Syst. 1993;59:205-210. https://doi.org/10.1016/0165-0114(93)90200-2
    [Google Scholar]
  2. , , . Fuzzy semirings. Fuzzy sets and systems. 1993;60:309-320. https://doi.org/10.1016/0165-0114(93)90441-j
    [Google Scholar]
  3. Al-Husban, A., Oudetallah, J., Al-Deiakeh, R., Saidat, S., Alhazayme, K., Al-Hammouri, A., Almousa, M., Banihani, G., Shatnawi, M., 2025. Complex fuzzy subring and complex fuzzy ideal. Proceedings of the 2nd joint international conference on mathematics, statistics and engineering: J-Comse2024 Kedah, Malaysia 040016. https://doi.org/10.1063/5.0295436
  4. , , . On fundamental theorems of fuzzy isomorphism of fuzzy subrings over a certain algebraic product. Symmetry. 2021;13:998. https://doi.org/10.3390/sym13060998
    [Google Scholar]
  5. . Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986;20:87-96. https://doi.org/10.1016/s0165-0114(86)80034-3
    [Google Scholar]
  6. . Some algebraic properties of pythagorean fuzzy bi-ideals. Sinop Üniversitesi Fen Bilimleri Dergisi. 2025;10:42-59.
    [Google Scholar]
  7. , , . On fuzzy rings. Fuzzy sets and systems. 1992;49:205-213. https://doi.org/10.1016/0165-0114(92)90325-x
    [Google Scholar]
  8. , . Similarity-distance decision-making technique and its applications via intuitionistic fuzzy pairs. JCCE. 2023;2:68-74. https://doi.org/10.47852/bonviewjcce512522514
    [Google Scholar]
  9. , . Intuitionistic fuzzy statistical correlation algorithm with applications to multicriteria‐based decision‐making processes. Int J Intell Syst. 2021;36:1386-1407. https://doi.org/10.1002/int.22347
    [Google Scholar]
  10. , . An advanced study on the similarity measures of intuitionistic fuzzy sets based on the set pair analysis theory and their application in decision making. Soft Comput. 2018;22:4959-4970. https://doi.org/10.1007/s00500-018-3202-1
    [Google Scholar]
  11. , . Linguistic interval-valued atanassov intuitionistic fuzzy sets and their applications to group decision making problems. IEEE Trans Fuzzy Syst. 2019;27:2302-2311. https://doi.org/10.1109/tfuzz.2019.2897961
    [Google Scholar]
  12. , , , , . Novel aczel–alsina operators for pythagorean fuzzy sets with application in multi-attribute decision making. Symmetry. 2022;14:940. https://doi.org/10.3390/sym14050940
    [Google Scholar]
  13. , . Fuzzy soft rings and fuzzy soft ideals. Neural Comput Appl. 2012;21(Suppl 1):1-8. https://doi.org/10.1007/s00521-011-0550-5
    [Google Scholar]
  14. , . Correspondence theorem for fuzzy ideals. Fuzzy Syst. 1991;41:213-219. https://doi.org/10.1016/0165-0114(91)90225-f
    [Google Scholar]
  15. . Fuzzy subgroups, fuzzy ideals, and fuzzy cosets: Some properties. Fuzzy Syst. 1992;48:267-274. https://doi.org/10.1016/0165-0114(92)90341-z
    [Google Scholar]
  16. , , . Some novel interactive hybrid weighted aggregation operators with pythagorean fuzzy numbers and their applications to decision making. Mathematics. 2019;7:1150. https://doi.org/10.3390/math7121150
    [Google Scholar]
  17. . Fuzzy invariant subgroups and fuzzy ideals. Fuzzy Syst. 1982;8:133-139. https://doi.org/10.1016/0165-0114(82)90003-3
    [Google Scholar]
  18. , , . Fuzzy soft sets. J Fuzzy Math. 2001;9:589-602. https://doi.org/10.1155/2009/586507
    [Google Scholar]
  19. . Fuzzy ideals of artinian rings. Fuzzy Syst. 1990;37:111-115. https://doi.org/10.1016/0165-0114(90)90069-i
    [Google Scholar]
  20. . Soft set theory—first results. Computers & mathematics with applications. 1999;37:19-31. https://doi.org/10.1016/s0898-1221(99)00056-5
    [Google Scholar]
  21. , . On fuzzy ideals of a ring I. Fuzzy Syst. 1987;21:99-104. https://doi.org/10.1016/0165-0114(87)90155-2
    [Google Scholar]
  22. , , . A Novel approach to decision-making with pythagorean fuzzy information. Mathematics. 2018;6:95. https://doi.org/10.3390/math6060095
    [Google Scholar]
  23. , . On pythagorean fuzzy ideals of a classical ring. MATH. 2023;8:4280-4303. https://doi.org/10.3934/math.2023213
    [Google Scholar]
  24. , , , , . A detailed study of mathematical rings in q-rung orthopair fuzzy framework. Symmetry. 2023;15:697. https://doi.org/10.3390/sym15030697
    [Google Scholar]
  25. , . A fuzzy soft set theoretic approach to decision making problems. J Comput Appl Math. 2007;203:412-418. https://doi.org/10.1016/j.cam.2006.04.008
    [Google Scholar]
  26. , . Group action on fuzzy ideals. Commun Algebra. 1998;26:4207-4220. https://doi.org/10.1080/00927879808826406
    [Google Scholar]
  27. , . Fuzzy prime ideals of rings. J Math Anal Appl. 1988;134:94-103. https://doi.org/10.1016/0022-247x(88)90009-1
    [Google Scholar]
  28. . Operations on fuzzy ideals. Fuzzy Syst. 1983;11:31-39. https://doi.org/10.1016/s0165-0114(83)80067-0
    [Google Scholar]
  29. Yager, R.R., 2013. Pythagorean fuzzy subsets. 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) Edmonton, AB, Canada, 57-61. https://doi.org/10.1109/ifsa-nafips.2013.6608375
  30. . Fuzzy semilattices. Info Sci. 1987;43:155-159. https://doi.org/10.1016/0020-0255(87)90035-1
    [Google Scholar]
  31. . Prime L-fuzzy ideals and primary L-fuzzy ideals. Fuzzy Syst. 1988;27:345-350. https://doi.org/10.1016/0165-0114(88)90060-7
    [Google Scholar]
  32. . Fuzzy sets. Info Control. 1965;8:338-353. https://doi.org/10.1016/s0019-9958(65)90241-x
    [Google Scholar]
  33. , , . A new divergence measure of pythagorean fuzzy sets based on belief function and its application in medical diagnosis. Mathematics. 2020;8:142. https://doi.org/10.3390/math8010142
    [Google Scholar]
Show Sections