In this article, Autism Spectrum Disorder (ASD) is discussed, with an emphasis placed on the multidimensional nature of the disorder, which is anchored in genetic and neurological components. Identifying genes related to ASD is essential to comprehend the mechanisms that underlie the illness, yet the condition's complexity has impeded precise information in this field. In ASD research, the analysis of gene expression data helps choose and categorize significant genes. The study used microarray data to provide a novel approach that integrated gene selection techniques with deep learning models to improve the accuracy of ASD prediction. It offered a detailed comparative examination of gene selection approaches and deep learning architectures, including singular value decompositions (SVD), principal component analyses (PCA), and convolutional neural networks (CNNs). This paper combines gene selection methods (PCA and SVD) with deep learning models (CNN) to improve ASD prediction. Compared to more traditional approaches, the study revealed that its integrated methodology was more effective in improving the accuracy of ASD prediction results through experimentation. There was a difference in the accuracy between the PCA-CNN model, which achieved 94.33% with a loss of 0.4312, and the SVD-CNN model, which achieved 92.21% with a loss less than or equal to 0.3354. These discoveries help in the development of more accurate diagnostic and prognostic tools for ASD, which is a complicated neurodevelopmental disorder. Additionally, they provide insights into the molecular pathways that underlie ASD.
Citation: Mahmoud M. Abdelwahab, Khamis A. Al-Karawi, H. E. Semary. Integrating gene selection and deep learning for enhanced Autisms' disease prediction: a comparative study using microarray data[J]. AIMS Mathematics, 2024, 9(7): 17827-17846. doi: 10.3934/math.2024867
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In this article, Autism Spectrum Disorder (ASD) is discussed, with an emphasis placed on the multidimensional nature of the disorder, which is anchored in genetic and neurological components. Identifying genes related to ASD is essential to comprehend the mechanisms that underlie the illness, yet the condition's complexity has impeded precise information in this field. In ASD research, the analysis of gene expression data helps choose and categorize significant genes. The study used microarray data to provide a novel approach that integrated gene selection techniques with deep learning models to improve the accuracy of ASD prediction. It offered a detailed comparative examination of gene selection approaches and deep learning architectures, including singular value decompositions (SVD), principal component analyses (PCA), and convolutional neural networks (CNNs). This paper combines gene selection methods (PCA and SVD) with deep learning models (CNN) to improve ASD prediction. Compared to more traditional approaches, the study revealed that its integrated methodology was more effective in improving the accuracy of ASD prediction results through experimentation. There was a difference in the accuracy between the PCA-CNN model, which achieved 94.33% with a loss of 0.4312, and the SVD-CNN model, which achieved 92.21% with a loss less than or equal to 0.3354. These discoveries help in the development of more accurate diagnostic and prognostic tools for ASD, which is a complicated neurodevelopmental disorder. Additionally, they provide insights into the molecular pathways that underlie ASD.
For a convex function σ:I⊆R→R on I with ν1,ν2∈I and ν1<ν2, the Hermite-Hadamard inequality is defined by [1]:
σ(ν1+ν22)≤1ν2−ν1∫ν2ν1σ(η)dη≤σ(ν1)+σ(ν2)2. | (1.1) |
The Hermite-Hadamard integral inequality (1.1) is one of the most famous and commonly used inequalities. The recently published papers [2,3,4] are focused on extending and generalizing the convexity and Hermite-Hadamard inequality.
The situation of the fractional calculus (integrals and derivatives) has won vast popularity and significance throughout the previous five decades or so, due generally to its demonstrated applications in numerous seemingly numerous and great fields of science and engineering [5,6,7].
Now, we recall the definitions of Riemann-Liouville fractional integrals.
Definition 1.1 ([5,6,7]). Let σ∈L1[ν1,ν2]. The Riemann-Liouville integrals Jϑν1+σ and Jϑν2−σ of order ϑ>0 with ν1≥0 are defined by
Jϑν1+σ(x)=1Γ(ϑ)∫xν1(x−η)ϑ−1σ(η)dη, ν1<x | (1.2) |
and
Jϑν2−σ(x)=1Γ(ϑ)∫ν2x(η−x)ϑ−1σ(η)dη, x<ν2, | (1.3) |
respectively. Here Γ(ϑ) is the well-known Gamma function and J0ν1+σ(x)=J0ν2−σ(x)=σ(x).
