The utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis of lung diseases particularly, as it was reported that one of the very few diseases greatly affecting human beings was related to lungs. This research study, as presented in the paper, introduces an advanced computer-assisted model that is specifically tailored for the classification of 13 lung diseases using deep learning techniques, with a focus on analyzing chest radiograph images. The work flows from data collection, image quality enhancement, feature extraction to a comparative classification performance analysis. For data collection, an open-source data set consisting of 112,000 chest X-Ray images was used. Since, the quality of the pictures was significant for the work, enhanced image quality is achieved through preprocessing techniques such as Otsu-based binary conversion, contrast limited adaptive histogram equalization-driven noise reduction, and Canny edge detection. Feature extraction incorporates connected regions, histogram of oriented gradients, gray-level co-occurrence matrix and Haar wavelet transformation, complemented by feature selection via regularized neighbourhood component analysis. The paper proposes an optimized hybrid model, improved Aquila optimization convolutional neural networks (CNN), which is a combination of optimized CNN and DENSENET121 with applied batch equalization, which provides novelty for the model compared with other similar works. The comparative evaluation of classification performance among CNN, DENSENET121 and the proposed hybrid model is also done to find the results. The findings highlight the proposed hybrid model's supremacy, boasting 97.00% accuracy, 94.00% precision, 96.00% sensitivity, 96.00% specificity and 95.00% F1-score. In the future, potential avenues encompass exploring explainable machine learning for discerning model decisions and optimizing performance through strategic model restructuring.
Citation: Binju Saju, Neethu Tressa, Rajesh Kumar Dhanaraj, Sumegh Tharewal, Jincy Chundamannil Mathew, Danilo Pelusi. Effective multi-class lungdisease classification using the hybridfeature engineering mechanism[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 20245-20273. doi: 10.3934/mbe.2023896
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The utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis of lung diseases particularly, as it was reported that one of the very few diseases greatly affecting human beings was related to lungs. This research study, as presented in the paper, introduces an advanced computer-assisted model that is specifically tailored for the classification of 13 lung diseases using deep learning techniques, with a focus on analyzing chest radiograph images. The work flows from data collection, image quality enhancement, feature extraction to a comparative classification performance analysis. For data collection, an open-source data set consisting of 112,000 chest X-Ray images was used. Since, the quality of the pictures was significant for the work, enhanced image quality is achieved through preprocessing techniques such as Otsu-based binary conversion, contrast limited adaptive histogram equalization-driven noise reduction, and Canny edge detection. Feature extraction incorporates connected regions, histogram of oriented gradients, gray-level co-occurrence matrix and Haar wavelet transformation, complemented by feature selection via regularized neighbourhood component analysis. The paper proposes an optimized hybrid model, improved Aquila optimization convolutional neural networks (CNN), which is a combination of optimized CNN and DENSENET121 with applied batch equalization, which provides novelty for the model compared with other similar works. The comparative evaluation of classification performance among CNN, DENSENET121 and the proposed hybrid model is also done to find the results. The findings highlight the proposed hybrid model's supremacy, boasting 97.00% accuracy, 94.00% precision, 96.00% sensitivity, 96.00% specificity and 95.00% F1-score. In the future, potential avenues encompass exploring explainable machine learning for discerning model decisions and optimizing performance through strategic model restructuring.
It is well known that the classical boundary conditions cannot describe certain peculiarities of physical, chemical, or other processes occurring within the domain. In order to overcome this situation, the concept of nonlocal conditions was introduced by Bicadze and Samarskiĭ [1]. These conditions are successfully employed to relate the changes happening at nonlocal positions or segments within the given domain to the values of the unknown function at end points or boundary of the domain. For a detailed account of nonlocal boundary value problems, for example, we refer the reader to the articles [2,3,4,5,6] and the references cited therein.
Computational fluid dynamics (CFD) technique directly deals with the boundary data [7]. In case of fluid flow problems, the assumption of circular cross-section is not justifiable for curved structures. The idea of integral boundary conditions serves as an effective tool to describe the boundary data on arbitrary shaped structures. One can find application of integral boundary conditions in the study of thermal conduction, semiconductor, and hydrodynamic problems [8,9,10]. In fact, there are numerous applications of integral boundary conditions in different disciplines such as chemical engineering, thermoelasticity, underground water flow, population dynamics, etc. [11,12,13]. Also, integral boundary conditions facilitate to regularize ill-posed parabolic backward problems, for example, mathematical models for bacterial self-regularization [14]. Some recent results on boundary value problems with integral boundary conditions can be found in the articles [15,16,17,18,19] and the references cited therein.
