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. 2021 Nov 19;37(8):5134–5151. doi: 10.1002/int.22754

Intuitionistic fuzzy set of Γ‐submodules and its application in modeling spread of viral diseases, mutated COVID‐n, via flights

Narjes Firouzkouhi 1, Abbas Amini 2,3,, Chun Cheng 4, Ali Zarrabi 5, Bijan Davvaz 6
PMCID: PMC8653288  PMID: 36226234

Abstract

In this study, we generalize fuzzy Γ‐module, as intuitionistic fuzzy Γ‐submodule of Γ‐module (IFΓM), and utilize it for modeling the spread of coronavirus in air travels. Certain fundamental features of intuitionistic fuzzy Γ‐submodule are provided, and it is proved that IFΓM can be considered as a complete lattice. Some elucidatory examples are demonstrated to explain the properties of IFΓM. The relevance between the upper and lower α‐level cut and intuitionistic fuzzy Γ‐submodules are presented and the characteristics of upper and lower under image and inverse image of IFΓM are acquired. It is verified that the image and inverse image of intuitionistic fuzzy Γ‐submodule are preserved under the module homomorphism. The obtained IFΓM is used to model the aerial transition of viral diseases, that is, COVID‐n, via flights.

Keywords: homomorphism, image and inverse image, intuitionistic fuzzy Γ‐submodule, intuitionistic fuzzy set, level subsets

1. INTRODUCTION

The theory of fuzzy set was established by Zadeh, 1 then Rosenfeld proposed a relation between fuzzy set and group theory and regulated the notion of fuzzy subgroups. 2 Atanassov established the intuitionistic fuzzy set (IFS) that involved basic and fundamental concepts as the extension of fuzzy sets. 3 In fact, the IFS has been beneficial to tackle incomplete and vague information. This theory is more effective as an IFS, related to the degree of nonmembership and membership in a unit interval, while a fuzzy set is associated to the degree of membership of an element in a specified set. Numerous ideas have been developed via IFS theory, for instance, Biwas 4 defined the intuitionistic fuzzy subgroups of a group, and Kim et al. surveyed the intuitionistic fuzzy ideals of semirings. 5 The authors presented the universal coefficient theorem in the category of intuitionistic fuzzy modules. 6 Sharma initiated the concept of t‐intuitionistic fuzzy subgroup, 7 fuzzy quotient group, 8 (α,β)‐cut of intuitionistic fuzzy group, 9 homomorphism of intuitionistic fuzzy group, 10 and direct product of intuitionistic fuzzy group. 11 Jun et al. investigated the quotient structures of intuitionistic fuzzy finite state machines, 12 they also studied the intuitionistic nil radicals of intuitionistic fuzzy ideals and Euclidean intuitionistic fuzzy ideals in rings. 13 Based on the intuitionistic fuzzy implications, Zhou et al. introduced the intuitionistic fuzzy rough sets. 14

Studies on Γ‐related were extended by Nobusawa 15 who characterized Γ‐rings and afterwards Barnes 16 and Luh 17 improved the structure of Γ‐rings. Sen et al. presented the idea of Γ‐semigroup as a generalization of semigroup, after that, Rao defined the idea of Γ‐semiring. 18 The authors introduced the theory of Γ‐semihypergroup and expanded various classical concepts of semigroups. 19 Ameri et al. developed the concept of Γ‐module over a Γ‐ring and extended fuzzy Γ‐hypermodules and fuzzy Γ‐modules. 20 , 21 They also defined a connection between fuzzy Γ‐hypermodules and Γ‐modules through fundamental relations. Another study was done on fuzzy Γ‐hypermodules and fuzzy Γ‐hyperrings to obtain basic results. 22 Other researchers proposed the concept of IFSs in Γ‐semigroups, 23 while Ersoy et al. studied the IFS in the Γ‐semihyperring. 24 The authors extended the Atanassov intuitionistic fuzzy grade of hypergroups, 25 the Atanassov intuitionistic (S,T)‐fuzzy n‐ary subhypergroups and their traits, 26 and the Atanassov intuitionistic fuzzy interior ideals of Γ‐semigroups. 27 Latif et al. explored basic theorems of t‐intuitionistic fuzzy isomorphism of t‐intuitionistic fuzzy subgroups. 28

Gulzar et al. developed some classes of t‐intuitionistic fuzzy subgroups, 29 and then determined the new applications of complex IFSs in group theory. 30 In fact, IFSs are helpful in advanced systems, systems theory, decision making, and so on. Recently, Ejegwa presented the correlation coefficient between IFSs and its applications in real‐life decision‐making problems. 31 Alcantud et al. studied the aggregation of infinite chains of IFSs and their applications with temporal IFSs. 32 Others extended the complex IFS by quaternion numbers along with utilizing them in decision making. 33 Wei et al. defined an information‐based score function of interval‐valued IFSs and its application in multiattribute decision‐making. 34 Also, Tao et al. explored dynamic multicriteria decision making in real life. 35 There are many other potential applications of IFSs in chemistry, mathematics, programming, physics, medicine, and machine learning. Kumar De et al. used the IFSs for medical diagnosis, 36 while the authors proposed the applications of IFS in medicine. 37 Ejewa et al. utilized the IFSs in electoral systems. 38 Mahanta et al. surveyed a novel distance measure with various applications, 39 while others analyzed the measure of width‐based distance on the interval‐valued IFS. 40

