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Research article Special Issues

Algorithmic generation of imprecise data from uniform and Weibull distributions

  • Received: 03 March 2024 Revised: 23 March 2024 Accepted: 29 March 2024 Published: 08 April 2024
  • MSC : 62A86

  • This paper introduced the neutrosophic uniform distribution and innovative simulation methods to generate random numbers from the neutrosophic uniform distribution and the neutrosophic Weibull distribution. We introduced simulation methods and algorithms designed to handle indeterminacy for both of these distributions. We provided random numbers generated from both distributions across a range of parameter values and degrees of indeterminacy. Furthermore, we conducted a comparative analysis between the classical simulation method in classical statistics and the neutrosophic simulation method. Our findings reveal that the proposed neutrosophic simulation method generates random numbers of smaller magnitudes compared to the classical simulation method under classical statistics. This observation forms the basis of our conclusion.

    Citation: Muhammad Aslam, Osama H. Arif. Algorithmic generation of imprecise data from uniform and Weibull distributions[J]. AIMS Mathematics, 2024, 9(5): 13087-13101. doi: 10.3934/math.2024639

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  • This paper introduced the neutrosophic uniform distribution and innovative simulation methods to generate random numbers from the neutrosophic uniform distribution and the neutrosophic Weibull distribution. We introduced simulation methods and algorithms designed to handle indeterminacy for both of these distributions. We provided random numbers generated from both distributions across a range of parameter values and degrees of indeterminacy. Furthermore, we conducted a comparative analysis between the classical simulation method in classical statistics and the neutrosophic simulation method. Our findings reveal that the proposed neutrosophic simulation method generates random numbers of smaller magnitudes compared to the classical simulation method under classical statistics. This observation forms the basis of our conclusion.



    Fractional calculus deals with the equations which involve integrals and derivatives of fractional orders. The history of fractional calculus begins from the history of calculus. The role of fractional integral operators is very vital in the applications of this subject in other fields. Several well known phenomenas and their solutions are presented in fractional calculus which can not be studied in ordinary calculus. Inequalities are useful tools in mathematical modelling of real world problems, they also appear as constraints to initial/boundary value problems. Fractional integral/derivative inequalities are of great importance in the study of fractional differential models and fractional dynamical systems. In recent years study of fractional integral/derivative inequalities accelerate very fastly. Many well known classical inequalities have been generalized by using classical and newly defined integral operators in fractional calculus. For some recent work on fractional integral inequalities we refer the readers to [1,2,3,4,5,6] and references therein.

    Our goal in this paper is to apply generalize Riemann-Liouville fractional integrals using a monotonically increasing function. The Hadamard inequalities are proved for these integral operators using strongly (α,m)-convex functions. Also error bounds of well known Hadamard inequalities are obtained by using two fractional integral identities. In connection with the results of this paper, we give generalizations and refinements of some well known results added recently in the literature of mathematical inequalities.

    Next, we like to give some definitions and established results which are necessary and directly associated with the findings of this paper.

    Definition 1. [7] A function f:[0,+)R is said to be strongly (α,m)-convex function with modulus c0, where (α,m)[0,1]2, if

    f(xt+m(1t)y)tαf(x)+m(1tα)f(y)cmtα(1tα)|yx|2, (1.1)

    holds x,y[0,+) and t[0,1].

    The well-known Hadamard inequality is a very nice geometrical interpretation of convex functions defined on the real line, it is stated as follows:

    Theorem 1. The following inequality holds:

    f(x+y2)1yxyxf(v)dvf(x)+f(y)2, (1.2)

    for convex function f:IR, where I is an interval and x,yI, x<y.

    The definition of Riemann-Liouville fractional integrals is given as follows:

    Definition 2. Let fL1[a,b]. Then left-sided and right-sided Riemann-Liouville fractional integrals of a function f of order μ where (μ)>0 are defined by

    Iμa+f(x)=1Γ(μ)xa(xt)μ1f(t)dt,x>a, (1.3)

    and

    Iμbf(x)=1Γ(μ)bx(tx)μ1f(t)dt,x<b. (1.4)

    The following theorems provide two Riemann-Liouville fractional versions of the Hadamard inequality for convex functions.

    Theorem 2. [8] Let f:[a,b]R be a positive function with 0a<b and fL1[a,b]. If f is a convex function on [a,b], then the following fractional integral inequality holds:

    f(a+b2)Γ(μ+1)2(ba)μ[Iμa+f(b)+Iμbf(a)]f(a)+f(b)2, (1.5)

    with μ>0.

    Theorem 3. [9] Under the assumption of Theorem 2, the following fractional integral inequality holds:

    f(a+b2)2μ1Γ(μ+1)(ba)μ[Iμ(a+b2)+f(b)+Iμ(a+b2)f(a)]f(a)+f(b)2, (1.6)

    with μ>0.

    Theorem 4. [8] Let f:[a,b]R be a differentiable mapping on (a,b) with a<b. If |f| is convex on [a,b], then the following fractional integral inequality holds:

    |f(a)+f(b)2Γ(μ+1)2(ba)μ[Iμa+f(b)+Iμbf(a)]|ba2(μ+1)(112μ)[|f(a)|+|f(b)|]. (1.7)

    The k-analogue of Riemann-Liouville fractional integrals is defined as follows:

    Definition 3. [10] Let fL1[a,b]. Then k-fractional Riemann-Liouville integrals of order μ where (μ)>0, k>0, are defined by

    kIμa+f(x)=1kΓk(μ)xa(xt)μk1f(t)dt,x>a, (1.8)

    and

    kIμbf(x)=1kΓk(μ)bx(tx)μk1f(t)dt,x<b, (1.9)

    where Γk(.) is defined as [11]

    Γk(μ)=0tμ1etkkdt.

    The k-fractional versions of Hadamard type inequalities (1.5)–(1.7) are given in the following theorems.

