Research article Special Issues

A study of quadratic Diophantine fuzzy sets with structural properties and their application in face mask detection during COVID-19

  • Received: 25 January 2023 Revised: 11 March 2023 Accepted: 21 March 2023 Published: 19 April 2023
  • MSC : 03E72, 90B50, 94D05

  • During the COVID-19 pandemic, identifying face masks with artificial intelligence was a crucial challenge for decision support systems. To address this challenge, we propose a quadratic Diophantine fuzzy decision-making model to rank artificial intelligence techniques for detecting masks, aiming to prevent the global spread of the disease. Our paper introduces the innovative concept of quadratic Diophantine fuzzy sets (QDFSs), which are advanced tools for modeling the uncertainty inherent in a given phenomenon. We investigate the structural properties of QDFSs and demonstrate that they generalize various fuzzy sets. In addition, we introduce essential algebraic operations, set-theoretical operations, and aggregation operators. Finally, we present a numerical case study that applies our proposed algorithms to select a unique face mask detection method and evaluate the effectiveness of our techniques. Our findings demonstrate the viability of our mask identification methodology during the COVID-19 outbreak.

    Citation: Muhammad Danish Zia, Esmail Hassan Abdullatif Al-Sabri, Faisal Yousafzai, Murad-ul-Islam Khan, Rashad Ismail, Mohammed M. Khalaf. A study of quadratic Diophantine fuzzy sets with structural properties and their application in face mask detection during COVID-19[J]. AIMS Mathematics, 2023, 8(6): 14449-14474. doi: 10.3934/math.2023738

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  • During the COVID-19 pandemic, identifying face masks with artificial intelligence was a crucial challenge for decision support systems. To address this challenge, we propose a quadratic Diophantine fuzzy decision-making model to rank artificial intelligence techniques for detecting masks, aiming to prevent the global spread of the disease. Our paper introduces the innovative concept of quadratic Diophantine fuzzy sets (QDFSs), which are advanced tools for modeling the uncertainty inherent in a given phenomenon. We investigate the structural properties of QDFSs and demonstrate that they generalize various fuzzy sets. In addition, we introduce essential algebraic operations, set-theoretical operations, and aggregation operators. Finally, we present a numerical case study that applies our proposed algorithms to select a unique face mask detection method and evaluate the effectiveness of our techniques. Our findings demonstrate the viability of our mask identification methodology during the COVID-19 outbreak.



    The convexity of function is a classical concept, since it plays a fundamental role in mathematical programming theory, game theory, mathematical economics, variational science, optimal control theory and other fields, a new branch of mathematics, convex analysis, appeared in the 1960s. However, it has been noticed that the functions encountered in a large number of theoretical and practical problems in economics are not classical convex functions, therefore, in the past decades, the generalization of function convexity has attracted the attention of many scholars and aroused great interest, such as h-convex functions [1,2,3,4,5], log-convex functions [6,7,8,9,10], log-h-convex functions [11], and especially for coordinated convex [12]. Since 2001, various extensions and generalizations of integral inequalities for coordinated convex functions have been established in [12,13,14,15,16,17].

    On the other hand, calculation error has always been a troublesome problem in numerical analysis. In many problems, it is often to speculate the accuracy of calculation results or use high-precision operation as far as possible to ensure the accuracy of the results, because the accumulation of calculation errors may make the calculation results meaningless, interval analysis as a new important tool to solve uncertainty problems has attracted much attention and also has yielded fruitful results, we refer the reader to the papers [18,19]. It is worth notion that in recent decades, many authors have combined integral inequalities with interval-valued functions(IVFs) and obtained many excellent conclusions. In [20], Costa gave Opial-type inequalities for IVFs. In [21,22], Chalco-Cano investigated Ostrowski type inequalities for IVFs by using generalized Hukuhara derivative. In [23], Román-Flores derived the Minkowski type inequalities and Beckenbach's type inequalities for IVFs. Very recently, Zhao [5,24] established the Hermite-Hadamard type inequalities for interval-valued coordinated functions.