The coronavirus disease‐2019 (COVID‐19) pandemic is a serious global crisis that has quickly spread over the world, causing millions of mortalities till date. Although the first cases were reported in China, new cases were identified in all other nations in a short period of time. 41 This viral disease has infected humanity worldwide with typical symptoms of fever, sore throat, cough, fatigue and dyspnea. Despite the capability of some countries on effective vaccination against coronavirus disease, the emergence of new infected cases is unpredictable and seriously worrying, as there is yet neither an adamant treatment against the mutated versions of COVID nor a prohibition methodology against the detrimental/deadly side effects of known vaccines. 42 As such, various countries implemented severe precautions to decelerate the diffusion of this disease after the World Health Organization (WHO) officially publicized the epidemic situation in mid‐March 2019. 43

Due to the COVID‐19 outbreak, many countries have faced case threats through inbound international and national flights. After identifying the first cases of coronavirus in different countries, strict rules were imposed on the airlines that yielded the disruption of global transportation. 44 In fact, to lessen the chances of proliferation of COVID‐19, very strict protocols were issued by governments on aerial sectors. These restrictions included installing high‐efficiency air filters in aircrafts, imposing C‐reactive protein (CRP) tests and vaccinations for travelers, wearing protective masks, and keeping social distances during the aerial trips. 45 While the air travels are considered as an essential transportation service worldwide, the surveillance/modeling of the corresponding global factors (studied here) is necessary to resume safe aerial trips with reduced/controlled COVID threats 46 (Figure 1).

Figure 1.

Figure 1

Impact of COVID‐19 outbreak on flights [Color figure can be viewed at wileyonlinelibrary.com]

The main contribution of this paper is the generalization of fuzzy Γ‐module through the development of IFS, and the construction of new application for the spread of viral diseases, that is, coronavirus, among individuals in air travels. By using Γ‐module, we expand the framework of IFS via the expression of some basic and significant characteristics with certain foundational traits. In Section 3, the intuitionistic fuzzy Γ‐submodule (IFΓM) is established via the notion of Γ‐modules to extend the fuzzy sets. Fundamental properties of intuitionistic fuzzy Γ‐submodule are found, and it is verified that IFΓM can be regarded as a complete lattice. Furthermore, by considering the upper and lower α‐level cut, we express the relationship between them and IFΓM, along with several traits of upper and lower via image and inverse image of IFΓM. It is shown that the image and inverse image of intuitionistic fuzzy Γ‐submodule are preserved under the module homomorphism. In Section 4, the elucidatory examples address the application of IFΓM in the immunological transmission of COVID‐n.

2. PRELIMINARIES

The IFSs are the generalization of the fuzzy sets which were proposed by Atanassov. 3 An IFS A of a nonvoid set X is described by the formation A={t,ϑA(t),ζA(t)tX}, where ϑA:X[0,1] is the degree of membership and ζA:X[0,1] is the degree of nonmembership of the element tX, and we have 0ϑA(t)+ζA(t)1. Note that we will write A=(ϑA,ζA) instead of A={t,ϑA(t),ζA(t)}. Consider ϑc the complement of ϑ which is determined by ϑAc(t)=1ϑA(t). Let A=(ϑA,ζA) and B=(ϑB,ζB) be two IFS of X. Thus, the next statements are introduced tX, as follows:

  • (i)

    ABϑA(t)ϑB(t),ζA(t)ζB(t),

  • (ii)

    Ac=ζA(t),ϑA(t),

  • (iii)

    AB=ϑA(t)ϑB(t),ζA(t)ζB(t),

  • (iv)

    AB=ϑA(t)ϑB(t),ζA(t)ζB(t),

  • (v)

    A=ϑA(t),ϑAc(t),A=ζAc(t),ζA(t).

and if {Ai}iI be arbitary family of IFS in X, where Ai=ϑAi,ζAi, thus

  • (i)

    iIAi=iIϑAi(x),iIζAi(x), that is, the intersection of Ai,

  • (ii)

    iIAi=iIϑAi(x),iIζAi(x), that is, the union of Ai.

Definition 2.1

((Barnes 16 )) Suppose R and Γ be additive abelian groups. R is considered as a Γ‐ring if a mapping exists:

·R×Γ×RR
(r1,α1,r2)r1.α1.r2(=r1α1r2)

so that r1,r2,r3R,α1,α2Γ, the next circumstances hold:

  • (i)

    (r1+r2)α1r3=r1α1r3+r2α1r3;

  • (ii)

    r1(α1+α2)r3=r1α1r3+r1α2r3;

  • (iii)

    r1α1(r2+r3)=r1α1r2+r1α1r3;

  • (iv)

    (r1α1r2)α2r3=r1α1(r2α2r3).

Definition 2.2

((Ameri and Sadeghi) 20 ) Consider R as a Γ‐ring. A left Γ ‐module under R is an additive abelian group M via a map ·R×Γ×MM that (r,γ,m)rγm, so that for all m,m1,m2M and γ,γ1,γ2Γ and r,r1,r2R the next implications are satisfied:

  • (i)
    rγ(m1+m2)=rγm1+rγm2
  • (ii)
    (r1+r2)γm=r1γm+r2γm
  • (iii)
    r(γ1+γ2)m=rγ1m+rγ2m
  • (iv)

    r1γ1(r2γ2m)=(r1γ1r2)γ2m.

A nonvoid subset S of M is considered as left (right) Γ ‐submodule of M provided for any S1,S2S implies S1+S2S and also RΓSS(SΓRS).