The non-uniformities in form of points or sub-segments on the heat sources can be relaxed by using the integro multi-point boundary conditions, which relate the sum of the values of the unknown function (e.g., temperature) at the nonlocal positions (points and sub-segments) and the value of the unknown function over the given domain. Such conditions also find their utility in the diffraction problems when scattering boundary consists of finitely many sub-strips (finitely many edge-scattering problems). For details and applications in engineering problems, for instance, see [20,21,22,23].
The subject of fractional calculus has emerged as an important area of research in view of extensive applications of its tools in scientific and technical disciplines. Examples include neural networks [24,25], immune systems [26], chaotic synchronization [27,28], Quasi-synchronization [29,30], fractional diffusion [31,32,33], financial economics [34], ecology [35], etc. Inspired by the popularity of this branch of mathematical analysis, many researchers turned to it and contributed to its different aspects. In particular, fractional order boundary value problems received considerable attention. For some recent results on fractional differential equations with multi-point and integral boundary conditions, see [36,37]. More recently, in [38,39], the authors analyzed boundary value problems involving Riemann-Liouville and Caputo fractional derivatives respectively. A boundary value problem involving a nonlocal boundary condition characterized by a linear functional was studied in [40]. In a recent paper [41], the existence results for a dual anti-periodic boundary value problem involving nonlinear fractional integro-differential equations were obtained.
On the other hand, fractional differential systems also received considerable attention as such systems appear in the mathematical models associated with physical and engineering processes [42,43,44,45,46]. For theoretical development of such systems, for instance, see the articles [47,48,49,50,51,52].
Motivated by aforementioned applications of nonlocal integral boundary conditions and fractional differential systems, in this paper, we study a nonlinear mixed-order coupled fractional differential system equipped with a new set of nonlocal multi-point integral boundary conditions on an arbitrary domain given by
{cDξa+x(t)=φ(t,x(t),y(t)),0<ξ≤1,t∈[a,b],cDζa+y(t)=ψ(t,x(t),y(t)),1<ζ≤2,t∈[a,b],px(a)+qy(b)=y0+x0∫ba(x(s)+y(s))ds,y(a)=0,y′(b)=m∑i=1δix(σi)+λ∫bτx(s)ds,a<σ1<σ2<…<σm<τ<b, | (1.1) |
where cDχ is Caputo fractional derivative of order χ∈{ξ,ζ},φ,ψ:[a,b]×R×R→R are given functions, p,q,δi,x0,y0∈R,i=1,2,…,m.
Here we emphasize that the novelty of the present work lies in the fact that we introduce a coupled system of fractional differential equations of different orders on an arbitrary domain equipped with coupled nonlocal multi-point integral boundary conditions. It is imperative to notice that much of the work related to the coupled systems of fractional differential equations deals with the fixed domain. Thus our results are more general and contribute significantly to the existing literature on the topic. Moreover, several new results appear as special cases of the work obtained in this paper.
We organize the rest of the paper as follows. In Section 2, we present some basic concepts of fractional calculus and solve the linear version of the problem (1.1). Section 3 contains the main results. Examples illustrating the obtained results are presented in Section 4. Section 5 contains the details of a variant problem. The paper concludes with some interesting observations.
Let us recall some definitions from fractional calculus related to our study [53].
Definition 2.1. The Riemann–Liouville fractional integral of order α∈R (α>0) for a locally integrable real-valued function ϱ of order α∈R, denoted by Iαa+ϱ, is defined as
Iαa+ϱ(t)=(ϱ∗tα−1Γ(α))(t)=1Γ(α)t∫a(t−s)α−1ϱ(s)ds,−∞≤a<t<b≤+∞, |
where Γ denotes the Euler gamma function.
