Research article

Prediction of diabetic patients in Iraq using binary dragonfly algorithm with long-short term memory neural network

  • Received: 14 July 2023 Revised: 30 August 2023 Accepted: 04 September 2023 Published: 18 September 2023
  • Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.

    Citation: Zaineb M. Alhakeem, Heba Hakim, Ola A. Hasan, Asif Ali Laghari, Awais Khan Jumani, Mohammed Nabil Jasm. Prediction of diabetic patients in Iraq using binary dragonfly algorithm with long-short term memory neural network[J]. AIMS Electronics and Electrical Engineering, 2023, 7(3): 217-230. doi: 10.3934/electreng.2023013

    Related Papers:

    [1] Ishfaq Mallah, Idris Ahmed, Ali Akgul, Fahd Jarad, Subhash Alha . On ψ-Hilfer generalized proportional fractional operators. AIMS Mathematics, 2022, 7(1): 82-103. doi: 10.3934/math.2022005
    [2] M. J. Huntul . Inverse source problems for multi-parameter space-time fractional differential equations with bi-fractional Laplacian operators. AIMS Mathematics, 2024, 9(11): 32734-32756. doi: 10.3934/math.20241566
    [3] Kanagaraj Muthuselvan, Baskar Sundaravadivoo, Kottakkaran Sooppy Nisar, Suliman Alsaeed . Discussion on iterative process of nonlocal controllability exploration for Hilfer neutral impulsive fractional integro-differential equation. AIMS Mathematics, 2023, 8(7): 16846-16863. doi: 10.3934/math.2023861
    [4] Zihan Yue, Wei Jiang, Boying Wu, Biao Zhang . A meshless method based on the Laplace transform for multi-term time-space fractional diffusion equation. AIMS Mathematics, 2024, 9(3): 7040-7062. doi: 10.3934/math.2024343
    [5] Junseok Kim . A normalized Caputo–Fabrizio fractional diffusion equation. AIMS Mathematics, 2025, 10(3): 6195-6208. doi: 10.3934/math.2025282
    [6] Lahcene Rabhi, Mohammed Al Horani, Roshdi Khalil . Existence results of mild solutions for nonlocal fractional delay integro-differential evolution equations via Caputo conformable fractional derivative. AIMS Mathematics, 2022, 7(7): 11614-11634. doi: 10.3934/math.2022647
    [7] Choukri Derbazi, Hadda Hammouche . Caputo-Hadamard fractional differential equations with nonlocal fractional integro-differential boundary conditions via topological degree theory. AIMS Mathematics, 2020, 5(3): 2694-2709. doi: 10.3934/math.2020174
    [8] Amjid Ali, Teruya Minamoto, Rasool Shah, Kamsing Nonlaopon . A novel numerical method for solution of fractional partial differential equations involving the ψ-Caputo fractional derivative. AIMS Mathematics, 2023, 8(1): 2137-2153. doi: 10.3934/math.2023110
    [9] Jagdev Singh, Jitendra Kumar, Devendra Kumar, Dumitru Baleanu . A reliable numerical algorithm for fractional Lienard equation arising in oscillating circuits. AIMS Mathematics, 2024, 9(7): 19557-19568. doi: 10.3934/math.2024954
    [10] Apassara Suechoei, Parinya Sa Ngiamsunthorn . Extremal solutions of φCaputo fractional evolution equations involving integral kernels. AIMS Mathematics, 2021, 6(5): 4734-4757. doi: 10.3934/math.2021278
  • Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.



    Ostrowski proved the following interesting and useful integral inequality in 1938, see [18] and [15, page:468].

    Theorem 1.1. Let f:IR, where IR is an interval, be a mapping differentiable in the interior I of I and let a,bI with a<b. If |f(x)|M for all x[a,b], then the following inequality holds:

    |f(x)1babaf(t)dt|M(ba)[14+(xa+b2)2(ba)2] (1.1)

    for all x[a,b]. The constant 14 is the best possible in sense that it cannot be replaced by a smaller one.

    This inequality gives an upper bound for the approximation of the integral average 1babaf(t)dt by the value of f(x) at point x[a,b]. In recent years, such inequalities were studied extensively by many researchers and numerous generalizations, extensions and variants of them appeared in a number of papers, see [1,2,10,11,19,20,21,22,23].