3. FUNDAMENTAL FEATURES OF IFS OF Γ‐SUBMODULES

Definition 3.1

A fuzzy left (right) Γ ‐module over a Γ‐ring R is introduced to be a couple (M,ϑ), where, M is a left Γ‐module and function ϑ:M[0,1] that holds the following circumstances:

  • (i)

    ϑ(0)=1,

  • (ii)

    ϑ(x+y)min{ϑ(x),ϑ(y)},

  • (iii)

    ϑ(rγx)ϑ(x)(ϑ(xγr)ϑ(x)).

ϑ is considered as a fuzzy Γ‐module of M supposing ϑ is a fuzzy left Γ‐module and also fuzzy right Γ‐module of M.

Example 3.2

Assume M=Zn for prime integer n, and R=Z and Γ=Z. Define

·Z×Z×ZnZn

with (r,γ,x¯)rγx¯rγx¯, for every rR,γΓ,x¯M, thus M is a Γ‐module under a Γ‐ring R (Figure 2).

Moreover, introduce the fuzzy set ϑ of M as follows:

ϑ(x¯)=1,ifx¯=0¯,13,otherwise,

Thus, ϑ is a fuzzy Γ‐module of M.

Figure 2.

Figure 2

Γ‐module M

Example 3.3

Suppose M=Z and R=Z and Γ be a subring of (Z,+,). Hence, R is a Γ‐ring and (M,+) is an abelian group. Define

·Z×Γ×ZZ

with (r,γ,m)rγmrγm for every rR,γΓ,mM. Therefore, M is a Γ‐module. Now, describe ϑ in the following way:

ϑ(m)=1,ifm=0,35,otherwise.

Hence, ϑ is a fuzzy Γ‐module of M.

Definition 3.4

Assume M be a left Γ‐module under a Γ‐ring. An IFS A=ϑA,ζA of M is described as left intuitionistic fuzzy Γ ‐submodule if for all x,yM,rR,γΓ the next statements is satisfied:

  • (i)

    ϑA(0)=1 and ζA(0)=0,

  • (ii)

    ϑA(x+y)min{ϑA(x),ϑA(y)} and ζA(x+y)max{ζA(x),ζA(y)},

  • (iii)

    ϑA(x)ϑA(rγx) and ζA(x)ζA(rγx).

Denote that IF Γ M is intuitionistic fuzzy Γ‐submodule. Also, it is defined for right Γ‐submodule, the IFS of A=ϑA,ζA of M is considered an IFΓM provided it is left and right IFΓM.

Example 3.5

Assume M=Z and R=Z and Γ=Z. Then, (M,+) is an abelian group and R is a Γ‐ring. Define

·Z×Z×ZZ

written by (r,γ,x)rγx, for every rR,γΓ,xM. Thus, M is a Γ‐module. Describe two fuzzy sets ϑ and ζ of M, in the following way:

ϑA(x)=1,ifx=0,13,otherwise,

and

ζA(x)=0,ifx=0,23,ifx=1,25,otherwise.

Hence, A=ϑA,ζA is an IFΓM of M.

Proposition 3.6

Suppose {Ai}iI be a family of IFΓM. Hence, iIAi and iIAi are IFΓM.

We will verify iIAiIFΓM, and the rest is similar. Let {Ai} be IFΓM for every iI. So, we prove the statements:

  • (i)

    ϑ(iIAi)(0)=ϑA1(0)ϑAn(0)=11=1 and ζ(iIAi)(1)=ζA1(1)ζAn(1)=00=0.

  • (ii)
    ϑ(iIAi)(x+y)=ϑA1(x+y)ϑAn(x+y)(ϑA1(x)ϑA1(y))(ϑAn(x)ϑAn(y))=(ϑA1(x)ϑA1(x))(ϑA1(y))ϑAn(y)=ϑ(iIAi)(x)ϑ(iIAi)(y)
  • (iii)

    ϑ(iIAi)(x)=ϑA1(x)ϑAn(x)ϑA1(rγx)ϑAn(rγx)=ϑ(iIAi)(rγx), and ζ(iIAi)(x)=ζA1(x)ζAn(x)ζA1(rγx)ζAn(rγx)=ζ(iIAi)(rγx).

This completes the proof.  □

Proposition 3.7

Assume M be a Γ‐module under Γ‐ring R. Thus, IF ΓM is a complete lattice under the inclusion .

Assume {Ai}iI be any subset of IFΓM, hence iIAiIFΓM. Evidently, iIAi is the largest intuitionistic fuzzy Γ‐submodule contained in Ai. Therefore, iIAi=iIAi. Also, iIAiIFΓM, and it is the least intuitionistic fuzzy Γ‐submodule containing Ai. So, iIAi=iIAi. It yields that IFΓ M is a complete lattice.  □

Theorem 3.8

If S1 is a Γ‐submodule of M, hence S1˜=χS1,χS1c is an IF Γ M of M.

Assume x,yS1,rR,γΓ. Since S1 is Γ‐submodule, so x+yS1 and rγxS1. We verify the next statements.

  • (i)

    χS1(x)=1 and χS1c(x)=0,

  • (ii)
    χS1(x+y)=1min{χS1(x),χS1(y)}=11=1, and
    χS1c(x+y)=1χS1(x+y)1min{χS1(x),χS1(y)}=max{1χS1(x),1χS1(y)}=max{χS1c(x),χS1c(y)},
  • (iii)

    χS1(rγx)=1χS1(x) and χS1c(rγx)=1χS1(rγx)1χS1(x)=χS1c(x).