With a huge application of fractional integration and Hermite-Hadamard inequality, many researchers in the field of fractional calculus extended their research to the Hermite-Hadamard inequality, including fractional integration rather than ordinary integration; for example see [8,9,10,11,12,13,14,15,16,17,18,19,20,21].
In this paper, we consider the integral inequality of Hermite-Hadamard-Mercer type that relies on the Hermite-Hadamard and Jensen-Mercer inequalities. For this purpose, we recall the Jensen-Mercer inequality: Let 0<x1≤x2≤⋯≤xn and μ=(μ1,μ2,…,μn) nonnegative weights such that ∑ni=1μi=1. Then, the Jensen inequality [22,23] is as follows, for a convex function σ on the interval [ν1,ν2], we have
σ(n∑i=1μixi)≤n∑i=1μiσ(xi), | (1.4) |
where for all xi∈[ν1,ν2] and μi∈[0,1], (i=¯1,n).
Theorem 1.1 ([2,23]). If σ is convex function on [ν1,ν2], then
σ(ν1+ν2−n∑i=1μixi)≤σ(ν1)+σ(ν2)−n∑i=1μiσ(xi), | (1.5) |
for each xi∈[ν1,ν2] and μi∈[0,1], (i=¯1,n) with ∑ni=1μi=1. For some results related with Jensen-Mercer inequality, see [24,25,26].
In view of above indices, we establish new integral inequalities of Hermite-Hadamard-Mercer type for convex functions via the Riemann-Liouville fractional integrals in the current project. Particularly, we see that our results can cover the previous researches.
Theorem 2.1. For a convex function σ:[ν1,ν2]⊆R→R with x,y∈[ν1,ν2], we have:
σ(ν1+ν2−x+y2)≤2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]≤σ(ν1)+σ(ν2)−σ(x)+σ(y)2. | (2.1) |
Proof. By using the convexity of σ, we have
σ(ν1+ν2−u+v2)≤12[σ(ν1+ν2−u)+σ(ν1+ν2−v)], | (2.2) |
and above with u=1−η2x+1+η2y, v=1+η2x+1−η2y, where x,y∈[ν1,ν2] and η∈[0,1], leads to
σ(ν1+ν2−x+y2)≤12[σ(ν1+ν2−(1−η2x+1+η2y))+σ(ν1+ν2−(1+η2x+1−η2y))]. | (2.3) |
Multiplying both sides of (2.3) by ηϑ−1 and then integrating with respect to η over [0,1], we get
1ϑσ(ν1+ν2−x+y2)≤12[∫10ηϑ−1σ(ν1+ν2−(1−η2x+1+η2y))dη+∫10ηϑ−1σ(ν1+ν2−(1+η2x+1−η2y))dη]=12[2ϑ(y−x)ϑ∫ν1+ν2−x+y2ν1+ν2−y((ν1+ν2−x+y2)−w)ϑ−1σ(w)dw+2ϑ(y−x)ϑ∫ν1+ν2−xν1+ν2−x+y2(w−(ν1+ν2−x+y2))ϑ−1σ(w)dw]=2ϑ−1Γ(ϑ)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)], |
and thus the proof of first inequality in (2.1) is completed.
On the other hand, we have by using the Jensen-Mercer inequality:
σ(ν1+ν2−(1−η2x+1+η2y))≤σ(ν1)+σ(ν2)−(1−η2σ(x)+1+η2σ(y)) | (2.4) |
σ(ν1+ν2−(1+η2x+1−η2y))≤σ(ν1)+σ(ν2)−(1+η2σ(x)+1−η2σ(y)). | (2.5) |
Adding inequalities (2.4) and (2.5) to get
σ(ν1+ν2−(1−η2x+1+η2y))+σ(ν1+ν2−(1+η2x+1−η2y))≤2[σ(ν1)+σ(ν2)]−[σ(x)+σ(y)]. | (2.6) |
Multiplying both sides of (2.6) by ηϑ−1 and then integrating with respect to η over [0,1] to obtain
∫10ηϑ−1σ(ν1+ν2−(1−η2x+1+η2y))dη+∫10ηϑ−1σ(ν1+ν2−(1+η2x+1−η2y))dη≤2ϑ[σ(ν1)+σ(ν2)]−1ϑ[σ(x)+σ(y)]. |
By making use of change of variables and then multiplying by ϑ2, we get the second inequality in (2.1).