    A function  f:IRR is said to be convex (AAconvex) if the inequality

    f(tx+(1t)y)tf(x)+(1t)f(y)

    holds for all x,yI  and t[0,1].

    In [4], Anderson et al. also defined generalized convexity as follows:

    Definition 1.1. Let f:I(0,) be continuous, where I is subinterval of (0,). Let M and N be any two Mean functions. We say f is MN-convex (concave) if

    f(M(x,y))()N(f(x),f(y))

    for all x,yI.

    Recall the definitions of AGconvex functions, GGconvex functions and GA functions that are given in [16] by Niculescu:

    The AGconvex functions (usually known as logconvex functions) are those functions f:I(0,) for which

    x,yI and λ[0,1]f(λx+(1λ)y)f(x)1λf(y)λ, (1.2)

    i.e., for which logf is convex.

    The GGconvex functions (called in what follows multiplicatively convex functions) are those functions f:IJ (acting on subintervals of (0,)) such that

    x,yI and λ[0,1]f(x1λyλ)f(x)1λf(y)λ. (1.3)

    The class of all GAconvex functions is constituted by all functions f:IR (defined on subintervals of (0,)) for which

    x,yI and λ[0,1]f(x1λyλ)(1λ)f(x)+λf(y). (1.4)

    The article organized three sections as follows: In the first section, some definitions an preliminaries for Riemann-Liouville and new fractional conformable integral operators are given. Also, some Ostrowski type results involving Riemann-Liouville fractional integrals are in this section. In the second section, an identity involving new fractional conformable integral operator is proved. Further, new Ostrowski type results involving fractional conformable integral operator are obtained by using some inequalities on established lemma and some well-known inequalities such that triangle inequality, Hölder inequality and power mean inequality. After the proof of theorems, it is pointed out that, in special cases, the results reduce the some results involving Riemann-Liouville fractional integrals given by Set in [27]. Finally, in the last chapter, some new results for AG-convex functions has obtained involving new fractional conformable integrals.

    Let [a,b] (<a<b<) be a finite interval on the real axis R. The Riemann-Liouville fractional integrals Jαa+f and Jαbf of order αC ((α)>0) with a0 and b>0 are defined, respectively, by

    Jαa+f(x):=1Γ(α)xa(xt)α1f(t)dt(x>a;(α)>0) (1.5)

    and

    Jαbf(x):=1Γ(α)bx(tx)α1f(t)dt(x<b;(α)>0) (1.6)

    where Γ(t)=0exxt1dx is an Euler Gamma function.

    We recall Beta function (see, e.g., [28, Section 1.1])

    B(α,β)={10tα1(1t)β1dt((α)>0;(β)>0)Γ(α)Γ(β)Γ(α+β)             (α,βCZ0). (1.7)

    and the incomplete gamma function, defined for real numbers a>0 and x0 by

    Γ(a,x)=xetta1dt.

    For more details and properties concerning the fractional integral operators (1.5) and (1.6), we refer the reader, for example, to the works [3,5,6,7,8,9,14,17] and the references therein. Also, several new and recent results of fractional derivatives can be found in the papers [29,30,31,32,33,34,35,36,37,38,39,40,41,42].

    In [27], Set gave some Ostrowski type results involving Riemann-Liouville fractional integrals, as follows:

    Lemma 1.1. Let f:[a,b]R be a differentiable mapping on (a,b) with a<b. If fL[a,b], then for all x[a,b] and α>0 we have:

    (xa)α+(bx)αbaf(x)Γ(α+1)ba[Jαxf(a)+Jαx+f(b)]=(xa)α+1ba10tαf(tx+(1t)a)dt(bx)α+1ba10tαf(tx+(1t)b)dt

    where Γ(α) is Euler gamma function.

    By using the above lemma, he obtained some new Ostrowski type results involving Riemann-Liouville fractional integral operators, which will generalized via new fractional integral operators in this paper.