    Motivated by these results, in the present paper, we introduce the concept of coordinated log-h-convex for IVFs, and then present some new Jensen type inequalities and Hermite-Hadamard type inequalities for interval-valued coordinated functions. Also, we give some examples to illustrate our main results.

    Let RI the collection of all closed and bounded intervals of R. We useR+IandR+ to represent the set of all positive intervals and the family of all positive real numbers respectively. The collection of all Riemann integrable real-valued functions on [a,b], IVFs on [a,b] and IVFs on =[a,b]×[c,d] are denoted by R([a,b]), IR([a,b]) and ID(). For more conceptions on IVFs, see [4,25]. Moreover, we have

    Theorem 1. [4] Let f:[a,b]RI such that f=[f_,¯f]. Then fIR([a,b]) iff f_, ¯fR([a,b]) and

    (IR)baf(x)dx=[(R)baf_(x)dx,(R)ba¯f(x)dx].

    Theorem 2. [25] Let F:RI. If FID(), then

    (ID)F(x,y)dxdy=(IR)badx(IR)dcF(x,y)dy.

    Definition 1. [26] Let h:[0,1]R+. We say that f:[a,b]R+I is interval log-h-convex function or that fSX(log-h,[a,b],R+I), if for all x,y[a,b] and ϑ[0,1], we have

    f(ϑx+(1ϑ)y)[f(x)]h(ϑ)[f(y)]h(1ϑ).

    h is called supermultiplicative if

    h(ϑτ)h(ϑ)h(τ) (2.1)

    for all ϑ,τ[0,1]. If "" in (2.1) is replaced with "", then h is called submultiplicative.

    Theorem 3. [26] Let F:[a,b]R+I,h(12)0. If FSX(log-h,[a,b],R+I) and FIR([a,b]), then

    F(a+b2)12h(12)exp[1babalnF(x)dx][F(a)F(b)]10h(ϑ)dϑ. (2.2)

    Theorem 4. [27] Let F:[a,b]R+I,h(12)0. If FSX(log-h,[a,b],R+I) and FIR([a,b]), then

    [F(a+b2)]14h2(12)[F(3a+b4)F(a+3b4)]14h(12)(baF(x)dx)1ba[F(a)F(b)F2(a+b2)]1210h(ϑ)dϑ[F(a)F(b)][12+h(12)]10h(ϑ)dϑ. (2.3)

    In this section, we define the coordinated log-h-convex for IVFs and prove some new Jensen type inequalities and Hermite-Hadamard type inequalities by using this new definition.

    Definition 2. Let h:[0,1]R+. Then F:R+I is called a coordinated log-h-convex IVFs on if the partial mappings

    Fy:[a,b]R+I,Fy(x)=F(x,y),Fx:[c,d]R+I,Fx(y)=F(x,y)

    are log-h-convex for all y[c,d] and x[a,b]. Then the set of all coordinated log-h-convex IVFs on is denoted by SX(log-ch,,R+I).

    Definition 3. Let h:[0,1]R+. Then F:R+ is called a coordinated log-h-convex function in if for any (x1,y1),(x2,y2) and ϑ[0,1] we have

    F(ϑx1+(1ϑ)x2,ϑy1+(1ϑ)y2)[F(x1,y1)]h(ϑ)[F(x2,y2)]h(1ϑ). (3.1)

    The set of all log-h-convex functions in is denoted by SX(log-h,,R+). If inequality (3.1) is reversed, then F is said to be a coordinated log-h-concave function, the set of all log-h-concave functions in is denoted by SV(log-h,,R+).