Definition 2.2. The Riemann–Liouville fractional derivative Dαa+ϱ of order α∈]m−1,m],m∈N is defined as
Dαa+ϱ(t)=dmdtmI1−αa+ϱ(t)=1Γ(m−α)dmdtmt∫a(t−s)m−1−αϱ(s)ds,−∞≤a<t<b≤+∞, |
while the Caputo fractional derivative cDαa+u is defined as
cDαa+ϱ(t)=Dαa+[ϱ(t)−ϱ(a)−ϱ′(a)(t−a)1!−…−ϱ(m−1)(a)(t−a)m−1(m−1)!], |
for ϱ,ϱ(m)∈L1[a,b].
Remark 2.1. The Caputo fractional derivative cDαa+ϱ is also defined as
cDαϱ(t)=1Γ(m−α)∫t0(t−s)m−α−1ϱ(m)(s)ds. |
In the following lemma, we obtain the integral solution of the linear variant of the problem (1.1).
Lemma 2.1. Let Φ,Ψ∈C([a,b],R). Then the unique solution of the system
{cDξa+x(t)=Φ(t),0<ξ≤1,t∈[a,b],cDζa+y(t)=Ψ(t),1<ζ≤2,t∈[a,b],px(a)+qy(b)=y0+x0∫ba(x(s)+y(s))ds,y(a)=0,y′(b)=m∑i=1δix(σi)+λ∫bτx(s)ds,a<σ1<σ2<…<σm<τ<b, | (2.1) |
is given by a pair of integral equations
x(t)=Iξa+Φ(t)+1Δ{y0+x0∫ba(b−s)ξΓ(ξ+1)Φ(s)ds+∫ba(x0(b−s)ζΓ(ζ+1)+ε1(b−s)ζ−2Γ(ζ−1)−q(b−s)ξ−1Γ(ξ))Ψ(s)ds−ε1m∑i=1δi∫σia(σi−s)ξ−1Γ(ξ)Φ(s)ds−ε1λ∫bτ∫sa(s−u)ξ−1Γ(ξ)Φ(u)duds}, | (2.2) |
y(t)=Iζa+Ψ(t)+(t−a)Δ{ε2y0+ε2x0∫ba(b−s)ξΓ(ξ+1)Φ(s)ds+∫ba(ε2x0(b−s)ζΓ(ζ+1)−ε2q(b−s)ξ−1Γ(ξ)−ε3(b−s)ζ−2Γ(ζ−1))Ψ(s)ds+ε3m∑i=1δi∫σia(σi−s)ξ−1Γ(ξ)Φ(s)ds+ε3λ∫bτ∫sa(s−u)ξ−1Γ(ξ)Φ(u)duds}, | (2.3) |
where
ε1=q(b−a)−x0(b−a)22,ε2=m∑i=1δi+λ(b−τ),ε3=p−(b−a)x0, | (2.4) |
and it is assumed that
Δ=ε3+ε2ε1≠0. | (2.5) |
Proof. Applying the integral operators Iξa+ and Iζa+ respectively on the first and second fractional differential equations in (2.1), we obtain
x(t)=Iξa+Φ(t)+c1andy(t)=Iζa+Ψ(t)+c2+c3(t−a), | (2.6) |
where ci∈R,i=1,2,3 are arbitrary constants. Using the condition y(a)=0 in (2.6), we get c2=0. Making use of the conditions px(a)+qy(b)=y0+x0∫ba(x(s)+y(s))ds and y′(b)=∑mi=1δix(σi)+λ∫bτx(s)ds in (2.6) after inserting c2=0 in it leads to the following system of equations in the unknown constants c1 and c3:
(p−(b−a)x0)c1+(q(b−a)−x0(b−a)22)c3=y0+x0∫ba(b−r)ξΓ(ξ+1)Φ(r)dr+x0∫ba(b−r)ζΓ(ζ+1)Ψ(r)dr−qIζa+Ψ(b), | (2.7) |
(m∑i=1δi+λ(b−τ))c1−c3=Iζ−1a+Ψ(b)−m∑i=1δiIξa+Φ(σi)−λ∫bτIξa+Φ(s)ds. | (2.8) |
Solving (2.7) and (2.8) for c1 and c3 and using the notation (2.5), we find that
c1=1Δ{ε1(Iζ−1a+Ψ(b)−m∑i=1δiIξa+Φ(σi)−λ∫bτIξa+Φ(s)ds)+y0+x0∫ba(b−r)ξΓ(ξ+1)Φ(r)dr+x0∫ba(b−r)ζΓ(ζ+1)Ψ(r)dr−qIξa+Ψ(b)},c3=1Δ{ε2(y0+∫ba(b−r)ξΓ(ξ+1)Φ(r)dr+x0∫ba(b−r)ζΓ(ζ+1)Ψ(r)dr−qIξa+Ψ(b))−ε3(Iζ−1a+Ψ(b)−m∑i=1δiIξa+Φ(σi)−λ∫bτIξa+Φ(s)ds)}. |
Inserting the values of c1,c2, and c3 in (2.6) leads to the solution (2.2) and (2.3). One can obtain the converse of the lemma by direct computation. This completes the proof.