    Theorem 1.2. Let f:[a,b][0,)R be a differentiable mapping on (a,b) with a<b such that fL[a,b]. If |f| is sconvex in the second sense on [a,b] for some fixed s(0,1] and |f(x)|M, x[a,b], then the following inequality for fractional integrals with α>0 holds:

    |(xa)α+(bx)αbaf(x)Γ(α+1)ba[Jαxf(a)+Jαx+f(b)]|Mba(1+Γ(α+1)Γ(s+1)Γ(α+s+1))[(xa)α+1+(bx)α+1α+s+1]

    where Γ is Euler gamma function.

    Theorem 1.3. Let f:[a,b][0,)R be a differentiable mapping on (a,b) with a<b such that fL[a,b]. If |f|q is sconvex in the second sense on [a,b] for some fixed s(0,1],p,q>1 and |f(x)|M, x[a,b], then the following inequality for fractional integrals holds:

    |(xa)α+(bx)αbaf(x)Γ(α+1)ba[Jαxf(a)+Jαx+f(b)]|M(1+pα)1p(2s+1)1q[(xa)α+1+(bx)α+1ba]

    where 1p+1q=1, α>0 and Γ is Euler gamma function.

    Theorem 1.4. Let f:[a,b][0,)R be a differentiable mapping on (a,b) with a<b such that fL[a,b]. If |f|q is sconvex in the second sense on [a,b] for some fixed s(0,1],q1 and |f(x)|M, x[a,b], then the following inequality for fractional integrals holds:

    |(xa)α+(bx)αbaf(x)Γ(α+1)ba[Jαxf(a)+Jαx+f(b)]|M(1+α)11q(1+Γ(α+1)Γ(s+1)Γ(α+s+1))1q[(xa)α+1+(bx)α+1ba]

    where α>0 and Γ is Euler gamma function.

    Theorem 1.5. Let f:[a,b][0,)R be a differentiable mapping on (a,b) with a<b such that fL[a,b]. If |f|q is sconcave in the second sense on [a,b] for some fixed s(0,1],p,q>1, x[a,b], then the following inequality for fractional integrals holds:

    |(xa)α+(bx)αbaf(x)Γ(α+1)ba[Jαxf(a)+Jαx+f(b)]|2s1q(1+pα)1p(ba)[(xa)α+1|f(x+a2)|+(bx)α+1|f(b+x2)|]

    where 1p+1q=1, α>0 and Γ is Euler gamma function.

    Some fractional integral operators generalize the some other fractional integrals, in special cases, as in the following integral operator. Jarad et. al. [13] has defined a new fractional integral operator. Also, they gave some properties and relations between the some other fractional integral operators, as Riemann-Liouville fractional integral, Hadamard fractional integrals, generalized fractional integral operators etc., with this operator.

    Let βC,Re(β)>0, then the left and right sided fractional conformable integral operators has defined respectively, as follows;

    βaJαf(x)=1Γ(β)xa((xa)α(ta)αα)β1f(t)(ta)1αdt; (1.8)
    βJαbf(x)=1Γ(β)bx((bx)α(bt)αα)β1f(t)(bt)1αdt. (1.9)

    The results presented here, being general, can be reduced to yield many relatively simple inequalities and identities for functions associated with certain fractional integral operators. For example, the case α=1 in the obtained results are found to yield the same results involving Riemann-Liouville fractional integrals, given before, in literatures. Further, getting more knowledge, see the paper given in [12]. Recently, some studies on this integral operators appeared in literature. Gözpınar [13] obtained Hermite-Hadamard type results for differentiable convex functions. Also, Set et. al. obtained some new results for quasiconvex, some different type convex functions and differentiable convex functions involving this new operator, see [24,25,26]. Motivating the new definition of fractional conformable integral operator and the studies given above, first aim of this study is obtaining new generalizations.

    Lemma 2.1. Let f:[a,b]R be a differentiable function on (a,b) with a<b and fL[a,b]. Then the following equality for fractional conformable integrals holds:

    (xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]=(xa)αβ+1ba10(1(1t)αα)βf(tx+(1t)a)dt+(bx)αβ+1ba10(1(1t)αα)βf(tx+(1t)b)dt.

    where α,β>0 and Γ is Euler Gamma function.