Remark 2.1. If we choose ϑ=1, x=ν1 and y=ν2 in Theorem 2.1, then the inequality (2.1) reduces to (1.1).
Corollary 2.1. Theorem 2.1 with
● ϑ=1 becomes [24, Theorem 2.1].
● x=ν1 and y=ν2 becomes:
σ(ν1+ν22)≤2ϑ−1Γ(ϑ+1)(ν2−ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2−σ(ν1+ν22)]≤σ(ν1)+σ(ν2)2, |
which is obtained by Mohammed and Brevik in [10].
The following Lemma linked with the left inequality of (2.1) is useful to obtain our next results.
Lemma 2.1. Let σ:[ν1,ν2]⊆R→R be a differentiable function on (ν1,ν2) and σ′∈L[ν1,ν2] with ν1≤ν2 and x,y∈[ν1,ν2]. Then, we have:
2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)=(y−x)4∫10ηϑ[σ′(ν1+ν2−(1−η2x+1+η2y))−σ′(ν1+ν2−(1+η2x+1−η2y))]dη. | (2.7) |
Proof. From right hand side of (2.7), we set
ϖ1−ϖ2:=∫10ηϑ[σ′(ν1+ν2−(1−η2x+1+η2y))−σ′(ν1+ν2−(1+η2x+1−η2y))]dη=∫10ηϑσ′(ν1+ν2−(1−η2x+1+η2y))dη−∫10ηϑσ′(ν1+ν2−(1+η2x+1−η2y))dη. | (2.8) |
By integrating by parts with w=ν1+ν2−(1−η2x+1+η2y), we can deduce:
ϖ1=−2(y−x)σ(ν1+ν2−y)+2ϑ(y−x)∫10ηϑ−1σ(ν1+ν2−(1−η2x+1+η2y))dη=−2(y−x)σ(ν1+ν2−y)+2ϑ+1ϑ(y−x)ϑ+1∫ν1+ν2−x+y2ν1+ν2−yσ((ν1+ν2−x+y2)−w)ϑ−1σ(w)dw=−2(y−x)σ(ν1+ν2−y)+2ϑ+1Γ(ϑ+1)(y−x)ϑ+1Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2). |
Similarly, we can deduce:
ϖ2=2y−xσ(ν1+ν2−x)−2ϑ+1Γ(ϑ+1)(y−x)ϑ+1Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2). |
By substituting ϖ1 and ϖ2 in (2.8) and then multiplying by (y−x)4, we obtain required identity (2.7).
Corollary 2.2. Lemma 2.1 with
● ϑ=1 becomes:
1y−x∫ν1+ν2−xν1+ν2−yσ(w)dw−σ(ν1+ν2−x+y2)=(y−x)4∫10η[σ′(ν1+ν2−(1−η2x+1+η2y))−σ′(ν1+ν2−(1+η2x+1−η2y))]dη. |
● ϑ=1, x=ν1 and y=ν2 becomes:
1ν2−ν1∫ν2ν1σ(w)dw−σ(ν1+ν22)=(ν2−ν1)4∫10η[σ′(ν1+ν2−(1−η2ν1+1+η2ν2))−σ′(ν1+ν2−(1+η2ν1+1−η2ν2))]dη. |
● x=ν1 and y=ν2 becomes:
2ϑ−1Γ(ϑ+1)(ν2−ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2−σ(ν1+ν22)]−σ(ν1+ν22)=(ν2−ν1)4∫10ηϑ[σ′(ν1+ν2−(1−η2ν1+1+η2ν2))−σ′(ν1+ν2−(1+η2ν1+1−η2ν2))]dη. |
Theorem 2.2. Let σ:[ν1,ν2]⊆R→R be a differentiable function on (ν1,ν2) and |σ′| is convex on [ν1,ν2] with ν1≤ν2 and x,y∈[ν1,ν2]. Then, we have:
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)2(1+ϑ)[|σ′(ν1)|+|σ′(ν2)|−|σ′(x)|+|σ′(y)|2]. | (2.9) |
Proof. By taking modulus of identity (2.7), we get
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4[∫10ηϑ|σ′(ν1+ν2−(1−η2x+1+η2y))|dη+∫10ηϑ|σ′(ν1+ν2−(1+η2x+1−η2y))|dη]. |
Then, by applying the convexity of |σ′| and the Jensen-Mercer inequality on above inequality, we get
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4[∫10ηϑ[|σ′(ν1)|+|σ′(ν2)|−(1+η2|σ′(x)|+1−η2)|σ′(y)|]dη+∫10ηϑ[|σ′(ν1)|+|σ′(ν2)|−(1−η2|σ′(x)|+1+η2)|σ′(y)|]dη]=(y−x)2(1+ϑ)[|σ′(ν1)|+|σ′(ν2)|−|σ′(x)|+|σ′(y)|2], |
which completes the proof of Theorem 2.2.