Supposing xS1 or yS1, thus χS1(x)=0 or χS1(y)=0. Therefore,

χS1(x+y)0=min{χS1(x),χS1(y)},

and

max{χS1c(x),χS1c(y)}=max{1χS1(x),1χS1(y)}=1minχS1(x),χS1=1χS1c(x+y)

Theorem 3.9

Consider S1 be a nonvoid subset of M. If S1˜=χS1,χS1c is an IF ΓM of M, then S1 is a Γ‐submodule of M.

Assume that S1˜=χS1,χS1c is an IFΓM of M. We should verify for x,yS1,rR,γΓ that x+yS1 and rγxS1. It yields that

χS1(x+y)min{χS1(x),χS1(y)}=11=1

and

χS1c(x+y)max{χS1c(x),χS1c(y)}=01=1

So, χS1(x+y)=1 then, x+yS1. Also, we have

χS1(rγx)χS1(x)=1

and

χS1c(rγx)χS1(x)=0

It means that rγxS1.   □

Proposition 3.10

Assume that A=ϑA,ζA be an IFΓM of M, and 0α1. Introduce an IFS B=ϑB,ζB on M by ϑB(x)=αϑA(x) and ζB(x)=(1α)ζA(x), for all xM. Hence, B=ϑB,ζB is an IFΓM of M.

We have

0ϑB(x)+ζB(x)=αϑA(x)+(1α)ζA(x)1.

  □

Proposition 3.11

Suppose that A=ϑA,ζA be an IFΓM of M. Describe an IFS B=ϑB,ζB on M, by ϑB(x)=(ϑA(x))2 and ζB(x)=1(1ζA(x))2, for all xM. Thus, B=ϑB,ζB is an IFΓS of M.

Consider A=ϑA,ζA be an IFΓM of M. So, we have ϑA(x+y)min{ϑA(x),ϑA(y)}. Then,

(ϑA(x+y))2(min{ϑA(x),ϑA(y)})2=min{(ϑA(x))2,(ϑA(y))2}=min{ϑB(x),ϑB(y)}.

Since ϑA(x)ϑA(rγx), therefore (ϑA(x))2(ϑA(rγx))2, that implies ϑB(x)ϑB(rγx). Also, we have

ζA(x+y)max{ζA(x),ζA(y)}(ζA(x+y))min{(ζA(x)),(ζA(y))}(1ζA(x+y))2(min{1ζA(x),1ζA(y)})2(1ζA(x+y))2min{(1ζA(x))2,(1ζA(y))2}(1ζA(x+y))2max{(1ζA(x))2,(1ζA(y))2}1(1ζA(x+y))2max{1(1ζA(x))2,1(1ζA(y))2}ζB(x+y)max{ζB(x),ζB(y)}.

In addition, we have

ζA(x)ζA(rγx)ζA(x)ζA(rγx)(1ζA(x))2(1ζA(rγx))2(1ζA(x))2(1ζA(rγx))21(1ζA(x))21(1ζA(rγx))2ζB(x)ζB(rγx).

The proof is completed.   □

Theorem 3.12

An IFS A=ϑA,ζA of left (right) Γ‐module M is an IF ΓM if and only if the fuzzy sets ϑA and ζAc are fuzzy left (right) Γ‐module.

Let A=ϑA,ζA be IFΓM of M. By definition, ϑA is left fuzzy Γ‐module. Moreover, for x,yM,γΓ, we attain

  • (i)

    ζAc(0)=1ζA(0)=10=1.

  • (ii)
    ζAc(x+y)=1ζA(x+y)1max{ζA(x),ζA(y)}=min{1ζA(x),1ζA(y)}=min{ζAc(x),ζAc(y)},
  • (iii)
    ζAc(rγx)=1ζA(rγx)1ζA(x)=ζAc(x),

Hence, ζAc is fuzzy left Γ‐module.

On the contrary, assume that the fuzzy sets ϑA and ζAc are fuzzy left (right) Γ‐module. So, ϑ(0)=1,ϑ(x+y)min{ϑ(x),ϑ(y)}, and ϑ(rγx)ϑ(x), for all x,yM,rR,γΓ. Also,

  • (i)

    ζA(0)=1ζAc(0)=11=0,

  • (ii)
    ζA(x+y)=1ζAc(x+y)1min{ζAc(x),ζAc(y)}=max{1ζAc(x),1ζAc(y)}=max{ζA(x),ζA(y)},
  • (iii)

    ζA(rγx)=1ζAc(rγx)1ζAc(x)=ζA(x).

Thus, A=ϑA,ζA is an IFΓM of M.   □

Theorem 3.13

Assume A=ϑA,ζA be IFΓM of M. Hence, A and A are also IFΓM of M.

Suppose A=ϑA,ζA be IFΓM of M. For all x,yM,rR,γΓ, we attain

  • (i)

    ϑ(0)=1,

  • (ii)

    ϑ(x+y)min{ϑ(x),ϑ(y)},

  • (iii)

    ϑ(rγx)ϑ(x)(ϑ(xγr)ϑ(x)).

Therefore, we have

ϑAc(0)=1ϑA(0)=11=0,

and

ϑAc(x+y)=1ϑA(x+y)1min{ϑA(x),ϑA(y)}=max{1ϑA(x),1ϑA(y)}=max{ϑAc(x),ϑAc(y)},

and

ϑAc(rγx)=1ϑA(rγx)1ϑA(x)=ϑAc(x).