    Proof. Using the definition as in (1.8) and (1.9), integrating by parts and and changing variables with u=tx+(1t)a and u=tx+(1t)b in

    I1=10(1(1t)αα)βf(tx+(1t)a)dt,I2=10(1(1t)αα)βf(tx+(1t)b)dt

    respectively, then we have

    I1=10(1(1t)αα)βf(tx+(1t)a)dt=(1(1t)αα)βf(tx+(1t)a)xa|10β10(1(1t)αα)β1(1t)α1f(tx+(1t)a)xadt=f(x)αβ(xa)βxa(1(xuxa)αα)β1(xuxa)α1f(u)xaduxa=f(x)αβ(xa)β(xa)αβ+1xa((xa)α(xu)αα)β1(xu)α1f(u)du=f(x)αβ(xa)Γ(β+1)(xa)αβ+1βJαxf(a),

    similarly

    I2=10(1(1t)αα)βf(tx+(1t)b)dt=f(x)αβ(bx)+Γ(β+1)(bx)αβ+1βxJαf(b)

    By multiplying I1 with (xa)αβ+1ba and I2 with (bx)αβ+1ba we get desired result.

    Remark 2.1. Taking α=1 in Lemma 2.1 is found to yield the same result as Lemma 1.1.

    Theorem 2.1. Let f:[a,b]R be a differentiable function on (a,b) with a<b and fL[a,b]. If |f| is convex on [a,b] and |f(x)|M with x[a,b], then the following inequality for fractional conformable integrals holds:

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|Mαβ+1B(1α,β+1)[(xa)αβ+1ba+(bx)αβ+1ba] (2.1)

    where α,β>0, B(x,y) and Γ are Euler beta and Euler gamma functions respectively.

    Proof. From Lemma 2.1 we can write

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(xa)αβ+1ba10(1(1t)αα)β|f(tx+(1t)a)|dt+(bx)αβ+1ba10(1(1t)αα)β|f(tx+(1t)b)|dt(xa)αβ+1ba[10(1(1t)αα)βt|f(x)|dt+10(1(1t)αα)β(1t)|f(a)|dt]+(bx)αβ+1ba[10(1(1t)αα)βt|f(x)|dt+10(1(1t)αα)β(1t)|f(b)|dt]. (2.2)

    Notice that

    10(1(1t)αα)βtdt=1αβ+1[B(1α,β+1)B(2α,β+1)],10(1(1t)αα)β(1t)dt=B(2α,β+1)αβ+1. (2.3)

    Using the fact that, |f(x)|M for x[a,b] and combining (2.3) with (2.2), we get desired result.

    Remark 2.2. Taking α=1 in Theorem 3.1 and s=1 in Theorem 1.2 are found to yield the same results.

    Theorem 2.2. Let f:[a,b]R be a differentiable function on (a,b) with a<b and fL[a,b]. If |f|q is convex on [a,b], p,q>1 and |f(x)|M with x[a,b], then the following inequality for fractional conformable integrals holds:

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|M[B(βp+1,1α)αβ+1]1p[(xa)αβ+1ba+(bx)αβ+1ba] (2.4)

    where 1p+1q=1, α,β>0, B(x,y) and Γ are Euler beta and Euler gamma functions respectively.

    Proof. By using Lemma 2.1, convexity of |f|q and well-known Hölder's inequality, we have

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(xa)αβ+1ba[(10(1(1t)αα)βp)1p(10|f(tx+(1t)a)|qdt)1q]+(bx)αβ+1ba[(10(1(1t)αα)βp)1p(10|f(tx+(1t)b)|qdt)1q]. (2.5)

    Notice that, changing variables with x=1(1t)α, we get

    10(1(1t)αα)βp=B(βp+1,1α)αβ+1. (2.6)

    Since |f|q is convex on [a,b] and |f|qMq, we can easily observe that,

    10|f(tx+(1t)a)|qdt10t|f(x)|qdt+10(1t)|f(a)|qdtMq. (2.7)

    As a consequence, combining the equality (2.6) and inequality (2.7) with the inequality (2.5), the desired result is obtained.

    Remark 2.3. Taking α=1 in Theorem 3.2 and s=1 in Theorem 1.3 are found to yield the same results.