    Theorem 5. [12] Let f:[a,b]R be a positive function with 0a<b. If f is a convex function on [a,b], then the following inequalities for k-fractional integrals hold:

    f(a+b2)Γk(μ+k)2(ba)μk[kIμa+f(b)+kIμbf(a)]f(a)+f(b)2. (1.10)

    Theorem 6. [13] Under the assumption of Theorem 5, the following fractional integral inequality holds:

    f(a+b2)2μk1Γk(μ+k)(ba)μk[kIμ(a+b2)+f(b)+kIμ(a+b2)f(a)]f(a)+f(b)2. (1.11)

    Theorem 7. [12] Let f:[a,b]R be a differentiable mapping on (a,b) with 0a<b. If |f| is convex on [a,b], then the following inequality for k-fractional integrals holds:

    |f(a)+f(b)2Γk(μ+k)2(ba)μk[kIμa+f(b)+kIμbf(a)]|ba2(μk+1)(112μk)[|f(a)|+|f(b)|]. (1.12)

    In the following, we give the definition of generalized Riemann-Liouville fractional integrals by a monotonically increasing function.

    Definition 4. [14] Let fL1[a,b]. Also let ψ be an increasing and positive monotone function on (a,b], having a continuous derivative ψ on (a,b). The left-sided and right-sided fractional integrals of a function f with respect to another function ψ on [a,b] of order μ where (μ)>0 are defined by

    Iμ,ψa+f(x)=1Γ(μ)xaψ(t)(ψ(x)ψ(t))μ1f(t)dt,x>a, (1.13)

    and

    Iμ,ψbf(x)=1Γ(μ)bxψ(t)(ψ(t)ψ(x))μ1f(t)dt,x<b. (1.14)

    The k-analogue of generalized Riemann-Liouville fractional integrals is defined as follows:

    Definition 5 [4] Let fL1[a,b]. Also let ψ be an increasing and positive monotone function on (a,b], having a continuous derivative ψ on (a,b). The left-sided and right-sided fractional integrals of a function f with respect to another function ψ on [a,b] of order μ where (μ)>0, k>0, are defined by

    kIμ,ψa+f(x)=1kΓk(μ)xaψ(t)(ψ(x)ψ(t))μk1f(t)dt,x>a, (1.15)

    and

    kIμ,ψbf(x)=1kΓk(μ)bxψ(t)(ψ(t)ψ(x))μk1f(t)dt,x<b. (1.16)

    For more details of above defined fractional integrals, we refer the readers to see [15,16].

    Rest of the paper is organized as follows: In Section 2, we find Hadamard type inequalities for generalized Riemann-Liouville fractional integrals with the help of strongly (α,m)-convex functions. The consequences of these inequalities are listed in remarks. Also some new fractional integral inequalities for convex functions, strongly convex functions and strongly m-convex functions are deduced in the form of corollaries. In Section 3, the error bounds of Hadamard type fractional inequalities are established via two fractional integral identities.

    Theorem 8. Let f:[a,b]R be a positive function with 0a<mb and fL1[a,b]. Also suppose that f is strongly (α,m)-convex function on [a,b] with modulus c0, ψ is positive strictly increasing function having continuous derivative ψ on (a,b). If [a,b]Range(ψ), k>0 and (α,m)(0,1]2, then the following k-fractional integral inequality holds:

    f(a+mb2)+cm(2α1)22α(μ+k)(μ+2k)[μ(μ+k)(ba)2+2k2(ammb)2+2μk(ba)(ammb)]Γk(μ+k)2α(mba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(mb))+(2α1)mμk+1kIμ,ψψ1(b)(fψ)(ψ1(am))][f(a)+m(2α1)f(b)]μ2α(μ+kα)+mkαμ(f(b)+m(2α1)f(am2))2α(μ2+μαk)cmkαμ[(ba)2+m(2α1)(bam2)2]2α(μ+αk)(μ+2αk), (2.1)

    with μ>0.

    Proof. Since f is strongly (α,m)-convex function, for x,y[a,b] we have

    f(x+my2)f(x)+m(2α1)f(y)2αcm(2α1)|yx|222α. (2.2)

    By setting x=at+m(1t)b, y=am(1t)+bt and integrating the resulting inequality after multiplying with tμk1, we get

    kμf(a+mb2)12α[10f(at+m(1t)b)tμk1dt+m(2α1)10f(am(1t)+bt)tμk1dt]cm(2α1)22αμ(μ+k)(μ+2k)[μk(μ+k)(ba)2+2k3(ammb)2+2k2μ(ba)(ammb)]. (2.3)

    Now, let u[a,b] such that ψ(u)=at+m(1t)b, that is, t=mbψ(u)mba and let v[a,b] such that ψ(v)=am(1t)+bt, that is, t=ψ(v)ambam in (2.3), then multiplying μk after applying Definition 5, we get the following inequality:

    f(a+mb2)Γk(μ+k)2α(mba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(mb))+mμk+1(2α1)kIμ,ψψ1(b)(fψ)(ψ1(am))]cm(2α1)22α(μ+k)(μ+2k)[μ(ba)2+2k2(ammb)2+2μk(ba)(ammb)]. (2.4)

    Hence by rearranging the terms, the first inequality is established. On the other hand, f is strongly (α,m)-convex function, for t[0,1], we have the following inequality:

    f(at+m(1t)b)+m(2α1)f(am(1t)+bt)tα[f(a)+m(2α1)f(b)]+m(1tα)[f(b)+m(2α1)f(am2)]cmtα(1tα)[(ba)2+m(2α1)(bam2)2]. (2.5)

    Multiplying inequality (2.5) with tμk1 on both sides and then integrating over the interval [0,1], we get

    10tμk1f(ta+m(1t)b)dt+m(2α1)10tμk1f(am(1t)+tb)dt(f(a)+m(2α1)f(b))(kμ+kα)+m(f(b)+m(2α1)f(am2))k2αμ2+μαkcmαk2[(ba)2+m(2α1)(bam2)2](μ+αk)(μ+2αk). (2.6)

    Again taking ψ(u)=at+m(1t)b that is t=mbψ(u)mba and ψ(v)=am(1t)+bt that is t=ψ(v)ambam in (2.6), then by applying Definition 5, the second inequality can be obtained.

    Remark 1. Under the assumption of Theorem 8, by fixing parameters one can achieve the following outcomes:

    (i) If α=m=1 in (2.1), then the inequality stated in [17,Theorem 9] can be obtained.

    (ii) If α=m=1, ψ=I and c=0 in (2.1), then Theorem 5 can be obtained.

    (iii) If α=k=m=1, ψ=I and c=0 in (2.1), then Theorem 2 can be obtained.

    (iv) If α=k=m=1 and ψ=I in (2.1), then the inequality stated in [18,Theorem 2.1] can be obtained.

    (v) If α=μ=k=m=1, ψ=I and c=0 in (2.1), then the Hadamard inequality can be obtained.

    (vi) If α=m=1 and c=0 in (2.1), then the inequality stated in [19,Theorem 1] can be obtained.

    (vii) If α=m=k=1 and c=0 in (2.1), then the inequality stated in [20,Theorem 2.1] can be obtained.

    (viii) If α=k=1 and ψ=I in (2.1), then the inequality stated in [21,Theorem 6] can be obtained.

    (ix) If α=μ=m=k=1 and ψ=I in (2.1), then the inequality stated in [22,Theorem 6] can be obtained.

    (x) If α=k=1, ψ=I and c=0 in (2.1), then the inequality stated in [23,Theorem 2.1] can be obtained.

    (xi) If k=1 and ψ=I in (2.1), then the inequality stated in [24,Theorem 4] can be obtained.

    Corollary 1. Under the assumption of Theorem 8 with c=0 in (2.1), the following fractional integral inequality holds:

    f(a+mb2)Γk(μ+k)2α(mba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(mb))+(2α1)mμk+1kIμ,ψψ1(b)(fψ)(ψ1(am))][f(a)+m(2α1)f(b)]μ2α(μ+kα)+mμαk(f(b)+m(2α1)f(am2))2α(μ2+μαk).

    Corollary 2. Under the assumption of Theorem 8 with k=1 in (2.1), the following fractional integral inequality holds:

    f(a+mb2)+cmμ(2α1)22αμ(μ+1)(μ+2)[μ(μ+1)(ba)2+2(ammb)2+2μ(ba)(ammb)]Γ(μ+1)2α(mba)μ[Iμ,ψψ1(a)+(fψ)(ψ1(mb))+(2α1)mμ+1Iμ,ψψ1(b)(fψ)(ψ1(am))][f(a)+m(2α1)f(b)]μ2α(μ+α)+m(f(b)+m(2α1)f(am2))αμ2α(μ2+μα)cmαμ[(ba)2+m(2α1)(bam2)2]2α(μ+α)(μ+2α).

    Corollary 3. Under the assumption of Theorem 8 with ψ=I in (2.1), the following fractional integral inequality holds:

    f(a+mb2)+cm(2α1)22α(μ+k)(μ+2k)[μ(μ+k)(ba)2+2k2(ammb)2+2μk(ba)(ammb)]Γk(μ+k)2α(mba)μk[kIμa+f(mb)+(2α1)mμk+1kIμbf(am)][f(a)+m(2α1)f(b)]μ2α(μ+kα)+mkαμ(f(b)+m(2α1)f(am2))2α(μ2+μαk)cmkαμ[(ba)2+m(2α1)(bam2)2]2α(μ+αk)(μ+2αk).

    Theorem 9. Under the assumption of Theorem 8, the following k-fractional integral inequality holds:

    f(a+mb2)+cmμ(2α1)22α+2(μ+2k)[μ(μ+k)(ba)2+(ammb)2(μ2+5kμ+8k2)+2μ(μ+3k)(ba)×(ammb)]2μkαΓk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1(2α1)kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]μ[f(a)+m(2α1)f(b)]22α(αk+μ)+m(2α(μ+αk)μ)22α(μ+αk)(f(b)+m(2α1)f(am2))cmμ[2α(μ+2αk)(μ+αk)]23α(μ+αk)(μ+2αk)((ba)2+m(bam2)2), (2.7)

    with μ>0.

    Proof. Let x=at2+m(2t2)b, y=am(2t2)+bt2 in (2.2) and integrating the resulting inequality over [0,1] after multiplying with tμk1, we get

    kμf(a+mb2)12α[10f(at2+m(2t2)b)tμk1dt+m(2α1)10f(am(2t2)+bt2)tμk1dt]cm(2α1)22α+2(μ+2k)[μ(μ+k)(ba)2k+k(ammb)2(μ2+5kμ+8k2)+2μ(ba)(ammb)(μ+3k)k]. (2.8)

    Let u[a,b], so that ψ(u)=at2+m(2t2)b, that is, t=2(mbψ(u))mba and v[a,b], so that ψ(v)=am(2t2)+bt2, that is, t=2(ψ(v)am)bam in (2.8), then by applying Definition 5, we get

    f(a+mb2)2μkΓk(μ+k)2α(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1(2α1)kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]cmμ(2α1)22α4(μ+2k)[μ(μ+k)(ba)2+(ammb)2(μ2+5kμ+8k2)+2μ(ba)(ammb)(μ+3k)]. (2.9)

    Hence by rearranging terms, the first inequality is established. Since f is strongly (α,m)-convex function with modulus c0, for t[0,1], we have following inequality

    f(at2+m(2t2)b)+m(2α1)f(am(2t2)+bt2)(t2)α[f(a)+m(2α1)f(b)]+m(2αtα2α)[f(b)+m(2α1)f(am2)]cmtα(2αtα)[(ba)2+m(bam2)2]22α. (2.10)

    Multiplying (2.10) with tμk1 on both sides and integrating over [0,1], we get

    10f(at2+m(2t2)b)tμk1dt+m(2α1)10f(am(2t2)+bt2)tμk1dtk[f(a)+m(2α1)f(b)]2α(αk+μ)+mk(2α(μ+αk)μ)2αμ(μ+αk)(f(b)+m(2α1)f(am2))cmk(2α(μ+2αk)(μ+αk))22α((ba)2+m(bam2)2). (2.11)

    Again taking ψ(u)=at2+m(2t2)b, that is, t=2(mbψ(v))mba and so that ψ(v)=am(2t2)+bt2, that is, t=2(ψ(v)am)bam in (2.11), then by applying Definition 5, the second inequality can be obtained.

    Remark 2. Under the assumption of Theorem 9, one can achieve the following outcomes:

    (i) If α=m=1 in (2.7), then the inequality stated in [17,Theorem 10] can be obtained.

    (ii) If α=m=k=1, ψ=I and c=0 in (2.7), then Theorem 3 can be obtained.

    (iii) If α=μ=m=k=1, ψ=I and c=0 in (2.7), then Hadamard inequality can be obtained.

    (iv) If α=m=1, ψ=I and c=0 in (2.7), then the inequality stated in [13,Theorem 2.1] can be obtained.

    (v) If α=m=1 and c=0 in (2.7), then the inequality stated in [17,corrollary 5] can be obtained.

    (vi) If α=k=1 and ψ=I in (2.7), then the inequality stated in [21,Theorem 7] can be obtained.

    (vii) If k=1 and ψ=I in (2.7), then the inequality stated in [24,Theorem 5] can be obtained.

    (viii) If α=m=k=1 and c=0 in (2.7), then the inequality stated in [25,Lemma 1] can be obtained.

    Corollary 4. Under the assumption of Theorem 9 with c=0 in (2.7), the following fractional integral inequality holds:

    f(a+mb2)2μkαΓk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1(2α1)kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]μ[f(a)+m(2α1)f(b)]22α(αk+μ)+m(2α(μ+αk)μ)22α(μ+αk)(f(b)+m(2α1)f(am2)).

    Corollary 5. Under the assumption of Theorem 9 with k=1 in (2.7), the following fractional integral inequality holds:

    f(a+mb2)+cmμ(2α1)22α+2(μ+1)(μ+2)[μ(μ+1)(ba)2+(ammb)2(μ2+5μ+8)+2μ(μ+3)(ba)(ammb)]2μαΓ(μ+1)(mba)μ[Iμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμ+1(2α1)Iμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]μ[f(a)+m(2α1)f(b)]22α(α+μ)+m[2α(μ+α)μ]22α(μ+α)(f(b)+m(2α1)f(am2))cmμ(2α(μ+2α)(μ+α))23α(μ+α)(μ+2α)×[(ba)2+m(bam2)2].

    Corollary 6. Under the assumption of Theorem 9 with ψ=I in (2.7), the following fractional integral inequality holds:

    f(a+mb2)+cmμ(2α1)22α+2(μ+2k)[μ(μ+k)(ba)2+(ammb)2(μ2+5kμ+8k2)+2μ(ba)(μ+3k)(ammb)]2μkαΓk(μ+k)(mba)μk[kIμ(a+mb2)+f(mb))+mμk+1(2α1)kIμ(a+mb2m)f(am)]μ[f(a)+m(2α1)f(b)]22α(αk+μ)+m(2α(μ+αk)μ)22α(μ+αk)(f(b)+m(2α1)f(am2))cmμ[2α(μ+2αk)(μ+αk)]23α(μ+αk)(μ+2αk)((ba)2+m(bam2)2).

    In this section, we find the error estimations of Hadamard type fractional inequalities for strongly (α,m)-convex functions by using (1.15) and (1.16) that gives the refinements of already proved estimations. The following lemma is useful to prove the next results.

    Lemma 1. Let a<b and f:[a,b]R be a differentiable mapping on (a,b). Also, suppose that fL[a,b], ψ is positive strictly increasing function, having a continuous derivative ψ on (a,b). If [a,b]Range(ψ), k>0, then the following identity holds for generalized fractional integral operators:

    f(a)+f(b)2Γk(μ+k)2(ba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(b))+kIμ,ψψ1(b)(fψ)(ψ1(a)]=ba210[(1t)μktμk]f(ta+(1t)b)dt. (3.1)

    Proof. We cosider the right hand side of (3.1) as follows:

    10((1t)αktμk)f(ta+(1t)b)dt=10(1t)μk1f(ta+(1t)b)dt10tμk1f(ta+(1t)b)dt=I1I2 (3.2)

    Integrating by parts we get

    I1=10(1t)μk1f(ta+(1t)b)dt=f(b)baμk(ba)10(1t)μk1f(ta+(1t)b)dt

    We have v[a,b] such that ψ(v)=ta+(1t)b, with this substitution one can have

    I1=f(b)baμk(ba)ψ1(b)ψ1(a)(ψ(v)aba)μk1(fψ(v))baψ(v)dv=f(b)baΓk(μ+k)(ba)μk+1Iμ,ψψ1(b)(fψ)(ψ1(a)). (3.3)

    Similarly one can get after a little computation

    I2=f(a)ba+Γk(μ+k)(ba)μk+1Iμ,ψψ1(a)+(fψ)(ψ1(b)). (3.4)

    Using (3.3) and (3.4) in (3.2), (3.1) can be obtained.

    Remark 3. (i) If k=1 and ψ=I in (3.1), then the equality stated in [8,Lemma 2] can be obtained.

    (ii) For μ=k=1 and ψ=I in (3.1), then the equality stated in [28,Lemma 2.1] can be obtained.

    Theorem 10. Let f:[a,b]R be a differentiable mapping on (a,b) with 0a<b. Also suppose that |f| is strongly (α,m)-convex with modulus c0, ψ is positive strictly increasing function having continuous derivative ψ on (a,b). If [a,b]Range(ψ), k>0 and (α,m)(0,1]2, then the following k-fractional integral inequality holds:

    |f(a)+f(b)2Γk(μ+k)2(ba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(b))+kIμ,ψψ1(b)(fψ)(ψ1(a))]|ba2[|f(a)|(2B(12;α+1,μk+1)+1(12)α+μkα+μk+1B(α+1,μk+1))+m|f(bm)|×(2(1(12)μk)μk+1+(12)1+μk+αμk+1+α2B(12;α+1,μk+1)1(12)1+μk+αμk+1+α+B(α+1,μk+1))cm(bma)22(2B(12;α+1,μk+1)2α4α2˜F1(1+2α,μk,2(1+α);12)+1(12)μk+αμk+1+αB(α+1,μk+1)1(12)μk+2αμk+1+2α+B(2α+1,μk+1))], (3.5)

    with μ>0 and 2˜F1(1+2α,μk,2(1+α);12) is regularized hypergeometric function.

    Proof. By Lemma 1, it follows that

    |f(a)+f(b)2Γk(μ+k)2(ba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(b))+kIμ,ψψ1(b)(fψ)(ψ1(b)]|ba210|(1t)μktμk||f(ta+(1t)b|)dt. (3.6)

    Since |f| is strongly (α,m)-convex function on [a,b] and t[0,1], we have

    |f(ta+(1t)b)|tα|f(a)|+m(1tα)|f(bm)|cmtα(1tα)(bma)2. (3.7)

    Therefore (3.6) implies the following inequality

    |f(a)+f(b)2Γk(μ+k)2(ba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(b))+kIμ,ψψ1(b)(fψ)(ψ1(b)]|ba210|(1t)μktμk|(tα|f(a)|+m(1tα)|f(bm)|cmtα(1tα)(bma)2]dtba2[|f(a)|(120tα((1t)μktμk)dt+112tα(tμk(1t)μk)dt)+m|f(bm)|(120(1tα)((1t)μktμk)dt+112(1tα)(tμk(1t)μk)dt)cm(bma)2(120tα(1tα)((1t)μktμk)dt+112tα(1tα)(tμk(1t)μk)dt)]. (3.8)

    In the following, we compute integrals appearing on the right side of the above inequality

    120tα((1t)μktμk)dt+112tα(tμk(1t)μk)dt=2B(12;α+1,μk+1)+1(12)α+μkα+μk+1B(α+1,μk+1). (3.9)
    120(1tα)((1t)μktμk)dt+112(1tα)(tμk(1t)μk)dt.=2(1(12)μk)μk+1+(12)1+μk+αμk+1+α2B(12;α+1,μk+1)1(12)1+μk+αμk+1+α+B(α+1,μk+1). (3.10)
    112tα(1tα)((1t)μktμk)dt+112tα(1tα)(tμk(1t)μk)dt=2B(12;α+1,μk+1)(12)1+μk+αμk+1+α2α4α2˜F1(1+2α,μk,2(1+α);12)+(12)1+μk+2αμk+1+2α+1(12)1+μk+αμk+1+αB(α+1,μk+1)1(12)1+μk+2αμk+1+2α+B(2α+1,μk+1). (3.11)

    Using (3.9), (3.10) and (3.11) in (3.8), we get the required inequality (3.5).

    Remark 4. Under the assumption of Theorem 10, one can achieve the following outcomes:

    (i) If α=m=1 in (3.5), then the inequality stated in [17,Theorem 11] can be obtained.

    (ii) If α=m=1 and c=0 in (3.5), then the inequality stated in [17,Corollary 10] can be obtained.

    (iii) If α=m=1, ψ=I and c=0 in (3.5), then Theorem 7 can be obtained.

    (iv) If α=m=k=1, ψ=I and c=0 in (3.5), then Theorem 4 can be obtained.

    (v) If α=k=1 and ψ=I in (3.5), then the inequality stated in [21,Theorem 8] can be obtained.

    (vi) If α=μ=m=k=1 and ψ=I in (3.5), then the inequality stated in [26,Corollary 6] can be obtained.

    Corollary 7. Under the assumption of Theorem 10 with c=0 in (3.5), the following inequality holds:

    |f(a)+f(b)2Γk(μ+k)2(ba)μk[kIμ,ψψ1(a)+(fψ)(ψ1(b))+kIμ,ψψ1(b)(fψ)(ψ1(a))]|ba2[|f(a)|(2B(12;α+1,μk+1)+1(12)α+μkα+μk+1B(α+1,μk+1))+m|f(bm)|×(2(1(12)μk)μk+1+(12)1+μk+αμk+1+α2B(12;α+1,μk+1)1(12)1+μk+αμk+1+α+B(α+1,μk+1))].

    Corollary 8. Under the assumption of Theorem 10 with k=m=1 and c=0 in (3.5), the following inequality holds:

    |f(a)+f(b)2Γ(μ+1)2(ba)μ[Iμ,ψψ1(a)+(fψ)(ψ1(b))+Iμ,ψψ1(b)(fψ)(ψ1(a))]|ba2[|f(a)|(2B(12;α+1,μ+1)+1(12)α+μα+μ+1B(α+1,μ+1))+|f(b)|×(2(1(12)μ)μ+1+(12)1+μ+αμ+1+α2B(12;α+1,μ+1)1(12)1+μ+αμ+1+α+B(α+1,μ+1))].

    Corollary 9. Under the assumption of Theorem 10 with ψ=I in (3.5), the following inequality holds:

    |f(a)+f(b)2Γk(μ+k)2(ba)μk[kIμa+f(b)+kIμbf(a)]|ba2[|f(a)|(2B(12;α+1,μk+1)+1(12)α+μkα+μk+1B(α+1,μk+1))+m|f(bm)|(2(1(12)μk)μk+1+(12)1+μk+αμk+1+α2B(12;α+1,μk+1)1(12)1+μk+αμk+1+α+B(α+1,μk+1))]c(ba)3(2B(12;α+1,μk+1)2α4α2˜F1(1+2α,μk,2(1+α);12)+1(12)μk+αμk+1+αB(α+1,μk+1)1(12)μk+2αμk+1+2α+B(2α+1,μk+1))].

    For next two results, we need the following lemma.

    Lemma 2. [26] Let f:[a,b]R be a differentiable mapping on (a,b) such that fL[a,b], ψ is positive increasing function having continuous derivative ψ on (a,b). If [a,b]Range(ψ), k>0 and m(0,1], then the following integral identity for fractional integral holds:

    2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]=mba4[10tμkf(at2+m(2t2)b)dt10tμkf(am(2t2)+bt2)dt]. (3.12)

    Theorem 11. Let f:[a,b]R be a differentiable mapping on (a,b) such that fŁ[a,b]. Also suppose that |f|q is strongly (α,m)-convex function on [a,b] for q1, ψ is an increasing and positive monotone function on (a,b], having a continuous derivative ψ on (a,b). If [a,b]Range(ψ), k>0 and (α,m)(0,1]2, then the following k-fractional integral inequality holds:

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba22+1q(μk+1)(μk+2)1q[(21αk|f(a)|q(μk+1)(μk+2)αk+μ+k+21αmk|f(b)|q(μk+1)(μk+2)(2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k))212αcm(ba)2(μk+1)(μk+2)×(2α(2αk+μ+k)(αk+μ+k)(kα+μ+k)(2αk+μ+k)))1q+(21αkm|f(am2)|q(μk+1)(μk+2)(2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k))+21αk(μk+1)(μk+2)|f(b)|qαk+μ+k212αcm(μk+1)(μk+2)(bam2)2(2α(2αk+μ+k)(αk+μ+k)(kα+μ+k)(2αk+μ+k)))1q], (3.13)

    with μ>0.

    Proof. Applying Lemma 2 and strongly (α,m)-convexity of |f|, (for q=1), we have

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba4[10|tμkf(at2+m(2t2)b)|dt+10|tμkf(am(2t2)+bt2)dt|]mba4[(|f(a)|+|f(b)|2α)10tμk+αdt+m(|f(b)|+|f(am2)|)2α10(2αtα)tμkdtcm((ba)2+(bam2)2)22α10tμk+α(2αtα)dt]mba4[k[|f(a)|+|f(b)|]2α(μ+αk+k)+mk[2α(αk+μ+k)(μ+k)](μ+k)(αk+μ+k)×(|f(b)|+|f(am2)|)cmk[2α(2αk+μ+k)(αk+μ+k)]22α(αk+μ+k)(2αk+μ+k)((ba)2+(bam2)2)].

    Now for q>1, we proceed as follows: From Lemma 2 and using power mean inequality, we get

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba4(10tμkdt)11q[(10tμk|f(at2+m(2t2)b)|qdt)1q+(10tμk|f(am(2t2)+bt2)|qdt)1q]mba4(μk+1)1p[(|f(a)|q2α10tα+μkdt+m|f(b)|q2α10(2αtα)tμkdtcm(ba)222α10(2αtα)tμk+αdt)1q+(m|f(am2)|2α10(2αtα)tμkdt+|f(b)|q2α10tα+μkdtcm(bam2)222α10(2αtα)tμk+αdt)1q]mba4(μk+1)1p[(k|f(a)|q2α(αk+μ+k)+mk|f(b)|q[2α(αk+μ+k)(μ+k)]2α(μ+k)(αk+μ+k)cmk(ba)2[2α(2αk+μ+k)(αk+μ+k)]22α(kα+μ+k)(2αk+μ+k))1q+(mk|f(am2)|q[2α(αk+μ+k)(μ+k)]2α(μ+k)(αk+μ+k)+k|f(b)|q2α(kα+μ+k)cmk(bam2)2[2α(2αk+μ+k)(αk+μ+k)]22α(kα+μ+k)(2αk+μ+k))1q]mba22+1q(μk+1)(μk+2)1q[(2k|f(a)|q(μk+1)(μk+2)2α(αk+μ+k)+21αmk|f(b)|q(μk+1)(μk+2)(2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k))212αcm(ba)2(μk+1)(μk+2)(2α(2αk+μ+k)(αk+μ+k)(kα+μ+k)(2αk+μ+k)))1q+(21αkm|f(am2)|q(μk+1)(μk+2)2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k)+2k(μk+1)(μk+2)|f(b)|q2α(αk+μ+k)2cm(μk+1)(μk+2)(bam2)222α2α(2αk+μ+k)(αk+μ+k)(kα+μ+k)(2αk+μ+k))1q].

    This completes the proof.

    Remark 5. Under the assumption of Theorem 11, one can achieve the following outcomes:

    (i) If α=m=1 in (3.13), then the inequality stated in [17,Theorem 12] can be obtained.

    (ii) If α=k=1 and ψ=I in (3.13), then the inequality stated in [21,Theorem 10] can be obtained.

    (iii) If α=k=1, ψ=I and c=0 in (3.13), then the inequality stated in [27,Theorem 2.4] can be obtained.

    (iv) If α=m=1, ψ=I and c=0 in (3.13), then the inequality stated in [13,Theorem 3.1] can be obtained.

    (v) If α=m=k=1, ψ=I and c=0 in (3.13), then the inequality stated in [9,Theorem 5] can be obtained.

    (vi) If α=μ=k=m=q=1 and ψ=I in (3.13), then the inequality stated in [26,Corollary 8] can be obtained.

    (vii) If α=μ=k=m=q=1, ψ=I and c=0 in (3.13), then the inequality stated in [28,Theorem 2.2] can be obtained.

    Corollary 10. Under the assumption of Theorem 11 with c=0 in (3.13), the following inequality holds:

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba22+1q(μk+1)(μk+2)1q[(21αk|f(a)|q(μk+1)(μk+2)αk+μ+k+21αmk|f(b)|q(μk+1)(μk+2)(2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k)))1q+(21αkm|f(am2)|q(μk+1)(μk+2)×(2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k))+21αk(μk+1)(μk+2)|f(b)|qαk+μ+k)1q].

    Corollary 11. Under the assumption of Theorem 11 with k=1 in (3.13), the following inequality holds:

    |2μ1Γ(μ+1)(mba)μ[Iμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμ+1Iμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba22+1q(μ+1)(μ+2)1q[(21α|f(a)|q(μ+1)(μ+2)α+μ+1+21αm|f(b)|q(μ+1)(μ+2)×(2α(α+μ+1)(μ+1)(μ+1)(α+μ+1))212αcm(ba)2(μ+1)(μ+2)(2α(2α+μ+1)(α+μ+1)(α+μ+1)(2α+μ+1)))1q+(21αm|f(am2)|q(μ+1)(μ+2)(2α(α+μ+1)(μ+1)(μ+1)(α+μ+1))+21α(μ+1)(μ+2)|f(b)|qα+μ+1212αcm(μ+1)(μ+2)(bam2)2(2α(2α+μ+1)(α+μ+1)(α+μ+1)(2α+μ+1)))1q].

    Corollary 12. Under the assumption of Theorem 11 with ψ=I in (3.13), the following inequality holds:

    |2μk1Γk(μ+k)(mba)μk[kIμ(a+mb2)+f(mb)+mμk+1kIμ(a+mb2m)f(am)]12[f(a+mb2)+mf(a+mb2m)]|mba22+1q(μk+1)(μk+2)1q[(21αk|f(a)|q(μk+1)(μk+2)αk+μ+k+21αmk|f(b)|q(μk+1)(μk+2)×(2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k))212αcm(ba)2(μk+1)(μk+2)(2α(2αk+μ+k)(αk+μ+k)(kα+μ+k)(2αk+μ+k)))1q+(21αkm|f(am2)|q(μk+1)(μk+2)(2α(αk+μ+k)(μ+k)(μ+k)(αk+μ+k))+21αk(μk+1)(μk+2)|f(b)|qαk+μ+k212αcm(μk+1)(μk+2)(bam2)2(2α(2αk+μ+k)(αk+μ+k)(kα+μ+k)(2αk+μ+k)))1q].

    Theorem 12. Let f:IR be a differentiable mapping on (a,b) with a<b. Also suppose that |f|q is strongly (α,m)-convex function for q>1, ψ is positive increasing function having continuous derivative ψ on (a,b). If [a,b]Range(ψ), k>0 and (α,m)(0,1]2, then the following fractional integral inequality holds:

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba421p(μpk+1)1p[((|f(a)|(22αα+1)1q+|f(b)|(2αm[2α(1+α)1]1+α)1q)q222αcm(ba)2(1α+2α(1+2α)(1+α)(1+2α)))1q+((|f(am2)|(22αm[2α(1+α)1]1+α)1q+(22αα+1)1q|f(b)|)q222αcm(bam2)2(1(1+α)+2α(1+2α)(1+α)(1+2α)))1q], (3.14)

    with μ>0 and 1p+1q=1.

    Proof. By applying Lemma 2 and using the property of modulus, we get

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba4[10|tμkf(at2+m(2t2)b)|dt+10|tμkf(am(2t2)+bt2)|dt].

    Now applying Hölder's inequality for integrals, we get

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba4(μpk+1)1p[(10|f(at2+m(2t2)b)|qdt)1q+(10|f(am(2t2)+bt2)|qdt)1q].

    Using strongly (α,m)-convexity of |f|q, we get

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba4(μpk+1)1p[(|f(a)|q2α10tαdt+m|f(b)|q2α10(2αtα)dtcm(ba)222α10tα(2αtα)dt)1q+(m|f(am2)|q2α10(2αtα)dt+|f(b)|q2α10tαdtcm(bam2)222α10tα(2αtα)dt)1q]=mba4(μpk+1)1p[(|f(a)|q2α(α+1)+m|f(b)|q[2α(1+α)1]2α(1+α)cm(ba)222α(1(1+α)+2α(1+2α)(1+α)(1+2α)))1q+(m|f(am2)|q[2α(1+α)1]2α(1+α)+|f(b)|q2α(α+1)cm(bam2)222α(1α+2α(1+2α)(1+α)(1+2α)))1q]mba421p(μpk+1)1p[(22α|f(a)|q(α+1)+22αm|f(b)|q[2α(1+α)1]1+α222αcm(ba)2(1α+2α(1+2α)(1+α)(1+2α)))1q+(22αm|f(am2)|q[2α(1+α)1](1+α)+22α|f(b)|qα+1222αcm(bam2)2(1α+2α(1+2α)(1+α)(1+2α)))1q]mba421p(μpk+1)1p[((|f(a)|(22αα+1)1q+|f(b)|(22αm[2α(1+α)1]1+α)1q)q222αcm(ba)2(1α+2α(1+2α)(1+α)(1+2α)))1q+((|f(am2)|×(22αm[2α(1+α)1]1+α)1q+(22αα+1)1q|f(b)|)q222αcm(bam2)2(1(1+α)+2α(1+2α)(1+α)(1+2α)))1q].

    Here, we have used the fact aq+bq(a+b)q, for q>1, a,b0. This completes the proof.

    Remark 6. Under the assumption of Theorem 12, one can achieve the following outcomes:

    (i) If α=m=1 in (3.14), then the inequality stated in [17,Theorem 13] can be obtained.

    (ii) If α=k=1 and ψ=I in (3.14), then the inequality stated in [21,Theorem 10] can be obtained.

    (iii) If α=k=1, ψ=I and c=0 in (3.14), then the inequality stated in [27,Theorem 2.7] can be obtained.

    (iv) If α=m=1, ψ=I and c=0 in (3.14), then the inequality stated in [13,Theorem 2.7] can be obtained.

    (v) If α=μ=k=m=1, ψ=I and c=0 in (3.14), then the inequality stated in [29,Theorem 2.4] can be obtained.

    Corollary 13. Under the assumption of Theorem 12 with c=0 in 3.14, the following inequality holds:

    |2μk1Γk(μ+k)(mba)μk[kIμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμk+1kIμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba421p(μpk+1)1p[|f(a)|(22αα+1)1q+|f(b)|(22αm[2α(1+α)1]1+α)1q+(|f(am2)|(22αm[2α(1+α)1]1+α)1q+(22αα+1)1q|f(b)|)].

    Corollary 14. Under the assumption of Theorem 12 with k=1 in (3.14), the following inequality holds:

    |2μ1Γ(μ+1)(mba)μ[Iμ,ψψ1(a+mb2)+(fψ)(ψ1(mb))+mμ+1Iμ,ψψ1(a+mb2m)(fψ)(ψ1(am))]12[f(a+mb2)+mf(a+mb2m)]|mba421p(μp+1)1p[((|f(a)|(22αα+1)1q+|f(b)|(2αm[2α(1+α)1]1+α)1q)q222αcm(ba)2(1α+2α(1+2α)(1+α)(1+2α)))1q+((|f(am2)|(22αm[2α(1+α)1]1+α)1q+(22αα+1)1q|f(b)|)q222αcm(bam2)2(1(1+α)+2α(1+2α)(1+α)(1+2α)))1q].

    Corollary 15. Under the assumption of Theorem 12 with ψ=I in (3.14), the following inequality holds:

    |2μk1Γk(μ+k)(mba)μk[kIμ(a+mb2)+f(mb)+mμk+1kIμ(a+mb2m)f(am)]12[f(a+mb2)+mf(a+mb2m)]|mba421p(μpk+1)1p[((|f(a)|(22αα+1)1q+|f(b)|(22αm[2α(1+α)1]1+α)1q)q222αcm(ba)2×(1α+2α(1+2α)(1+α)(1+2α)))1q+((|f(am2)|(22αm[2α(1+α)1]1+α)1q+(22αα+1)1q|f(b)|)q222αcm(bam2)2(1(1+α)+2α(1+2α)(1+α)(1+2α)))1q].

    Some new versions of the Hadamard type inequalities are established for strongly (α,m)-convex functions via the generalized Riemann-Liouville fractional integrals. We have obtained new generalizations as well as proved estimations of such inequalities for strongly (α,m)-convex functions. We conclude that findings of this study give the refinements as well as generalization of several fractional inequalities for convex, strongly convex and strongly m-convex functions. The reader can further deduce inequalities for Riemann-Liouville fractional integrals.