Let X=C([a,b],R) be a Banach space endowed with the norm ‖x‖=sup{|x(t)|,t∈[a,b]}.
In view of Lemma 2.1, we define an operator T:X×X→X by:
T(x(t),y(t))=(T1(x(t),y(t)),T2(x(t),y(t))), |
where (X×X,‖(x,y)‖) is a Banach space equipped with norm ‖(x,y)‖=‖x‖+‖y‖,x,y∈X,
T1(x,y)(t)=Iξa+φ(t,x(t),y(t))+1Δ(y0+x0∫ba(b−s)ξΓ(ξ+1)φ(s,x(s),y(s))ds+∫baρ1(s)ψ(s,x(s),y(s))ds−ε1m∑i=1δi∫σia(σi−s)ξ−1Γ(ξ)φ(s,x(s),y(s))ds−ε1λ∫bτ∫sa(s−u)ξ−1Γ(ξ)φ(u,x(u),y(u))duds),T2(x,y)(t)=Iζa+ψ(t,x(t),y(t))+(t−a)Δ(ε2y0+ε2x0∫ba(b−s)ξΓ(ξ+1)φ(s,x(s),y(s))ds+∫baρ2(s)ψ(s,x(s),y(s))ds+ε3m∑i=1δi∫σia(σi−s)ξ−1Γ(ξ)φ(s,x(s),y(s))ds+ε3λ∫bτ∫sa(s−u)ξ−1Γ(ξ)φ(u,x(u),y(u))duds), |
ρ1(s)=x0(b−s)ζΓ(ζ+1)+ε1(b−s)ζ−2Γ(ζ−1)−q(b−s)ξ−1Γ(ξ),ρ2(s)=ε2x0(b−s)ζΓ(ζ+1)−ε2q(b−s)ξ−1Γ(ξ)−ε3(b−s)ζ−2Γ(ζ−1). |
For computational convenience we put:
L1=(b−a)ξΓ(ξ+1)+1|Δ|(|x0|(b−a)ξ+1Γ(ξ+2)+|ε1|m∑i=1|δi|(σi−a)ξΓ(ξ+1)+|ε1λ||(b−a)ξ+1−(τ−a)ξ+1|Γ(ξ+2)),M1=1|Δ|(|x0|(b−a)ζ+1Γ(ζ+2)+|ε1|(b−a)ζ−1Γ(ζ)+|q|(b−a)ξΓ(ξ+1)),L2=(b−a)|Δ|(|ε2x0|(b−a)ξ+1Γ(ξ+2)+|ε3|m∑i=1|δi|(σi−a)ξΓ(ξ+1)+|ε3λ||(b−a)ξ+1−(τ−a)ξ+1|Γ(ξ+2)),M2=(b−a)ζΓ(ζ+1)+b−a|Δ|(|ε2x0|(b−a)ζ+1Γ(ζ+2)+|ε2q|(b−a)ξΓ(ξ+1)+|ε3|(b−a)ζ−1Γ(ζ)). | (3.1) |
Our first existence result for the system (1.1) relies on Leray-Schauder alternative [54].