    Theorem 2.3. Let f:[a,b]R be a differentiable function on (a,b) with a<b and fL[a,b]. If |f|q is convex on [a,b], q1 and |f(x)|M with x[a,b], then the following inequality for fractional conformable integrals holds:

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|Mαβ+1B(1α,β+1)[(xa)αβ+1ba+(bx)αβ+1ba] (2.8)

    where α,β>0, B(x,y) and Γ are Euler Beta and Euler Gamma functions respectively.

    Proof. By using Lemma 2.1, convexity of |f|q and well-known power-mean inequality, we have

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(xa)αβ+1ba(10(1(1t)αα)βdt)11q(10(1(1t)αα)β|f(tx+(1t)a)|qdt)1q+(bx)αβ+1ba(10(1(1t)αα)βdt)11q(10(1(1t)αα)β|f(tx+(1t)b)|qdt)1q. (2.9)

    Since |f|q is convex and |f|qMq, by using (2.3) we can easily observe that,

    10(1(1t)αα)β|f(tx+(1t)a)|qdt10(1(1t)αα)β[t|f(x)|q+(1t)|f(a)|q]dtMqαβ+1B(1α,β+1). (2.10)

    As a consequence,

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(xa)αβ+1ba(1αβ+1B(1α,β+1))11q(Mqαβ+1B(1α,β+1))1q+(bx)αβ+1ba(1αβ+1B(1α,β+1))11q(Mqαβ+1B(1α,β+1))1q=Mαβ+1B(1α,β+1)[(xa)αβ+1ba+(bx)αβ+1ba]. (2.11)

    This means that, the desired result is obtained.

    Remark 2.4. Taking α=1 in Theorem 3.2 and s=1 in Theorem 1.4 are found to yield the same results.

    Theorem 2.4. Let f:[a,b]R be a differentiable function on (a,b) with a<b and fL[a,b]. If |f|q is concave on [a,b], p,q>1 and |f(x)|M with x[a,b], then the following inequality for fractional conformable integrals holds:

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|[B(βp+1,1α)αβ+1]1p[(xa)αβ+1ba|f(x+a2)|+(bx)αβ+1ba|f(x+b2)|] (2.12)

    where 1p+1q=1, α,β>0, B(x,y) and Γ are Euler Beta and Gamma functions respectively.

    Proof. By using Lemma 2.1 and well-known Hölder's inequality, we have

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(xa)αβ+1ba[(10(1(1t)αα)βp)1p(10|f(tx+(1t)a)|qdt)1q]+(bx)αβ+1ba[(10(1(1t)αα)βp)1p(10|f(tx+(1t)b)|qdt)1q]. (2.13)

    Since |f|q is concave, it can be easily observe that,

    |f(tx+(1t)a)|qdt|f(x+a2)|,|f(tx+(1t)b)|qdt|f(b+x2)|. (2.14)

    Notice that, changing variables with x=1(1t)α, as in (2.6), we get,

    10(1(1t)αα)βp=B(βp+1,1α)αβ+1. (2.15)

    As a consequence, substituting (2.14) and (2.15) in (2.13), the desired result is obtained.

    Remark 2.5. Taking α=1 in Theorem 3.2 and s=1 in Theorem 1.5 are found to yield the same results.

    Some new inequalities for AG-convex functions has obtained in this chapter. For the simplicity, we will denote |f(x)||f(a)|=ω and |f(x)||f(b)|=ψ.

    Theorem 3.1. Let f:[a,b]R be a differentiable function on (a,b) with a<b and fL[a,b]. If |f| is AGconvex on [a,b], then the following inequality for fractional conformable integrals holds:

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]||f(a)|(xa)αβ+1αβ(ba)[ω1lnω(ωlnαβ1(ω)(Γ(αβ+1)Γ(αβ+1,lnω)))]+|f(b)|(bx)αβ+1αβ(ba)[ψ1lnψ(ψlnαβ1(ψ)(Γ(αβ+1)Γ(αβ+1,lnψ)))]

    where α>0,β>1, Re(lnω)<0Re(lnψ)<0Re(αβ)>1,B(x,y),Γ(x,y) and Γ are Euler Beta, Euler incomplete Gamma and Euler Gamma functions respectively.