It implies that A is an IFΓM of M. Similarly, we can verify for A.   □

Remark 3.14

For a proper IFS of A, we have AAA and AAA, but if A is a fuzzy Γ‐module, then we have A=A=A.

U(ϑA;α) is described as an upper bound α ‐level cut of ϑ, and written by U(ϑA;α)={xMϑA(x)α}, and also L(ϑA;α) is considered as lower bound α ‐level cut of ϑ, and written by L(ϑA;α)={xMϑA(x)α}, for any fuzzy set ϑ of M and α[0,1].

Theorem 3.15

An IFS A of a Γ‐module M is a left (right) IFΓM if and only if for every α,β[0,1], the subsets U(ϑA;α) and L(ζA;β) of M are left (right) Γ‐submodule.

Suppose that A=ϑA,ζA be IFΓM of M. Let x,yU(ϑA;α). Since ϑA(x+y)min{ϑA(x),ϑA(y)}, and ϑA(x)α,ϑA(y)α so we have ϑA(x+y)αα=α, it means that x+yU(ϑA;α). Also, since ϑ(rγx)ϑ(x), and ϑA(x)α, so we have ϑ(rγx)ϑ(x)α, it yields that rγxU(ϑA;α).

Now, assume that x,yL(ζA;β). Since ζA(x+y)max{ζA(x),ζA(y)}, and ζA(x)β,ζA(y)β, so we have ζA(x+y)ββ=β, it follows x+yL(ζA;β). Moreover, since ζ(rγx)ζ(x), and ζA(x)β, so we have ζ(rγx)ζ(x)β, we conclude that rγxL(ζA;β).

On the contrary, assume that the subsets U(ϑA;α) and L(ζA;β) of M are left Γ‐submodule. Let x,yM,γΓ, and ϑA(x)=α0,ϑA(y)=α1,ζA(x)=β0, and ζA(y)=β1, that α0α1 and β0β1. If x,yU(ϑA;α0) and x,yL(ζA;β1), by hypothesis we attain x+yU(ϑA;α0), and x+yL(ζA;β1). Therefore,

α0=min{ϑA(x),ϑA(y)}ϑA(x+y),
β1=max{ζA(x),ζA(y)}ζA(x+y).

Also, rγxU(ϑA;α0), and rγxL(ζA;β1), so we have ϑA(rγx)α0, and ζA(rγx)β1. Thus, ϑA(rγx)ϑA(x), and ζA(rγx)ζA(x). Hence, A=ϑA,ζA is an IFΓM of M.   □

Definition 3.16

Assume that A=ϑA,ζA and B=ϑB,ζB be two IFS of M and M¯. Consider π:MM¯ be a map. Hence, we have

  • (i)
    The image of A under the map π is signified by π(A), that is written π(A)=(ϑπ(A),ζπ(A)), which m¯M¯, we note
    ϑπ(A)(m¯)=mπ1(m¯)ϑA(m),ifπ1(m¯),0,otherwise,
    and
    ζπ(A)(m¯)=mπ1(m¯)ζA(m),ifπ1(m¯),1,otherwise.
  • (ii)
    The inverse image of B is signified by π1(B), that is written π1(B)=(ϑπ1(B),ζπ1(B)), which for mM, we note
    ϑπ1(B)(m)=ϑB(π(m)),ζπ1(B)(m)=ζB(π(m)).

The image and inverse image are depicted in Figure 3.

Figure 3.

Figure 3

Image and inverse image of IFS

Proposition 3.17

Assume M1 and M2 be two Γ‐modules over Γ‐ring R and π:M1M2 be a surjective homomorphism. Suppose A=ϑA,ζA is an IFΓM of M1, thus for every α,β[0,1], we have

  • (i)

    π(U(ϑA;α))=U(ϑπ(A);α),

  • (ii)

    π(L(ζA;β))=L(ζπ(A);β).

We prove (i),

yπ(U(ϑA;α))x0U(ϑA;α);π(x0)=yx0U(ϑA;α);x0π1(y)ϑA(x0)α;x0π1(y)(x0π1(y)ϑA(x0))αϑπ(A)(y)αyU(ϑπ(A);α)

also, we prove (ii) in the following:

yπ(L(ζA;β))x0L(ζA;β);π(x0)=yx0L(ζA;β);x0π1(y)ζA(x0)β;x0π1(y)(x0π1(y)ζA(x0))βζπ(A)(y)βyL(ζπ(A);β).

  □

Proposition 3.18

Suppose M1 and M2 be two Γ‐modules over Γ‐ring R and π:M1M2 be a surjective homomorphism. Assume B=ϑB,ζB be an IFΓM of M2, hence for every α,β[0,1], we have

  • (i)

    π1(U(ϑB;α))=U(ϑπ1(B);α),

  • (ii)

    π1(L(ζB;β))=L(ζπ1(B);β).

We verify (i) in the following:

xπ1(U(ϑB;α))π(x)U(ϑB;α)ϑB(π(x))αϑπ1(B)(x)αxU(ϑπ1(B);α).

Moreover, we prove (ii) as follows:

xπ1(L(ζB;β))π(x)L(ζB;β)ζB(π(x))βζπ1(B)(x)βxL(ζπ1(B);β).

  □

Definition 3.19

Assume M be Γ‐module over R, and M¯ be Γ¯‐module over R¯. If the map π:MM¯ and bijection φ:ΓΓ¯ and ψ:RR¯ exist. (π,φ,ψ) is called a homomorphism of M to M¯, provided for all x,yM,γΓ, we attain

π(x+y)=π(x)+π(y),
π(rγx)=ψ(r)φ(γ)π(x).