Theorem 3.1. Assume that:
(H1)φ,ψ:[a,b]×R×R→R are continuous functions and there exist real constants ki,γi≥0,(i=1,2) and k0>0,γ0>0 such that ∀x,y∈R,
|φ(t,x,y)|≤k0+k1|x|+k2|y|,|ψ(t,x,y)|≤γ0+γ1|x|+γ2|y|. |
Then there exists at least one solution for the system (1.1) on [a,b] if
(L1+L2)k1+(M1+M2)γ1<1and(L1+L2)k2+(M1+M2)γ2<1, | (3.2) |
where Li,Mi,i=1,2 are given by (3.1).
Proof. Let us note that continuity of the functions φ and ψ implies that of the operator T:X×X→X×X. Next, let Ω⊂X×X be bounded such that
|φ(t,x(t),y(t))|≤K1,|ψ(t,x(t),y(t))|≤K2,∀(x,y)∈Ω, |
for positive constants K1 and K2. Then for any (x,y)∈Ω, we have
|T1(x,y)(t)|≤Iξa+|φ(t,x(t),y(t))|+1|Δ|(|y0|+|x0|∫ba(b−s)ξΓ(ξ+1)|φ(s,x(s),y(s))|ds+∫ba|ρ1(s)||ψ(s,x(s),y(s))|ds+|ε1|m∑i=1|δi|∫σia(σi−s)ξ−1Γ(ξ)|φ(s,x(s),y(s))|ds+|ε1λ|∫bτ∫sa(s−u)ξ−1Γ(ξ)|φ(u,x(u),y(u))|duds)≤|y0||Δ|+{(b−a)ξΓ(ξ+1)+1|Δ|(|x0|(b−a)ξ+1Γ(ξ+2)+|ε1|m∑i=1|δi|(σi−a)ξΓ(ξ+1)+|ε1λ||(b−a)ξ+1−(τ−a)ξ+1|Γ(ξ+2))}K1+{1|Δ|(|x0|(b−a)ζ+1Γ(ζ+2)+|ε1|(b−a)ζ−1Γ(ζ)+|q|(b−a)ξΓ(ξ+1))}K2=|y0||Δ|+L1K1+M1K2, | (3.3) |
which implies that
‖T1(x,y)‖≤|y0||Δ|+L1K1+M1K2. |
In a similar manner, one can obtain that
‖T2(x,y)‖≤|ε2y0|(b−a)|Δ|+L2K1+M2K2. |
In consequence, the operator T is uniformly bounded as
‖T(x,y)‖≤|y0||Δ|+|ε2y0|(b−a)|Δ|+(L1+L2)K1+(M1+M2)K2. |
Now we show that T is equicontinuous. Let t1,t2∈[a,b] with t1<t2. Then we have
|T1(x(t2),y(t2))−T1(x(t1),y(t1))|≤K1|1Γ(ξ)∫t2a(t2−s)ξ−1ds−1Γ(ξ)∫t1a(t1−s)ξ−1ds|≤K1{1Γ(ξ)∫t1a[(t2−s)ξ−1−(t1−s)ξ−1]ds+1Γ(ξ)∫t2t1(t2−s)ξ−1ds}≤K1Γ(ξ+1)[2(t2−t1)ξ+|tξ2−tξ1|]. | (3.4) |
Analogously, we can obtain
|T2(x(t2),y(t2))−T2(x(t1),y(t1))|≤K2Γ(ζ+1)[2(t2−t1)ζ+|tζ2−tζ1|]+|t2−t1||Δ|{|ε2x0|(b−a)ξ+1Γ(ξ+2)K1+(|ε2x0|(b−a)ζ+1Γ(ζ+2)+|ε2q|(b−a)ξΓ(ξ+1)+|ε3|(b−a)ζ−1Γ(ζ))K2+|ε3|m∑i=1|δi|(σ1−b)ξΓ(ξ+1)K1+|ε3λ||(b−a)ξ+1−(τ−a)ξ+1|Γ(ξ+2)K1}. |
From the preceding inequalities, it follows that the operator T(x,y) is equicontinuous. Thus the operator T(x,y) is completely continuous.
Finally, we consider the set P={(x,y)∈X×X:(x,y)=νT(x,y),0≤ν≤1} and show that it is bounded.