    Proof. From Lemma 2.1 and definition of AGconvexity, we have

    (xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)](xa)αβ+1ba10(1(1t)αα)β|f(tx+(1t)a)|dt+(bx)αβ+1ba10(1(1t)αα)β|f(tx+(1t)b)|dt(xa)αβ+1ba[10(1(1t)αα)β|f(a)|(|f(x)||f(a)|)tdt]+(bx)αβ+1ba[10(1(1t)αα)β|f(b)|(|f(x)||f(b)|)tdt]. (3.1)

    By using the fact that |1(1t)α|β1|1t|αβ for α>0,β>1, we can write

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(xa)αβ+1αβ(ba)[10(1|1t|αβ)|f(a)|(|f(x)||f(a)|)tdt]+(bx)αβ+1αβ(ba)[10(1|1t|αβ)|f(b)|(|f(x)||f(b)|)tdt].

    By computing the above integrals, we get the desired result.

    Theorem 3.2. Let f:[a,b]R be a differentiable function on (a,b) with a<b and fL[a,b]. If |f|q is AGconvex on [a,b] and p,q>1, then the following inequality for fractional conformable integrals holds:

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(B(βp+1,1α)αβ+1)1p[|f(a)|(xa)αβ+1ba(ωq1qlnω)1q+|f(b)|(bx)αβ+1ba(ψq1qlnψ)1q].

    where 1p+1q=1, α,β>0, B(x,y) and Γ are Euler beta and Euler gamma functions respectively.

    Proof. By using Lemma 2.1, AGconvexity of |f|q and well-known Hölder's inequality, we can write

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(xa)αβ+1ba[(10(1(1t)αα)βp)1p(|f(a)|q10(|f(x)||f(a)|)qtdt)1q]+(bx)αβ+1ba[(10(1(1t)αα)βp)1p(|f(b)|q10(|f(x)||f(b)|)qtdt)1q].

    By a simple computation, one can obtain

    |(xa)αβ+(bx)αβ(ba)αβf(x)Γ(β+1)ba[βxJαf(b)+βJαxf(a)]|(B(βp+1,1α)αβ+1)1p×[|f(a)|(xa)αβ+1ba(ωq1qlnω)1q+|f(b)|(bx)αβ+1ba(ψq1qlnψ)1q].

    This completes the proof.

    Corollary 3.1. In our results, some new Ostrowski type inequalities can be derived by choosing |f|M. We omit the details.

    The authors declare that no conflicts of interest in this paper.