    Authors do not have conflict of interest.



    [1] N. T. Thomopoulos, Essentials of Monte Carlo simulation: Statistical methods for building simulation models, New York: Springer, 2013. https://doi.org/10.1007/978-1-4614-6022-0
    [2] J. W. Bang, R. E. Schumacker, P. L. Schlieve, Random-number generator validity in simulation studies: An investigation of normality, Educ. Psychol. Mea., 58 (1998), 430–450. https://doi.org/10.1177/0013164498058003005 doi: 10.1177/0013164498058003005
    [3] M. A. Schulz, B. Schmalbach, P. Brugger, K. Witt, Analysing humanly generated random number sequences: a pattern-based approach, PloS One, 7 (2012), e41531. https://doi.org/10.1371/journal.pone.0041531 doi: 10.1371/journal.pone.0041531
    [4] S. G. Tanyer, Random number generation with the method of uniform sampling: Very high goodness of fit and randomness, Eng. Let., 26 (2018), 23–31.
    [5] D. Kaya, S. A. Tuncer, Generating random numbers from biological signals in LabVIEW environment and statistical analysis, Trait. Signal, 36 (2019), 303–310. https://doi.org/10.18280/ts.360402 doi: 10.18280/ts.360402
    [6] I. Tanackov, F. Sinani, M. Stanković, V. Bogdanović1, Ž. Stević, M. Vidić, et al., Natural test for random numbers generator based on exponential distribution, Mathematics, 7 (2019), 920. https://doi.org/10.3390/math7100920 doi: 10.3390/math7100920
    [7] M. M. Jacak, P. Jóźwiak, J. Niemczuk. J. E. Jacak, Quantum generators of random numbers, Sci. Rep., 11 (2021), 16108. https://doi.org/10.1038/s41598-021-95388-7 doi: 10.1038/s41598-021-95388-7
    [8] M. S. Ridout, Generating random numbers from a distribution specified by its Laplace transform, Stat. Comput., 19 (2009), 439. https://doi.org/10.1007/s11222-008-9103-x doi: 10.1007/s11222-008-9103-x
    [9] W. Hörmann, J. Leydold, Generating generalized inverse Gaussian random variates, Stat. Comput., 24 (2014), 547–557. https://doi.org/10.1007/s11222-013-9387-3 doi: 10.1007/s11222-013-9387-3
    [10] N. B. Rached, A. Haji-Ali, G. Rubino, R. Tempone, Efficient importance sampling for large sums of independent and identically distributed random variables, Stat. Comput., 31 (2021), 79. https://doi.org/10.1007/s11222-021-10055-1 doi: 10.1007/s11222-021-10055-1
    [11] R. A. K. Sherwani, M. Aslam, M. A. Raza, M. Farooq, M. Abid, M. Tahir, Neutrosophic normal probability distribution—A spine of parametric neutrosophic statistical tests: aroperties and applications, In: Neutrosophic operational research, Cham: Springer, 2021,153–169. https://doi.org/10.1007/978-3-030-57197-9_8
    [12] W. Q. Duan, Z. Khan, M. Gulistan, A. Khurshid, Neutrosophic exponential distribution: Modeling and applications for complex data analysis, Complexity, 2021 (2021), 5970613. https://doi.org/10.1155/2021/5970613 doi: 10.1155/2021/5970613
    [13] R. A. Aliev, A. V. Alizadeh, O. H. Huseynov, K. I. Jabbarova, Z‐number‐based linear programming, Int. J. Intell. Syst., 30 (2015), 563–589. https://doi.org/10.1002/int.21709 doi: 10.1002/int.21709
    [14] R. Gao, D. A. Ralescu, Convergence in distribution for uncertain random variables, IEEE T. Fuzzy Syst., 26 (2018), 1427–1434. https://doi.org/10.1109/TFUZZ.2017.2724021 doi: 10.1109/TFUZZ.2017.2724021
    [15] S. Pirmuhammadi, T. Allahviranloo, M. Keshavarz, The parametric form of Z‐number and its application in Z‐number initial value problem, Int. J. Intell. Syst., 32 (2017), 1030–1061. https://doi.org/10.1002/int.21883 doi: 10.1002/int.21883
    [16] R. A. Aliev, W. Pedrycz, B. G. Guirimov, O. H. Huseynov, Acquisition of Z-number-valued clusters by using a new compound function, IEEE T. Fuzzy Syst., 30 (2020), 279–286. https://doi.org/10.1109/TFUZZ.2020.3037969 doi: 10.1109/TFUZZ.2020.3037969
    [17] S. D. Nguyen, V. S. T. Nguyen, N. T. Pham, Determination of the optimal number of clusters: A fuzzy-set based method, IEEE T. Fuzzy Syst., 30 (2022), 3514–3526. https://doi.org/10.1109/TFUZZ.2021.3118113 doi: 10.1109/TFUZZ.2021.3118113
    [18] P. Wang, W. Q. Chen, S. L. Lin, L. Y. Liu, Z. W. Sun, F. G. Zhang, Consensus algorithm based on verifiable quantum random numbers, Int. J. Intell. Syst., 37 (2022), 6857–6876.
    [19] M. Aslam, Truncated variable algorithm using DUS-neutrosophic Weibull distribution, Complex Intell. Syst., 9 (2023), 3107–3114. https://doi.org/10.1007/s40747-022-00912-5 doi: 10.1007/s40747-022-00912-5
    [20] M. Aslam, Simulating imprecise data: sine-cosine and convolution methods with neutrosophic normal distribution, J. Big Data, 10 (2023), 143. https://doi.org/10.1186/s40537-023-00822-4 doi: 10.1186/s40537-023-00822-4
    [21] M. Albassam, M. Ahsan-ul-Haq, M. Aslam, Weibull distribution under indeterminacy with applications, AIMS Mathematics, 8 (2023), 10745–10757. https://doi.org/10.3934/math.2023545 doi: 10.3934/math.2023545
    [22] M. Aslam, Testing average wind speed using sampling plan for Weibull distribution under indeterminacy, Sci. Rep., 11 (2021), 7532. https://doi.org/10.1038/s41598-021-87136-8 doi: 10.1038/s41598-021-87136-8
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