Let (x,y)∈P with (x,y)=νT(x,y). For any t∈[a,b], we have x(t)=νT1(x,y)(t),y(t)=νT2(x,y)(t). Then by (H1) we have
|x(t)|≤|y0||Δ|+L1(k0+k1|x|+k2|y|)+M1(γ0+γ1|x|+γ2|y|)=|y0||Δ|+L1k0+M1γ0+(L1k1+M1γ1)|x|+(L1k2+M1γ2)|y|, |
and
|y(t)|≤|ε2y0|(b−a)|Δ|+L2(k0+k1|x|+k2|y|)+M2(γ0+γ1|x|+γ2|y|)=|ε2y0|(b−a)|Δ|+L2k0+M2γ0+(L2k1+M2γ1)|x|+(L2k2+M2γ2)|y|. |
In consequence of the above inequalities, we deduce that
‖x‖≤|y0||Δ|+L1k0+M1γ0+(L1k1+M1γ1)‖x‖+(L1k2+M1γ2)‖y‖, |
and
‖y‖≤|ε2y0|(b−a)|Δ|+L2k0+M2γ0+(L2k1+M2γ1)‖x‖+(L2k2+M2γ2)‖y‖, |
which imply that
‖x‖+‖y‖≤|y0||Δ|+|ε2y0|(b−a)|Δ|+(L1+L2)k0+(M1+M2)γ0+[(L1+L2)k1+(M1+M2)γ1]‖x‖+[(L1+L2)k2+(M1+M2)γ2]‖y‖. |
Thus
‖(x,y)‖≤1M0[|y0||Δ|+|ε2y0|(b−a)|Δ|+(L1+L2)k0+(M1+M2)γ0], |
where M0=min{1−[(L1+L2)k1+(M1+M2)γ1],1−[(L1+L2)k2+(M1+M2)γ2]}. Hence the set P is bounded. As the hypothesis of Leray-Schauder alternative [54] is satisfied, we conclude that the operator T has at least one fixed point. Thus the problem (1.1) has at least one solution on [a,b].
By using Banach's contraction mapping principle we prove in the next theorem the existence of a unique solution of the system (1.1).
Theorem 3.2. Assume that:
(H2)φ,ψ:[a,b]×R×R→R are continuous functions and there exist positive constants l1 and l2 such that for all t∈[a,b] and xi,yi∈R,i=1,2, we have
|φ(t,x1,x2)−φ(t,y1,y2)|≤l1(|x1−y1|+|x2−y2|), |
|ψ(t,x1,x2)−ψ(t,y1,y2)|≤l2(|x1−y1|+|x2−y2|). |
If
(L1+L2)l1+(M1+M2)l2<1, | (3.5) |
where Li,Mi,i=1,2 are given by (3.1) then the system (1.1) has a unique solution on [a,b].
Proof. Define supt∈[a,b]φ(t,0,0)=N1<∞, supt∈[a,b]ψ(t,0,0)=N2<∞ and r>0 such that
r>(|y0|/|Δ|)(1+(b−a)|ε2|)+(L1+L2)N1+(M1+M2)N21−(L1+L2)l1−(M1+M2)l2. |
Let us first show that TBr⊂Br, where Br={(x,y)∈X×X:‖(x,y)‖≤r}. By the assumption (H2), for (x,y)∈Br,t∈[a,b], we have
|φ(t,x(t),y(t))|≤|φ(t,x(t),y(t))−φ(t,0,0)|+|φ(t,0,0)|≤l1(|x(t)|+|y(t)|)+N1≤l1(‖x‖+‖y‖)+N1≤l1r+N1. | (3.6) |
Similarly, we can get
|ψ(t,x(t),y(t))|≤l2(‖x‖+‖y‖)+N2≤l2r+N2. | (3.7) |
Using (3.6) and (3.7), we obtain
|T1(x,y)(t)|≤Iξa+|φ(t,x(t),y(t))|+1|Δ|(|y0|+|x0|∫ba(b−s)ξΓ(ξ+1)|φ(s,x(s),y(s))|ds+∫ba|ρ1(s)||ψ(s,x(s),y(s))|ds+|ε1|m∑i=1|δi|∫σia(σi−s)ξ−1Γ(ξ)|φ(s,x(s),y(s))|ds+|ε1λ|∫bτ∫sa(s−u)ξ−1Γ(ξ)|φ(u,x(u),y(u))|duds)≤|y0||Δ|+{(b−a)ξΓ(ξ+1)+1|Δ|(|x0|(b−a)ξ+1Γ(ξ+2)+|ε1|m∑i=1|δi|(σi−a)ξΓ(ξ+1)+|ε1λ||(b−a)ξ+1−(τ−a)ξ+1|Γ(ξ+2))}(l1r+N1)+{1|Δ|(|x0|(b−a)ζ+1Γ(ζ+2)+|ε1|(b−a)ζ−1Γ(ζ)+|q|(b−a)ξΓ(ξ+1))}(l2r+N2)=|y0||Δ|+L1(l1r+N1)+M1(l2r+N2)=|y0||Δ|+(L1l1+M1l2)r+L1N1+M1N2. | (3.8) |
Taking the norm of (3.8) for t∈[a,b], we get
‖T1(x,y)‖≤|y0||Δ|+(L1l1+M1l2)r+L1N1+M1N2. |
Likewise, we can find that
‖T2(x,y)‖≤|ε2y0|(b−a)|Δ|+(L2l1+M2l2)r+L2N1+M2N2. |
Consequently,
‖T(x,y)‖≤|y0||Δ|+|ε2y0|(b−a)|Δ|+[(L1+L2)l1+(M1+M2)l2]r+(L1+L2)N1+(M1+M2)N2≤r. |
Now, for (x1,y1),(x2,y2)∈X×X and for any t∈[a,b], we get
|T1(x2,y2)(t)−T1(x1,y1)(t)|≤{(b−a)ξΓ(ξ+1)+1|Δ|(|x0|(b−a)ξ+1Γ(ξ+2)+|ε1|m∑i=1|δi|(σi−a)ξΓ(ξ+1)+|ε1λ||(b−a)ξ+1−(τ−a)ξ+1|Γ(ξ+2))}l1(‖x2−x1‖+‖y2−y1‖)+{1|Δ|(|x0|(b−a)ζ+1Γ(ζ+2)+|ε1|(b−a)ζ−1Γ(ζ)+|q|(b−a)ξΓ(ξ+1))}l2(‖x2−x1‖+‖y2−y1‖)=(L1l1+M1l2)(‖x2−x1‖+‖y2−y1‖), |
which implies that
‖T1(x2,y2)−T1(x1,y1)‖≤(L1l1+M1l2)(‖x2−x1‖+‖y2−y1‖). | (3.9) |
Similarly, we find that
‖T2(x2,y2)−T2(x1,y1)‖≤(L2l1+M2l2)(‖x2−x1‖+‖y2−y1‖). | (3.10) |
It follows from (3.9) and (3.10) that
‖T(x2,y2)−T(x1,y1)‖≤[(L1+L2)l1+(M1+M2)l2](‖x2−x1‖+‖y2−y1‖). |
From the above inequality, we deduce that T is a contraction. Hence it follows by Banach's fixed point theorem that there exists a unique fixed point for the operator T, which corresponds to a unique solution of problem (1.1) on [a,b]. This completes the proof.
Consider the following mixed-type coupled fractional differential system
{D34a+x(t)=φ(t,x(t),y(t)),t∈[1,2],D74a+y(t)=ψ(t,x(t),y(t)),t∈[1,2]15x(1)+110y(2)=11000∫21(x(s)+y(s))ds,y(1)=0,y′(2)=2∑i=1δix(σi)+110∫274x(s)ds, | (3.11) |
where ξ=3/4,ζ=7/4,p=1/5,q=1/10,x0=1/1000,y0=0,δ1=1/10,δ2=1/100,σ1=5/4,σ2=3/2,τ=7/4,λ=1/10. With the given data, it is found that L1≃3.5495×10−2,L2≃6.5531×10−2,M1≃1.0229,M2≃0.90742.
(1) In order to illustrate Theorem 3.1, we take
φ(t,x,y)=e−2t+18xcosy+e−t3ysiny,ψ(t,x,y)=t√t2+3+e−t3πxtan−1y+1√48+t2y. | (3.12) |
It is easy to check that the condition (H1) is satisfied with k0=1/e2,k1=1/8,k2=1/(3e),γ0=2√7,γ1=1/(6e),γ2=1/7. Furthermore, (L1+L2)k1+(M1+M2)γ1≃0.13098<1, and (L1+L2)k2+(M1+M2)γ2≃0.28815<1. Clearly the hypotheses of Theorem 3.1 are satisfied and hence the conclusion of Theorem 3.1 applies to problem (3.11) with φ and ψ given by (3.12).
(2) In order to illustrate Theorem 3.2, we take
φ(t,x,y)=e−t√3+t2cosx+cost,ψ(t,x,y)=15+t4(sinx+|y|)+e−t, | (3.13) |
which clearly satisfy the condition (H2) with l1=1/(2e) and l2=1/6. Moreover (L1+L2)l1+(M1+M2)l2≃0.3403<1. Thus the hypothesis of Theorem 3.2 holds true and consequently there exists a unique solution of the problem (3.11) with φ and ψ given by (3.13) on [1,2].
In this section, we consider a variant of the problem (1.1) in which the nonlinearities φ and ψ do not depend on x and y respectively. In precise terms, we consider the following problem:
{cDξa+x(t)=¯φ(t,y(t)),0<ξ≤1,t∈[a,b],cDζa+y(t)=¯ψ(t,x(t)),1<ζ≤2,t∈[a,b],px(a)+qy(b)=y0+x0∫ba(x(s)+y(s))ds,y(a)=0,y′(b)=m∑i=1δix(σi)+λ∫bτx(s)ds,a<σ1<σ2<…<σm<τ<…<b, | (4.1) |
where φ,ψ:[a,b]×R→R are given functions. Now we present the existence and uniqueness results for the problem (4.1). We do not provide the proofs as they are similar to the ones for the problem (1.1).
Theorem 4.1. Assume that ¯φ,¯ψ:[a,b]×R→R are continuous functions and there exist real constants ¯ki,¯γi≥0,(i=0,1) and ¯k0>0,¯γ0>0 such that, ∀x,y∈R,
|¯φ(t,y)|≤¯k0+¯k1|y|,|¯ψ(t,x)|≤¯γ0+¯γ1|x|. |
Then the system (4.1) has at least one solution on [a,b] provided that (M1+M2)¯γ1<1 and (L1+L2)¯k1<1, where L1,M1 and L2,M2 are given by (3.1).
Theorem 4.2. Let ¯φ,¯ψ:[a,b]×R→R be continuous functions and there exist positive constants ¯l1 and ¯l2 such that, for all t∈[a,b] and xi,yi∈R,i=1,2,
|¯φ(t,x1)−¯φ(t,y1)|≤¯l1|x1−y1|,|¯ψ(t,x1)−¯ψ(t,y1)|≤¯l2|x1−y1|. |
If (L1+L2)¯l1+(M1+M2)¯l2<1, where L1,M1 and L2,M2 are given by (3.1) then the system (4.1) has a unique solution on [a,b].
We studied the solvability of a coupled system of nonlinear fractional differential equations of different orders supplemented with a new set of nonlocal multi-point integral boundary conditions on an arbitrary domain by applying the tools of modern functional analysis. We also presented the existence results for a variant of the given problem containing the nonlinearities depending on the cross-variables (unknown functions). Our results are new not only in the given configuration but also yield some new results by specializing the parameters involved in the problems at hand. For example, by taking δi=0,i=1,2,…,m in the obtained results, we obtain the ones associated with the coupled systems of fractional differential equations in (1.1) and (4.1) subject to the boundary conditions:
px(a)+qy(b)=y0+x0∫ba(x(s)+y(s))ds,y(a)=0,y′(b)=λ∫bτx(s)ds. |
For λ=0, our results correspond to the boundary conditions of the form:
px(a)+qy(b)=y0+x0∫ba(x(s)+y(s))ds,y(a)=0,y′(b)=m∑i=1δix(σi). | (5.1) |
Furthermore, the methods employed in this paper can be used to solve the systems involving fractional integro-differential equations and multi-term fractional differential equations complemented with the boundary conditions considered in the problem (1.1).
All authors declare no conflicts of interest in this paper.
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under grant no. (KEP-PhD-41-130-41). The authors, therefore, acknowledge with thanks DSR technical and financial support. The authors also thank the reviewers for their useful suggestions on our work.
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