    Definition 4. Let h:[0,1]R+. Then F:R+I is called a coordinated log-h-convex IVF in if for any (x1,y1),(x2,y2) and ϑ[0,1] we have

    F(ϑx1+(1ϑ)x2,ϑy1+(1ϑ)y2)[F(x1,y1)]h(ϑ)[F(x2,y2)]h(1ϑ).

    The set of all log-h-convex IVFs in is denoted by SX(log-h,,R+I).

    Theorem 5. Let F:R+I such that F=[F_,¯F]. If FSX(log-h,,R+I) iff F_SX(log-h,,R+) and ¯FSV(log-h,,R+).

    Proof. The proof is completed by combining the Definitions 3 and 4 above and the Theorem 3.7 of [4].

    Theorem 6. If FSX(log-h,,R+I), then FSX(log-ch,,R+I).

    Proof. Assume that FSX(log-h,,R+I). Let Fx:[c,d]R+I,Fx(y)=F(x,y). Then for all ϑ[0,1] and y1,y2[c,d], we have

    Fx(ϑy1+(1ϑ)y2)=F(x,ϑy1+(1ϑ)y2)F(ϑx+(1ϑ)x,ϑy1+(1ϑ)y2)[F(x,y1)]h(ϑ)[F(x,y2)]h(1ϑ)=[Fx(y1)]h(ϑ)[Fx(y2)]h(1ϑ).

    Hence Fx(y)=F(x,y) is log-h-convex on [c,d]. The fact that Fy(x)=F(x,y) is log-h-convex on [a,b] goes likewise.

    Remark 1. The converse of Theorem 6 is not generally true. Let h(ϑ)=ϑ and ϑ[0,1], 1=[π4,π2]×[π4,π2], and F:1R+I be defined:

    F(x,y)=[esinxsiny,64xy].

    Obviously, we have that FSX(log-ch,1,R+I) and FSX(log-h,1,R+I). Indeed, if (π4,π2),(π2,π4)1, we have

    F(ϑπ4+(1ϑ)π2,ϑπ2+(1ϑ)π4)=[esinϑπ4sin(1ϑ)π2,8π2ϑ(1ϑ)],(F(π4,π2))h(ϑ)(F(π2,π4))h(1ϑ)=[e(122)ϑ1,2ϑ+1π].

    If ϑ=0, then

    [0,1e][1e,2π].

    Thus, FSX(log-h,1,R+I).

    In the following, Jensen type inequalities for coordinated log-h-convex functions in is considered.

    Theorem 7. Let piR+,xi[a,b],yi[c,d],(i=1,2,...,n),F:R+. If h is a nonnegative supermultiplicative function and FSX(log-h,,R+), then

    F(1Pnni=1pixi,1Pnni=1piyi)ni=1[F(xi,yi)]h(piPn), (3.2)

    where Pn=ni=1pi. If h is a nonnegative submultiplicative function and FSV(log-h,,R+), then (3.2) is reversed.

    Proof. If n=2, then from Definition 3, we have

    F(p1P2x1+p2P2x2,p1P2y1+p2P2y2)[F(x1,y1)]h(p1P2)[F(x2,y2)]h(p2P2).

    Suppose (3.2) holds for n=k, then

    F(1Pkki=1pixi,1Pkki=1piyi)ki=1[F(xi,yi)]h(piPk).

    Now, let us prove that (3.2) is valid when n=k+1,

    F(1Pk+1k+1i=1pixi,1Pk+1k+1i=1piyi)=F(1Pk+1k1i=1pixi+pk+pk+1Pk+1(pkxkpk+pk+1+pk+1xk+1pk+pk+1),1Pk+1k1i=1piyi+pk+pk+1Pk+1(pkykpk+pk+1+pk+1yk+1pk+pk+1))[F(pkxkpk+pk+1+pk+1xk+1pk+pk+1,pkykpk+pk+1+pk+1yk+1pk+pk+1)]h(pk+pk+1Pk+1)k1i=1[F(xi,yi)]h(piPk+1)([F(xk,yk)]h(pkpk+pk+1)[F(xk+1,yk+1)]h(pk+1pk+pk+1))h(pk+pk+1Pk+1)k1i=1[F(xi,yi)]h(piPk+1)[F(xk,yk)]h(pkPk+1)[F(xk+1,yk+1)]h(pk+1Pk+1)k1i=1[F(xi,yi)]h(piPk+1)=k+1i=1[F(xi,yi)]h(piPk+1).