Moreover, if π be a bijection, then we call (π,φ,ψ) is an isomorphism.

Theorem 3.20

Assume M be Γ‐module, and M¯ be Γ¯‐module. Let (π,φ,ψ) be homomorphism from M to M¯. Hence,

  • (i)

    if A=ϑA,ζA is an IFΓM of M, thus π(A) is an IFΓ¯M of M¯.

  • (ii)

    if B=ϑB,ζB is an IFΓ¯M of M¯, thus π1(B) is an IFΓM of M.

(i): Since π(A)=(ϑπ(A),ζπ(A)), hence for all xM,γΓ,rR,x,yM¯,γΓ¯,rR¯, we have

ϑπ(A)(x+y)=tπ1(x+y)ϑA(t)π(z)=x+yϑA(z)=π(z)=π(x)+π(y)ϑA(z)=π(z)=π(x+y)ϑA(z)=z=x+yϑA(z)π(x)=x,π(y)=ymin{ϑA(x),ϑA(y)}=min{π(x)=xϑA(x),π(y)=yϑA(y)}=min{ϑπ(A)(x),ϑπ(A)(y)},

Moreover, ϑπ(A)(rγx)=tπ1(rγx)ϑA(t)π(z)=rγxϑA(z)=π(z)=ψ(r)φ(γ)π(x)ϑA(z)=π(z)=π(rγx)ϑA(z)=z=rγxϑA(z)π(x)=xϑA(x)=ϑπ(A)(x). It is straightforward to prove for ζπ(A). Thus, π(A) is an IFΓ¯M of M¯.

The proof of (ii) is analogous to (i).   □

4. APPLICATION OF IFΓM FOR THE SPREAD TREND OF COVID‐n VIA AIR TRAVELS

The application of an IFS on‐submodules is expressed for the diffusion of coronavirus disease 2019 (COVID‐19) via flights. COVID‐19 is the most recent epidemic disease which has affected all over the world yielding nearly 4 million deaths till July 2021. This viral disease was first emerged in Wuhan, China, and quickly spread across the world in a short period of time, entangling all the countries and devastating numerous infrastructures. 41 Air travels have negatively assisted the global epidemic of viral diseases, specifically those highly infectious diseases, that is, COVID‐n. 46 It was reported that after a major flight, there have been some new patients infected with coronavirus. 47 Here, we utilize the developed IFΓM to model the dispersion of coronavirus disease between individuals who traveled to different countries via different airlines. In this transition, we appoint Γ as the set of airlines, R as the set of countries, and M as the set of family members (Figure 4).

Figure 4.

Figure 4

The set R and Γ

Assume Γ be important airlines which operate in different countries. Consider Γ={QatarAirline,DeltaAirline,UnitedAirline} with the operation “+” that is defined as follows:

x+y=The airline which plays a role in disease transmission to xand y

The set Γ with the operation + is shown in Table 1.

Table 1.

Group (Γ,+)

+ Qatar Airline = A Delta Airline = B United Airline = C
Qatar Airline =  A A B C
Delta Airline = B B C A
United Airline = C C A B

Thus, (Γ,+) is an abelian group.

Suppose R be the countries that participated in our model. Let R={China,Canada,USA} and the operation determined in the following manner:

ab=The country which contaminates a and b.

The set R via the operation is given in Table 2.

Table 2.

Ring (R,)

China = 1 Canada = 2 USA = 3
China =  1 1 2 3
Canada = 2 2 1 3
USA = 3 3 2 1

Therefore, (R,) is an abelian group. Now, we introduce the operation “” in the next way:

:R×Γ×RR(r,γ,r)rγr=1

which rγr means the country infected by COVID‐19 in relation with the airlines. Hence, (R,,) is Γ‐ring.

Consider the set M as the family members who travel to countries R with airlines Γ. Let M={Bob,Jack,Sara,Nancy}. Describe the operation “” as follows:

ts=The person who transmits the disease to t and s

In Table 3. (M,) is defined.

Table 3.

Module (M,)

Bob = a Jack = b Sara = c Nancy = d
Bob = a a b c d
Jack = b b a d c
Sara = c c d a b
Nancy = d d c b a

Then, (M,) is the abelian group. Introduce the operation “” for all rR,γΓ,mM, in the following manner:

:R×Γ×MM(r,γ,m)rγm=a

Therefore, (M,,) is Γ‐module over Γ‐ring R.

The IFS A of M is determined as follows.

The degree of membership can be interpreted as a percentage of dependence. Table 4 depicts that the disease transmission power of Bob is more than the others, Jack is in the second rank and so on. To verify that A is IFΓM of M, we pursue the following procedure for all elements of A. For example, ϑA(bd)=ϑA(c)=0.5ϑA(b)ϑA(d)=0.60.5, and ζA(bd)=ζA(c)=0.3ζA(b)ζA(d)=0.40.4. Also, ϑA(rγb)=ϑA(a)=1ϑA(b)=0.6, and ζA(rγb)=ζA(a)=0ζA(b)=0.4. Therefore, an IFS A=ϑA,ζA is IFΓM of M.

Table 4.