Corollary 2.3. Theorem 2.2 with
● ϑ=1 becomes:
|1y−x∫ν1+ν2−xν1+ν2−yσ(w)dw−σ(ν1+ν2−x+y2)|≤(y−x)4[|σ′(ν1)|+|σ′(ν2)|−|σ′(x)|+|σ′(y)|2]. |
● ϑ=1, x=ν1 and y=ν2 becomes [27, Theorem 2.2].
● x=ν1 and y=ν2 becomes:
|1ν2−ν1∫ν2ν1σ(w)dw−σ(ν1+ν22)|≤(ν2−ν1)4[|σ′(ν1)|+|σ′(ν2)|2]. |
Theorem 2.3. Let σ:[ν1,ν2]⊆R→R be a differentiable function on (ν1,ν2) and |σ′|q,q>1 is convex on [ν1,ν2] with ν1≤ν2 and x,y∈[ν1,ν2]. Then, we have:
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4p√ϑp+1[(|σ′(ν1)|q+|σ′(ν2)|q−(|σ′(x)|q+3|σ′(y)|q4))1q+(|σ′(ν1)|q+|σ′(ν2)|q−(3|σ′(x)|q+|σ′(y)|q4))1q], | (2.10) |
where 1p+1q=1.
Proof. By taking modulus of identity (2.7) and using Hölder's inequality, we get
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4(∫10ηϑp)1p{(∫10|σ′(ν1+ν2−(1−η2x+1+η2y))|qdη)1q+(∫10|σ′(ν1+ν2−(1+η2x+1−η2y))|qdη)1q}. |
Then, by applying the Jensen-Mercer inequality with the convexity of |σ′|q, we can deduce
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4(∫10ηϑp)1p{(∫10|σ′(ν1)|q+|σ′(ν2)|q−(1−η2|σ′(x)|q+1+η2|σ′(y)|q))1q+(∫10|σ′(ν1)|q+|σ′(ν2)|q−(1+η2|σ′(x)|q+1−η2|σ′(y)|q))1q}=(y−x)4p√ϑp+1[(|σ′(ν1)|q+|σ′(ν2)|q−(|σ′(x)|q+3|σ′(y)|q4))1q+(|σ′(ν1)|q+|σ′(ν2)|q−(3|σ′(x)|q+|σ′(y)|q4))1q], |
which completes the proof of Theorem 2.3.
Corollary 2.4. Theorem 2.3 with
● ϑ=1 becomes:
|1y−x∫ν1+ν2−xν1+ν2−yσ(w)dw−σ(ν1+ν2−x+y2)|≤(y−x)4p√p+1[(|σ′(ν1)|q+|σ′(ν2)|q−(|σ′(x)|q+3|σ′(y)|q4))1q+(|σ′(ν1)|q+|σ′(ν2)|q−(3|σ′(x)|q+|σ′(y)|q4))1q]. |
● ϑ=1, x=ν1 and y=ν2 becomes:
|1ν2−ν1∫ν2ν1σ(w)dw−σ(ν1+ν22)|≤(ν2−ν1)22p(1p+1)1p[|σ′(ν1)|+|σ′(ν2)|]. |
● x=ν1 and y=ν2 becomes:
|2ϑ−1Γ(ϑ+1)(ν2−ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2−σ(ν1+ν22)]−σ(ν1+ν22)|≤2ϑ−1−2qν2−ν1(1p+1)1p[|σ′(ν1)|+|σ′(ν2)|]. |
Theorem 2.4. Let σ:[ν1,ν2]⊆R→R be a differentiable function on (ν1,ν2) and |σ′|q,q≥1 is convex on [ν1,ν2] with ν1≤ν2 and x,y∈[ν1,ν2]. Then, we have:
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4(ϑ+1)[(|σ′(ν1)|q+|σ′(ν2)|q−(|σ′(x)|q+(2ϑ+3)|σ′(y)|q2(ϑ+2)))1q+(|σ′(ν1)|q+|σ′(ν2)|q−((2ϑ+3)|σ′(x)|q+|σ′(y)|q2(ϑ+2)))1q]. | (2.11) |
Proof. By taking modulus of identity (2.7) with the well-known power mean inequality, we can deduce
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4(∫10ηϑ)1−1q{(∫10ηϑ|σ′(ν1+ν2−(1−η2x+1+η2y))|qdη)1q+(∫10ηϑ|σ′(ν1+ν2−(1+η2x+1−η2y))|qdη)1q}. |
By applying the Jensen-Mercer inequality with the convexity of |σ′|q, we can deduce
|2ϑ−1Γ(ϑ+1)(y−x)ϑ[Jϑ(ν1+ν2−y)+σ(ν1+ν2−x+y2)+Jϑ(ν1+ν2−x)−σ(ν1+ν2−x+y2)]−σ(ν1+ν2−x+y2)|≤(y−x)4(∫10ηϑ)1−1q{(∫10ηϑ[|σ′(ν1)|q+|σ′(ν2)|q−(1−η2|σ′(x)|q+1+η2|σ′(y)|q)])1q+(∫10ηϑ[|σ′(ν1)|q+|σ′(ν2)|q−(1+η2|σ′(x)|q+1−η2|σ′(y)|q)])1q}=(y−x)4(ϑ+1)[(|σ′(ν1)|q+|σ′(ν2)|q−(|σ′(x)|q+(2ϑ+3)|σ′(y)|q2(ϑ+2)))1q+(|σ′(ν1)|q+|σ′(ν2)|q−((2ϑ+3)|σ′(x)|q+|σ′(y)|q2(ϑ+2)))1q], |
which completes the proof of Theorem 2.4.
Corollary 5. Theorem 2.4 with
● q=1 becomes Theorem 2.2.
● ϑ=1 becomes:
|1y−x∫ν1+ν2−xν1+ν2−yσ(w)dw−σ(ν1+ν2−x+y2)|≤(y−x)8[(|σ′(ν1)|q+|σ′(ν2)|q−(|σ′(x)|q+5|σ′(y)|q6))1q+(|σ′(ν1)|q+|σ′(ν2)|q−(5|σ′(x)|q+|σ′(y)|q6))1q]. |
● ϑ=1, x=ν1 and y=ν2 becomes:
|1ν2−ν1∫ν2ν1σ(w)dw−σ(ν1+ν22)|≤(y−x)8[(5|σ′(ν1)|q+|σ′(ν2)|q6)1q+(|σ′(ν1)|q+5|σ′(ν2)|q6)1q]. |
● x=ν1 and y=ν2 becomes:
|2ϑ−1Γ(ϑ+1)(ν2−ν1)ϑ[Jϑν1+σ(ν1+ν22)+Jϑν2−σ(ν1+ν22)]−σ(ν1+ν22)|≤(ν2−ν1)4(ϑ+1)[((2ϑ+3)|σ′(ν1)|q+|σ′(ν2)|q2(ϑ+2))1q+(|σ′(ν1)|q+(2ϑ+3)|σ′(ν2)|q2(ϑ+2))1q]. |
Here, we consider the following special means:
● The arithmetic mean:
A(ν1,ν2)=ν1+ν22,ν1,ν2≥0. |
● The harmonic mean:
H(ν1,ν2)=2ν1ν2ν1+ν2,ν1,ν2>0. |
● The logarithmic mean:
L(ν1,ν2)={ν2−ν1lnν2−lnν1,ifν1≠ν2,ν1,ifν1=ν2,ν1,ν2>0. |
● The generalized logarithmic mean:
Ln(ν1,ν2)={[νn+12−νn+11(n+1)(ν2−ν1)]1n,ifν1≠ν2ν1,ifν1=ν2,ν1,ν2>0;n∈Z∖{−1,0}. |
Proposition 3.1. Let 0<ν1<ν2 and n∈N, n≥2. Then, for all x,y∈[ν1,ν2], we have:
|Lnn(ν1+ν2−y,ν1+ν2−x)−(2A(ν1,ν2)−A(x,y))n|≤n(y−x)4[2A(νn−11,νn−12)−A(xn−1,yn−1)]. | (3.1) |
Proof. By applying Corollary 2.3 (first item) for the convex function σ(x)=xn,x>0, one can obtain the result directly.
Proposition 3.2. Let 0<ν1<ν2. Then, for all x,y∈[ν1,ν2], we have:
|L−1(ν1+ν2−y,ν1+ν2−x)−(2A(ν1,ν2)−A(x,y))−1|≤(y−x)4[2H−1(ν21,ν22)−H−1(x2,y2)]. | (3.2) |
Proof. By applying Corollary 2.3 (first item) for the convex function σ(x)=1x,x>0, one can obtain the result directly.
Proposition 3.3. Let 0<ν1<ν2 and n∈N, n≥2. Then, we have:
|Lnn(ν1,ν2)−An(ν1,ν2)|≤n(ν2−ν1)4[A(νn−11,νn−12)], | (3.3) |
and
|L−1(ν1,ν2)−A−1(ν1,ν2)|≤(ν2−ν1)4H−1(ν21,ν22). | (3.4) |
Proof. By setting x=ν1 and y=ν2 in results of Proposition 3.1 and Proposition 3.2, one can obtain the Proposition 3.3.
Proposition 3.4. Let 0<ν1<ν2 and n∈N, n≥2. Then, for q>1,1p+1q=1 and for all x,y∈[ν1,ν2], we have:
|Lnn(ν1+ν2−y,ν1+ν2−x)−(2A(ν1,ν2)−A(x,y))n|≤n(y−x)4p√p+1{[2A(νq(n−1)1,νq(n−1)2)−12A(xq(n−1),3yq(n−1))]1q+[2A(νq(n−1)1,νq(n−1)2)−12A(3xq(n−1),yq(n−1))]1q}. | (3.5) |
Proof. By applying Corollary 2.4 (first item) for convex function σ(x)=xn,x>0, one can obtain the result directly.
Proposition 3.5. Let 0<ν1<ν2. Then, for q>1,1p+1q=1 and for all x,y∈[ν1,ν2], we have:
|L−1(ν1+ν2−y,ν1+ν2−x)−(2A(ν1,ν2)−A(x,y))−1|≤q√2(y−x)4p√p+1{[H−1(ν2q1,ν2q2)−34H−1(x2q,3y2q)]1q+[H−1(ν2q1,ν2q2)−34H−1(3x2q,y2q)]1q}. | (3.6) |
Proof. By applying Corollary 2.4 (first item) for the convex function σ(x)=1x,x>0, one can obtain the result directly.
Proposition 3.6. Let 0<ν1<ν2 and n∈N, n≥2. Then, for q>1 and 1p+1q=1, we have:
|Lnn(ν1,ν2)−An(ν1,ν2)|≤n(ν2−ν1)4p√p+1{[2A(νq(n−1)1,νq(n−1)2)−12A(νq(n−1)1,3νq(n−1)2)]1q+[2A(νq(n−1)1,νq(n−1)2)−12A(3νq(n−1)1,νq(n−1)2)]1q}, | (3.7) |
and
|L−1(ν1,ν2)−A−1(ν1,ν2)|≤q√2(ν2−ν1)4p√p+1{[H−1(ν2q1,ν2q2)−34H−1(ν2q1,3ν2q2)]1q+[H−1(ν2q1,ν2q2)−34H−1(3ν2q1,ν2q2)]1q}. | (3.8) |
Proof. By setting x=ν1 and y=ν2 in results of Proposition 3.4 and Proposition 3.5, one can obtain the Proposition 3.6.
As we emphasized in the introduction, integral inequality is the most important field of mathematical analysis and fractional calculus. By using the well-known Jensen-Mercer and power mean inequalities, we have proved new inequalities of Hermite-Hadamard-Mercer type involving Riemann-Liouville fractional operators. In the last section, we have considered some propositions in the context of special functions; these confirm the efficiency of our results.
We would like to express our special thanks to the editor and referees. Also, the first author would like to thank Prince Sultan University for funding this work through research group Nonlinear Analysis Methods in Applied Mathematics (NAMAM) group number RG-DES-2017-01-17.
The authors declare no conflict of interest.
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