    This completes the proof.

    Remark 2. If h(ϑ)=ϑ, then the inequality (3.2) is the Jensen inequality for log-convex functions.

    Now, we prove the Jensen inequality for log-h-convex IVFs in .

    Theorem 8. Let piR+,xi[a,b],yi[c,d],i=1,2,...,n,F:R+I such that F=[F_,¯F]. If h is a nonnegative supermultiplicative function and FSX(log-h,,R+I), then

    F(1Pnni=1pixi,1Pnni=1piyi)ni=1[F(xi,yi)]h(piPn), (3.3)

    where Pn=ni=1pi. If FSV(log-h,,R+I), then (3.3) is reversed.

    Proof. By Theorem 5 and Theorem 7, we have

    F_(1Pnni=1pixi,1Pnni=1piyi)ni=1[F_(xi,yi)]h(piPn)

    and

    ¯F(1Pnni=1pixi,1Pnni=1piyi)ni=1[¯F(xi,yi)]h(piPn).

    Thus,

    F(1Pnni=1pixi,1Pnni=1piyi)=[F_(1Pnni=1pixi,1Pnni=1piyi),¯F(1Pnni=1pixi,1Pnni=1piyi)][ni=1[F_(xi,yi)]h(piPn),ni=1[¯F(xi,yi)]h(piPn)]=ni=1[F(xi,yi)]h(piPn).

    This completes the proof.

    Next, we prove the Hermite-Hadamard type inequalities for coordinated log-h-convex IVFs.

    Theorem 9. Let F:R+I and h:[0,1]R+ be continuous. If FSX(log-ch,,R+I), then

    [F(a+b2,c+d2)]14h2(12)exp[14h(12)(12h(12)(ba)balnF(x,c+d2)dx+12h(12)(dc)dclnF(a+b2,y)dy)]exp[1(ba)(dc)badclnF(x,y)dxdy]exp[1210h(ϑ)dϑ(1babalnF(x,c)dx+1abalnF(x,d)dx+1dcdclnF(a,y)dy+1dcdclnF(b,y)dy)][F(a,c)F(a,d)F(b,c)F(b,d)](10h(ϑ)dϑ)2. (3.4)

    Proof. Since FSX(log-ch,,R+I), we have

    Fx(c+d2)=Fx(ϑc+(1ϑ)d+(1ϑ)c+ϑd2)[Fx(ϑc+(1ϑ)d)]h(12)[Fx((1ϑ)c+ϑd)]h(12).

    That is,

    lnFx(c+d2)h(12)ln[Fx(ϑc+(1ϑ)d)Fx((1ϑ)c+ϑd)].

    Moreover, we have

    1h(12)lnFx(c+d2)[10lnFx(ϑc+(1ϑ)d)dϑ+10lnFx((1ϑ)c+ϑd)dϑ]=[10lnF_x(ϑc+(1ϑ)d)dϑ,10ln¯Fx(ϑc+(1ϑ)d)dϑ]+[10lnF_x((1ϑ)c+ϑd)dϑ,10ln¯Fx((1ϑ)c+ϑd)dϑ]=2[1dcdclnF_x(y)dy,1dcdcln¯Fx(y)dy]=2dcdclnFx(y)dy.

    Similarly, we get

    1dcdclnFx(y)dyln[Fx(c)Fx(d)]10h(ϑ)dϑ.

    Then

    12h(12)lnFx(c+d2)1dcdclnFx(y)dyln[Fx(c)Fx(d)]10h(ϑ)dϑ.

    That is,

    12h(12)lnF(x,c+d2)1dcdclnF(x,y)dyln[F(x,c)F(x,d)]10h(ϑ)dϑ.

    Integrating over [a,b], we have

    12h(12)(ba)balnF(x,c+d2)dx1(ba)(dc)badclnF(x,y)dxdy[1babalnF(x,c)dx+1babalnF(x,d)dx]10h(ϑ)dϑ.

    Similarly, we have

    12h(12)(dc)dclnF(a+b2,y)dy1(ba)(dc)badclnF(x,y)dxdy[1dcdclnF(a,y)dy+1dcdclnF(b,y)dy]10h(ϑ)dϑ.

    Finally, we obtain

    14h2(12)lnF(a+b2,c+d2)=14h(12)[12h(12)(ba)balnF(x,c+d2)dx+12h(12)(dc)dclnF(a+b2,y)dy]1(ba)(dc)badclnF(x,y)dxdy1210h(ϑ)dϑ[1babalnF(x,c)dx+1babalnF(x,d)dx+1dcdclnF(a,y)dy+1dcdclnF(b,y)dy]12(10h(ϑ)dϑ)2[lnF(a,c)+lnF(a,d)+lnF(b,c)+lnF(b,d)+lnF(a,c)+lnF(a,d)+lnF(b,c)+lnF(b,d)](10h(ϑ)dϑ)2[lnF(a,c)F(a,d)F(b,c)F(b,d)].

    This concludes the proof.

    Remark 3. If F_=¯F and h(ϑ)=ϑ, then Theorem 9 reduces to Corollary 3.1 of [13].

    Example 1. Let [a,b]=[c,d]=[2,3],h(ϑ)=ϑ. We define F:[2,3]×[2,3]R+I by

    F(x,y)=[1xy,ex+y].

    From Definition 2, F(x,y)SX(log-ch,,R+I).

    Since

    [F(a+b2,c+d2)]14h2(12)=[425,e10],exp[14h(12)(12h(12)(ba)balnF(x,c+d2)dx+12h(12)(dc)dclnF(a+b2,y)dy)]=[8e135,e102+23423],exp[1(ba)(dc)badclnF(x,y)dxdy]=[16e2729,e43(3322)],exp[1210h(ϑ)dϑ(1babalnF(x,c)dx+1babalnF(x,d)dx+1dcdclnF(a,y)dy+1dcdclnF(b,y)dy)]=[26e81,e153526],

    and

    [F(a,c)F(a,d)F(b,c)F(b,d)](10h(ϑ)dϑ)2=[16,e2+3].

    It follows that

    [425,e10][8e135,e102+23423][16e2729,e43(3322)][26e81,e153526][16,e2+3]

    and Theorem 9 is verified.

    Theorem 10. Let F:R+I and h:[0,1]R+ be continuous. If FSX(log-ch,,R+I), then

    [F(a+b2,c+d2)]14h3(12)exp[14h2(12)(ba)baln(F(x,c+d2))dx+14h2(12)(dc)dcln(F(a+b2,y))dy]exp[14h(12)(ba)baln(F(x,3c+d4)F(x,c+3d4))dx+14h(12)(dc)dcln(F(3a+b4,y)F(a+3b4,y))dy]exp[2(ba)(dc)badclnF(x,y)dxdy] (3.5)
    exp[12(ba)baln(F(x,c)F(x,d)F2(x,fracc+d2))dx10h(ϑ)dϑ+12(dc)dcln(F(a,y)F(b,y)F2(a+b2,y))dy10h(ϑ)dϑ]exp[(12+h(12))1babaln[F(x,c)F(x,d)]dx10h(ϑ)dϑ+(12+h(12))1dcdcln[F(a,y)F(b,y)]dy10h(ϑ)dϑ][F(a,c)F(a,d)F(b,c)F(b,d)F(a+b2,c)F(a+b2,d)×F(a,c+d2)F(b,c+d2)][12+h(12)](10h(ϑ)dϑ)2[F(a,c)F(a,d)F(b,c)F(b,d)]2[12+h(12)]2(10h(ϑ)dϑ)2.

    Proof. Since FSX(log-ch,,R+I), by using Theorem 6 and (2.3), we have

    14h2(12)ln[Fy(a+b2)]14h(12)ln[Fy(3a+b4)Fy(a+3b4)]1babalnFy(x)dx12ln[Fy(a)Fy(b)F2y(a+b2)]10h(ϑ)dϑ[12+h(12)]ln[Fy(a)Fy(b)]10h(ϑ)dϑ.

    That is,

    14h2(12)ln[F(a+b2,y)]14h(12)ln[F(3a+b4,y)F(a+3b4,y)]1babalnF(x,y)dx12ln[F(a,y)F(b,y)F2(a+b2,y)]10h(ϑ)dϑ[12+h(12)]ln[F(a,y)F(b,y)]10h(ϑ)dϑ.

    Moreover, we have

    14h2(12)(dc)dcln[F(a+b2,y)]dy14h(12)(dc)dcln[F(3a+b4,y)F(a+3b4,y)]dy1(ba)(dc)badclnF(x,y)dxdy12(dc)dcln[F(a,y)F(b,y)F2(a+b2,y)]dy10h(ϑ)dϑ[12+h(12)]1dcdcln[F(a,y)F(b,y)]dy10h(ϑ)dϑ.

    Similarly, we have

    We also from (2.2),

    12h(12)lnF(a+b2,c+d2)1babalnF(x,c+d2)dx,12h(12)lnF(a+b2,c+d2)1dcdclnF(a+b2,y)dy.

    Again from (2.3),

    1babalnF(x,c)dx12ln[F(a,c)F(b,c)F2(a+b2,c)]10h(ϑ)dϑ[12+h(12)]ln[F(a,c)F(b,c)]10h(ϑ)dϑ,1babalnF(x,d)ds12ln[F(a,d)F(b,d)F2(a+b2,d)]10h(ϑ)dϑ[12+h(12)]ln[F(a,d)F(b,d)]10h(ϑ)dϑ,1dcdclnF(a,y)dy12ln[F(a,c)F(a,d)F2(a,c+d2)]10h(ϑ)dϑ[12+h(12)]ln[F(a,c)F(a,d)]10h(ϑ)dϑ,1dcdclnF(b,y)dy12ln[F(b,c)F(b,d)F2(b,c+d2)]10h(ϑ)dϑ[12+h(12)]ln[F(b,c)F(b,d)]10h(ϑ)dϑ

    and proof is completed.

    Example 2. Furthermore, by Example 1, we have

    and

    [F(a,c)F(a,d)F(b,c)F(b,d)]2[12+h(12)]2(10h(θ)dθ)2=[136,e23+22].

    It follows that

    [16625,e210][64e218225,e4(3322)+3103][256e272171,e4(3322)3+3+112][256e4531441,e8(3322)3][166e210935,e12382+3106][8e22187,e153523][690,e33+32+102][136,e23+22]

    and Theorem 10 is verified.

    We introduced the coordinated log-h-convexity for interval-valued functions, some Jensen type inequalities and Hermite-Hadamard type inequalities are proved. Our results generalize some known inequalities and will be useful in developing the theory of interval integral inequalities and interval convex analysis. The next step in the research direction investigated inequalities for fuzzy-interval-valued functions, and some applications in interval nonlinear programming.

    The first author was supported in part by the Key Projects of Educational Commission of Hubei Province of China (D20192501), the Natural Science Foundation of Jiangsu Province (BK20180500) and the National Key Research and Development Program of China (2018YFC1508100).

    The authors declare no conflict of interest.



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