Intuitionistic fuzzy set A

A Degree of membership and nonmembership of COVID‐19
Bob = a (1,0)
Jack = b (0.6, 0.4)
Sara = c (0.5,0.3)
Nancy = d (0.5,0.4)

5. CONCLUSION

In this paper, a framework for the IFS associated to Γ‐submodule was constructed to generalize the fuzzy set. Certain features of IFS of Γ‐modules were expressed along with illustrative examples, and a link between upper and lower α‐level cut and intuitionistic fuzzy Γ‐submodules was also presented. By applying the module homomorphism, the image and inverse image of intuitionistic fuzzy Γ‐submodule were preserved under the homomorphism. In addition, the convenient circumstance was carried out to create the t‐IFS of Γ‐modules, (α,β)‐IFS of Γ‐modules, homomorphism and direct product of IFS of Γ‐modules which were the main characteristics of the intuitionistic fuzzy Γ‐submodules. The effective application of this survey was demonstrated in modeling the spread of COVID‐19 via air travels. The results rationalized the immunological case by using the developed intuitionistic fuzzy Γ‐submodules. There is a potential to exploit the capability of IFS of Γ‐subrings and IFS of Γ‐subgroups in other fields.

CONFLICT OF INTERESTS

The authors declare that there are no conflict of interests.

ACKNOWLEDGMENTS

The Australian College of ACK is highly acknowledged for providing the Research Grants No: IRC‐2020/2021‐SOE‐ME‐PR05 and PR06. BioRedner, Toronto, Canada, is acknowledged for providing drawing modules.

Firouzkouhi N, Amini A, Cheng C, Zarrabi A, Davvaz B. Intuitionistic fuzzy set of Γ‐submodules and its application in modelling spread of viral diseases, mutated COVID‐n, via flights. Int J Intell Syst. 2022;37:5134‐5151. 10.1002/int.22754

REFERENCES

  • 1. Zadeh LA. Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh; 1965:394‐432.
  • 2. Rosenfeld A. Fuzzy groups. J Math Anal Appl. 1971;35(3):512‐517. [Google Scholar]
  • 3. Atanassov K. Intuitionistic fuzzy sets. IntJ Bioautomation. 2016;20:1‐6 [Google Scholar]
  • 4. Biswas R. Intuitionistic fuzzy subgroups. Notes on IFS. 1997;3:53‐60. [Google Scholar]
  • 5. Kim KH. Intuitionistic fuzzy ideals of semigroups. Indian J Pure Appl Math. 2002;33(4):443‐449. [Google Scholar]
  • 6. Gunduz A, Cigdem BD. The universal coefficient theorem in the category of intuitionistic fuzzy modules. Utilitas Math. 2010;81:131‐156. [Google Scholar]
  • 7. Sharma PK. t ‐Intuitionistic fuzzy subgroups. Int J Fuzzy Math Syst. 2012;3:233‐243. [Google Scholar]
  • 8. Sharma PK. t ‐Intuitionistic fuzzy quotient group. Adv Fuzzy Math. 2012;7(1):1‐9. [Google Scholar]
  • 9. Sharma PK. (α,β) ‐Cut of intuitionistic fuzzy groups. Int Math Forum. 2011;6(53):2605‐2614. [Google Scholar]
  • 10. Sharma PK. Homomorphism of Intuitionistic fuzzy groups. Int Math Forum. 2011;6(64):3169‐3178. [Google Scholar]
  • 11. Sharma PK. On the direct product of Intuitionistic fuzzy subgroups. Int Math Forum. 2012;7(11):523‐530. [Google Scholar]
  • 12. Jun YB. Quotient structures of intuitionistic fuzzy finite state machines. Inf Sci. 2007;177(22):4977‐4986. [Google Scholar]
  • 13. Jun YB, Ozturk MA, Park CH. Intuitionistic nil radicals of intuitionistic fuzzy ideals and Euclidean intuitionistic fuzzy ideals in rings. Inf Sci. 2007;177(21):4662‐4677. [Google Scholar]
  • 14. Zhou L, Wu WZ, Zhang WX. On characterization of intuitionistic fuzzy rough sets based on intuitionistic fuzzy implicators. Inf Sci. 2009;179(7):883‐898. [Google Scholar]
  • 15. Nobusawa N. On a generalization of the ring theory. Osaka J Math. 1964;1(1):81‐89. [Google Scholar]
  • 16. Barnes W. On the Γ‐rings of Nobusawa. Pac J Math. 1966;18(3):411‐422. [Google Scholar]
  • 17. Luh J. On the theory of simple Γ‐rings. Michigan Math J. 1969;16(1):65‐75. [Google Scholar]
  • 18. Rao MMK. Γ‐semirings. Southeast Asian Bull Math. 1995;19(1):49‐54. [Google Scholar]
  • 19. Heidari D, Dehkordi SO, Davvaz B. Γ‐semihypergroups and their properties. University Politehnica of Bucharest Sci Bull Ser A. 2010;72(1):195‐208. [Google Scholar]
  • 20. Ameri R, Sadeghi R. Gamma modules. Ratio math. 2010;20(1):127‐147. [Google Scholar]
  • 21. Ameri R, Sadeghi R. On fuzzy gamma hypermodules. Ratio Math. 2013;24(1):11‐30. [Google Scholar]
  • 22. Leoreanu‐Fotea V, Zhan J, Leoreanu L. Fuzzy Γ‐hyperrings and fuzzy Γ‐hypermodules. J Intell Fuzzy Syst. 2013;24(3):647‐655. [Google Scholar]
  • 23. Uckun M, Ozturk MA, Jun YB. Intuitionistic fuzzy sets in Γ‐semigroups. Bull Korean Math Soc. 2007;44(2):359‐367. [Google Scholar]
  • 24. Ersoy BA, Davvaz B. Structure of intuitionistic fuzzy sets in Γ ‐semihyperrings. Abstr Appl Anal Hindawi; 2013. 10.1155/2013/560698 [DOI]
  • 25. Cristea I, Davvaz B. Atanassovas intuitionistic fuzzy grade of hypergroups. Inf Sci. 2010;180(8):1506‐1517. [Google Scholar]
  • 26. Davvaz B, Corsini P, Leoreanu‐Fotea V. Atanassovs intuitionistic (S,T)‐fuzzy n‐ary subhypergroups and their properties. Inf Sci. 2009;179(5):654‐666. [Google Scholar]
  • 27. Davvaz B, Majumder SK. Atanassov's intuitionistic fuzzy interior ideals of Γ‐semigroups. Sci Bull A. 2011;73(3):45‐60. [Google Scholar]
  • 28. Latif L, Shuaib U, Alolaiyan H, Razaq A. On fundamental theorems of t ‐intuitionistic fuzzy isomorphism of t ‐intuitionistic fuzzy subgroups. IEEE Access. 2018;6:74547‐74556. [Google Scholar]
  • 29. Gulzar M, Alghazzawi D, Mateen MH, Kausar N. A certain class of t‐intuitionistic fuzzy subgroups. IEEE Access. 2020;8:163260‐163268. [Google Scholar]
  • 30. Gulzar M, Mateen MH, Alghazzawi D, Kausar N. A novel applications of complex intuitionistic fuzzy sets in group theory. IEEE Access. 2020;8:196075‐196085. [Google Scholar]
  • 31. Ejegwa PA. An improved correlation coefficient between intuitionistic fuzzy sets and its applications to real‐life decision‐making problems. Notes IFS. 2010;26(2):1‐14. [Google Scholar]
  • 32. Alcantud JCR, Khameneh AZ, Kilicman A. Aggregation of infinite chains of intuitionistic fuzzy sets and their application to choices with temporal intuitionistic fuzzy information. Inf Sci. 2020;514:106‐117. [Google Scholar]
  • 33. Ngan RT, Ali M, Tamir DE, Rishe ND, Kandel A. Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making. Appl Soft Comput. 2020;87:105961. [Google Scholar]
  • 34. Wei AP, Li DF, Lin PP, Jiang BQ. An information‐based score function of interval‐valued intuitionistic fuzzy sets and its application in multiattribute decision making. Soft Comput. 2021;25(3):1913‐1923. [Google Scholar]
  • 35. Tao R, Liu Z, Cai R, Cheong KH. A dynamic group MCDM model with intuitionistic fuzzy set: Perspective of alternative queuing method. Inf Sci. 2021;555:85‐103. [Google Scholar]
  • 36. De SK, Biswas R, Roy AR. An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst. 2001;117(2):209‐213. [Google Scholar]
  • 37. Davvaz B, Sadrabadi EH. An application of intuitionistic fuzzy sets in medicine. Int J Biomath. 2016;9(3):1650037. 10.1142/S1793524516500376 [DOI] [Google Scholar]
  • 38. Ejegwa PA, Tyoakaa GU, Ayenge AM. Application of intuitionistic fuzzy sets in electoral system. Int J Fuzzy Math Arch. 2016;10(1):35‐41. [Google Scholar]
  • 39. Mahanta J, Panda S. A novel distance measure for intuitionistic fuzzy sets with diverse applications. Int J Intell Syst. 2021;36(2):615‐627. [Google Scholar]
  • 40. Li X, Suo C, Li Y. Width‐based distance measures on interval‐valued intuitionistic fuzzy sets. J Intell Fuzzy Syst. 2021. ​:1‐13. 10.3233/JIFS-200889 [DOI] [Google Scholar]
  • 41. Cascella M, Rajnik M, Aleem A, Dulebohn S, DiNapoli R. Features, evaluation, and treatment of coronavirus (COVID‐19). Uniformed Services University of The Health Sciences. 2021.
  • 42. Stasi C, Fallani S, Voller F, Silvestri C. Treatment for COVID‐19: an overview. Eur J Pharmacol. 2020. ​:173644. 10.1016/j.ejphar.2020.173644 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Gavriatopoulou M, Ntanasis‐Stathopoulos I, Fotiou D, et al. Emerging treatment strategies for COVID‐19 infection. Clin Exp Med. 2021;21(2):167‐179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Blomquist PB, Bolt H, Packer S, et al. Risk of symptomatic COVID‐19 due to aircraft transmission: a retrospective cohort study of contact‐traced flights during Englands containment phase. Influenza Other Respirat Viruses. 2021;15(3):336‐344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Zhang L, Yang H, Wang K, Zhan Y, Bian L. Measuring imported case risk of COVID‐19 from inbound international flights—a case study on China. J Air Transp Manage. 2020;89:101918. 10.1016/j.jairtraman.2020.101918 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Murphy N, Boland M, Bambury N, et al. A large national outbreak of COVID‐19 linked to air travel, Ireland, summer 2020. Eurosurveillance. 2020;25(42):2001624. 10.2807/1560-7917.ES.2020.25.42.2001624 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Yang N, Shen Y, Shi C, et al. In‐flight transmission cluster of COVID‐19: a retrospective case series. Infect Dis. 2020;52(12):891‐901. [DOI] [PubMed] [Google Scholar]

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