    [1] Iraq diabetes report 2000-2045. Available from: https://www.diabetesatlas.org/data/en/country/96/iq.html
    [2] Tigga NP, Garg S (2023) Speech Emotion Recognition for multiclass classification using Hybrid CNN-LSTM. International Journal of Microsystems and Iot 1: 9–17. https://doi.org/10.5281/zenodo.8158288 doi: 10.5281/zenodo.8158288
    [3] Jaber HA, Rashid MT (2019) HD-sEMG gestures recognition by SVM classifier for controlling prosthesis. Iraqi Journal of Computers, Communications, Control and System Engineering (IJCCCE) 19: 10–19. https://doi.org/10.33103/uot.ijccce.19.1.2 doi: 10.33103/uot.ijccce.19.1.2
    [4] Abgeena A, Garg S (2023) A novel convolution bi-directional gated recurrent unit neural network for emotion recognition in multichannel electroencephalogram signals. Technol Health Care 31: 1215–1234. https://doi.org/10.3233/THC-220458 doi: 10.3233/THC-220458
    [5] Mujumdar A, Vaidehi V (2019) Diabetes prediction using machine learning algorithms. Procedia Computer Science 165: 292–299. https://doi.org/10.1016/j.procs.2020.01.047 doi: 10.1016/j.procs.2020.01.047
    [6] Madan P, Singh V, Chaudhari V, Albagory Y, Dumka A, Singh R, et al. (2022) An optimization-based diabetes prediction model using cnn and bi-directional lstm in real-time environment. Applied Sciences 12: 3989. https://doi.org/10.3390/app12083989 doi: 10.3390/app12083989
    [7] Chang V, Bailey J, Xu QA, Sun Z (2023) Pima indians diabetes mellitus classification based on machine learning (ml) algorithms. Neural Computing and Applications 36: 16157–16173. https://doi.org/10.1007/s00521-022-07049-z doi: 10.1007/s00521-022-07049-z
    [8] Noori NA, Yassin AA (2021) A comparative analysis for diabetic prediction based on machine learning techniques. Journal of Basrah Researches (Sciences) 47.
    [9] Ahamed BS, Arya MS, Nancy AO (2022) Prediction of type-2 diabetes mellitus disease using machine learning classifiers and techniques. Frontiers in Computer Science 4: 835242. https://doi.org/10.1155/2022/9220560 doi: 10.1155/2022/9220560
    [10] Butt UM, Letchmunan S, Ali M, Hassan FH, Baqir A, Sherazi H (2021) Machine learning based diabetes classification and prediction for healthcare applications. Journal of healthcare engineering 2021. https://doi.org/10.1155/2021/9930985
    [11] Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H (2018) Predicting diabetes mellitus with machine learning techniques. Frontiers in genetics 9: 515. https://doi.org/10.3389/fgene.2018.00515 doi: 10.3389/fgene.2018.00515
    [12] Naz H, Ahuja S (2020) Deep learning approach for diabetes prediction using pima indian dataset. Journal of Diabetes and Metabolic Disorders 19: 391–403. https://doi.org/10.1007/s40200-020-00520-5 doi: 10.1007/s40200-020-00520-5
    [13] Olisah CC, Smith L, Smith M (2022) Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Comput Meth Prog Bio 220: 106773. https://doi.org/10.1016/j.cmpb.2022.106773 doi: 10.1016/j.cmpb.2022.106773
    [14] Rashid A (2020) Diabetes dataset, Mendeley Data.
    [15] Mirjalili S (2015) Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27: 1053–1073. https://doi.org/10.1007/s00521-015-1920-1 doi: 10.1007/s00521-015-1920-1
    [16] Mafarja MM, Eleyan D, Jaber I, Hammouri A, Mirjalili S (2017) Binary Dragonfly Algorithm for Feature Selection. 2017 International Conference on New Trends in Computing Sciences (ICTCS), 12–17.
    [17] Alhakeem ZM, Ali RS (2019) Fast channel selection method using crow search algorithm. Proceedings of the International Conference on Information and Communication Technology, 210–214. https://doi.org/10.1145/3321289.3321309
    [18] Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Computation 9: 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 doi: 10.1162/neco.1997.9.8.1735
    [19] Dalianis H (2018) Evaluation Metrics and Evaluation, Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-78503-5_6
  • This article has been cited by:

    1. Anjali Upadhyay, Surendra Kumar, The exponential nature and solvability of stochastic multi-term fractional differential inclusions with Clarke’s subdifferential, 2023, 168, 09600779, 113202, 10.1016/j.chaos.2023.113202
    2. Amadou Diop, Wei-Shih Du, Existence of Mild Solutions for Multi-Term Time-Fractional Random Integro-Differential Equations with Random Carathéodory Conditions, 2021, 10, 2075-1680, 252, 10.3390/axioms10040252
    3. Yong-Kui Chang, Jianguo Zhao, Some new asymptotic properties on solutions to fractional evolution equations in Banach spaces, 2021, 0003-6811, 1, 10.1080/00036811.2021.1969016
    4. Ahmad Al-Omari, Hanan Al-Saadi, António M. Lopes, Impulsive fractional order integrodifferential equation via fractional operators, 2023, 18, 1932-6203, e0282665, 10.1371/journal.pone.0282665
    5. Hiba El Asraoui, Ali El Mfadel, M’hamed El Omari, Khalid Hilal, Existence of mild solutions for a multi-term fractional differential equation via ψ-(γ,σ)-resolvent operators, 2023, 16, 1793-5571, 10.1142/S1793557123502121
    6. Zhiyuan Yuan, Luyao Wang, Wenchang He, Ning Cai, Jia Mu, Fractional Neutral Integro-Differential Equations with Nonlocal Initial Conditions, 2024, 12, 2227-7390, 1877, 10.3390/math12121877
    7. Jia Mu, Zhiyuan Yuan, Yong Zhou, Mild Solutions of Fractional Integrodifferential Diffusion Equations with Nonlocal Initial Conditions via the Resolvent Family, 2023, 7, 2504-3110, 785, 10.3390/fractalfract7110785
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1493) PDF downloads(119) Cited by(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog