Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
Citation: Danial Sharifrazi, Roohallah Alizadehsani, Javad Hassannataj Joloudari, Shahab S. Band, Sadiq Hussain, Zahra Alizadeh Sani, Fereshteh Hasanzadeh, Afshin Shoeibi, Abdollah Dehzangi, Mehdi Sookhak, Hamid Alinejad-Rokny. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 2381-2402. doi: 10.3934/mbe.2022110
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Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
Segre [1] made a pioneering attempt in the development of special algebra. He conceptualized the commutative generalization of complex numbers, bicomplex numbers, tricomplex numbers, etc. as elements of an infinite set of algebras. Subsequently, in the 1930s, researchers contributed in this area [2,3,4]. The next fifty years failed to witness any advancement in this field. Later, Price [5] developed the bicomplex algebra and function theory. Recent works in this subject [6,7] find some significant applications in different fields of mathematical sciences as well as other branches of science and technology. An impressive body of work has been developed by a number of researchers. Among these works, an important work on elementary functions of bicomplex numbers has been done by Luna-Elizaarrarˊas et al. [8]. Choi et al. [9] proved some common fixed point theorems in connection with two weakly compatible mappings in bicomplex valued metric spaces. Jebril [10] proved some common fixed point theorems under rational contractions for a pair of mappings in bicomplex valued metric spaces. In 2017, Dhivya and Marudai [11] introduced the concept of a complex partial metric space, suggested a plan to expand the results and proved some common fixed point theorems under a rational expression contraction condition. In 2019, Mani and Mishra [12] proved coupled fixed point theorems on a complex partial metric space using different types of contractive conditions. In 2021, Gunaseelan et al. [13] proved common fixed point theorems on a complex partial metric space. In 2021, Beg et al.[14] proved fixed point theorems on a bicomplex valued metric space. In 2021, Zhaohui et al. [15] proved common fixed theorems on a bicomplex partial metric space. In this paper, we prove coupled fixed point theorems on a bicomplex partial metric space. An example is provided to verify the effectiveness and applicability of our main results. An application of these results to Fredholm integral equations and nonlinear integral equations is given.
Throughout this paper, we denote the set of real, complex and bicomplex numbers, respectively, as C0, C1 and C2. Segre [1] defined the complex number as follows:
z=ϑ1+ϑ2i1, |
where ϑ1,ϑ2∈C0, i21=−1. We denote the set ofcomplex numbers C1 as:
C1={z:z=ϑ1+ϑ2i1,ϑ1,ϑ2∈C0}. |
Let z∈C1; then, |z|=(ϑ21+ϑ22)12. The norm ||.|| of an element in C1 is the positive real valued function ||.||:C1→C+0 defined by
||z||=(ϑ21+ϑ22)12. |
Segre [1] defined the bicomplex number as follows:
ς=ϑ1+ϑ2i1+ϑ3i2+ϑ4i1i2, |
where ϑ1,ϑ2,ϑ3,ϑ4∈C0, and independent units i1,i2 are such that i21=i22=−1 and i1i2=i2i1. We denote the set of bicomplex numbers C2 as:
C2={ς:ς=ϑ1+ϑ2i1+ϑ3i2+ϑ4i1i2,ϑ1,ϑ2,ϑ3,ϑ4∈C0}, |
i.e.,
C2={ς:ς=z1+i2z2,z1,z2∈C1}, |
where z1=ϑ1+ϑ2i1∈C1 and z2=ϑ3+ϑ4i1∈C1. If ς=z1+i2z2 and η=ω1+i2ω2 are any two bicomplex numbers, then the sum is ς±η=(z1+i2z2)±(ω1+i2ω2)=z1±ω1+i2(z2±ω2), and the product is ς.η=(z1+i2z2)(ω1+i2ω2)=(z1ω1−z2ω2)+i2(z1ω2+z2ω1).
There are four idempotent elements in C2: They are 0,1,e1=1+i1i22,e2=1−i1i22 of which e1 and e2 are nontrivial, such that e1+e2=1 and e1e2=0. Every bicomplex number z1+i2z2 can be uniquely expressed as the combination of e1 and e2, namely
ς=z1+i2z2=(z1−i1z2)e1+(z1+i1z2)e2. |
This representation of ς is known as the idempotent representation of a bicomplex number, and the complex coefficients ς1=(z1−i1z2) and ς2=(z1+i1z2) are known as the idempotent components of the bicomplex number ς.
An element ς=z1+i2z2∈C2 is said to be invertible if there exists another element η in C2 such that ςη=1, and η is said to be inverse (multiplicative) of ς. Consequently, ς is said to be the inverse(multiplicative) of η. An element which has an inverse in C2 is said to be a non-singular element of C2, and an element which does not have an inverse in C2 is said to be a singular element of C2.
An element ς=z1+i2z2∈C2 is non-singular if and only if ||z21+z22||≠0 and singular if and only if ||z21+z22||=0. When it exists, the inverse of ς is as follows.
ς−1=η=z1−i2z2z21+z22. |
Zero is the only element in C0 which does not have a multiplicative inverse, and in C1, 0=0+i10 is the only element which does not have a multiplicative inverse. We denote the set of singular elements of C0 and C1 by O0 and O1, respectively. However, there is more than one element in C2 which does not have a multiplicative inverse: for example, e1 and e2. We denote this set by O2, and clearly O0={0}=O1⊂O2.
A bicomplex number ς=ϑ1+ϑ2i1+ϑ3i2+ϑ4i1i2∈C2 is said to be degenerated (or singular) if the matrix
(ϑ1ϑ2ϑ3ϑ4) |
is degenerated (or singular). The norm ||.|| of an element in C2 is the positive real valued function ||.||:C2→C+0 defined by
||ς||=||z1+i2z2||={||z21||+||z22||}12=[|z1−i1z2|2+|z1+i1z2|22]12=(ϑ21+ϑ22+ϑ23+ϑ24)12, |
where ς=ϑ1+ϑ2i1+ϑ3i2+ϑ4i1i2=z1+i2z2∈C2.
The linear space C2 with respect to a defined norm is a normed linear space, and C2 is complete. Therefore, C2 is a Banach space. If ς,η∈C2, then ||ςη||≤√2||ς||||η|| holds instead of ||ςη||≤||ς||||η||, and therefore C2 is not a Banach algebra. For any two bicomplex numbers ς,η∈C2, we can verify the following:
1. ς⪯i2η⟺||ς||≤||η||,
2. ||ς+η||≤||ς||+||η||,
3. ||ϑς||=|ϑ|||ς||, where ϑ is a real number,
4. ||ςη||≤√2||ς||||η||, and the equality holds only when at least one of ς and η is degenerated,
5. ||ς−1||=||ς||−1 if ς is a degenerated bicomplex number with 0≺ς,
6. ||ςη||=||ς||||η||, if η is a degenerated bicomplex number.
The partial order relation ⪯i2 on C2 is defined as follows. Let C2 be the set of bicomplex numbers and ς=z1+i2z2, η=ω1+i2ω2∈C2. Then, ς⪯i2η if and only if z1⪯ω1 and z2⪯ω2, i.e., ς⪯i2η if one of the following conditions is satisfied:
1. z1=ω1, z2=ω2,
2. z1≺ω1, z2=ω2,
3. z1=ω1, z2≺ω2,
4. z1≺ω1, z2≺ω2.
In particular, we can write ς⋦i2η if ς⪯i2η and ς≠η, i.e., one of 2, 3 and 4 is satisfied, and we will write ς≺i2η if only 4 is satisfied.
Now, let us recall some basic concepts and notations, which will be used in the sequel.
Definition 2.1. [15] A bicomplex partial metric on a non-void set U is a function ρbcpms:U×U→C+2, where C+2={ς:ς=ϑ1+ϑ2i1+ϑ3i2+ϑ4i1i2,ϑ1,ϑ2,ϑ3,ϑ4∈C+0} and C+0={ϑ1∈C0|ϑ1≥0} such that for all φ,ζ,z∈U:
1. 0⪯i2ρbcpms(φ,φ)⪯i2ρbcpms(φ,ζ) (small self-distances),
2. ρbcpms(φ,ζ)=ρbcpms(ζ,φ) (symmetry),
3. ρbcpms(φ,φ)=ρbcpms(φ,ζ)=ρbcpms(ζ,ζ) if and only if φ=ζ (equality),
4. ρbcpms(φ,ζ)⪯i2ρbcpms(φ,z)+ρbcpms(z,ζ)−ρbcpms(z,z) (triangularity) .
A bicomplex partial metric space is a pair (U,ρbcpms) such that U is a non-void set and ρbcpms is a bicomplex partial metric on U.
Example 2.2. Let U=[0,∞) be endowed with bicomplex partial metric space ρbcpms:U×U→C+2 with ρbcpms(φ,ζ)=max, where e^{i_{2}\theta} = \cos \theta +i_{2}\sin \theta , for all \varphi, \zeta\in \mathcal{U} and 0\leq \theta\leq \frac{\pi}{2} . Obviously, (\mathcal{U}, \rho_{bcpms}) is a bicomplex partial metric space.
Definition 2.3. [15] A bicomplex partial metric space \mathcal{U} is said to be a T_{0} space if for any pair of distinct points of \mathcal{U} , there exists at least one open set which contains one of them but not the other.
Theorem 2.4. [15] Let (\mathcal{U}, \rho_{bcpms}) be a bicomplex partial metric space; then, (\mathcal{U}, \rho_{bcpms}) is T_{0} .
Definition 2.5. [15] Let (\mathcal{U}, \rho_{bcpms}) be a bicomplex partial metric space. A sequence \{\varphi_{\tau}\} in \mathcal{U} is said to be convergent and converges to \varphi\in\mathcal{U} if for every 0\prec_{i_{2}}\epsilon\in \mathscr{C}^{+}_{2} there exists \mathcal{N}\in \mathbb{N} such that \varphi_{\tau}\in \mathfrak{B}_{ \rho_{bcpms}}(\varphi, \epsilon) = \{\omega\in \mathcal{U}:\rho_{bcpms}(\varphi, \omega) < \epsilon+\rho_{bcpms}(\varphi, \varphi)\} for all \tau\geq \mathcal{N} , and it is denoted by \lim\limits_{\tau\rightarrow \infty} \varphi_{\tau} = \varphi .
Lemma 2.6. [15] Let (\mathcal{U}, \rho_{bcpms}) be a bicomplex partial metric space. A sequence \{\varphi_{\tau}\}\in \mathcal{U} is converges to \varphi\in \mathcal{U} iff \rho_{bcpms}(\varphi, \varphi) = \lim\limits_{\tau \to \infty} \rho_{bcpms}(\varphi, \varphi_{\tau}) .
Definition 2.7. [15] Let (\mathcal{U}, \rho_{bcpms}) be a bicomplex partial metric space. A sequence \{\varphi_{\tau}\} in \mathcal{U} is said to be a Cauchy sequence in (\mathcal{U}, \rho_{bcpms}) if for any \epsilon > 0 there exist \vartheta\in \mathscr{C}^{+}_{2} and \mathcal{N}\in \mathbb{N} such that || \rho_{bcpms}(\varphi_{\tau}, \varphi_{\upsilon})-\vartheta|| < \epsilon for all \tau, \upsilon\geq\mathcal{N} .
Definition 2.8. [15] Let (\mathcal{U}, \rho_{bcpms}) be a bicomplex partial metric space. Let \{\varphi_{\tau}\} be any sequence in \mathcal{U} . Then,
1. If every Cauchy sequence in \mathcal{U} is convergent in \mathcal{U} , then (\mathcal{U}, \rho_{bcpms}) is said to be a complete bicomplex partial metric space.
2. A mapping \mathcal{S}:\mathcal{U} \to \mathcal{U} is said to be continuous at \varphi_{0}\in \mathcal{U} if for every \epsilon > 0 , there exists \delta > 0 such that \mathcal{S}(\mathfrak{B}_{ \rho_{bcpms}}(\varphi_{0}, \delta))\subset \mathfrak{B}_{ \rho_{bcpms}}(\mathcal{S}(\varphi_{0}, \epsilon)) .
Lemma 2.9. [15] Let (\mathcal{U}, \rho_{bcpms}) be a bicomplex partial metric space and \{\varphi_{\tau}\} be a sequence in \mathcal{U} . Then, \{\varphi_{\tau}\} is a Cauchy sequence in \mathcal{U} iff \lim\limits_{\tau, \upsilon\to \infty} \rho_{bcpms}(\varphi_{\tau}, \varphi_{\upsilon}) = \rho_{bcpms}(\varphi, \varphi) .
Definition 2.10. Let (\mathcal{U}, \rho_{bcpms}) be a bicomplex partial metric space. Then, an element (\varphi, \zeta)\in \mathcal{U}\times \mathcal{U} is said to be a coupled fixed point of the mapping \mathcal{S}: \mathcal{U}\times \mathcal{U}\to \mathcal{U} if \mathcal{S}(\varphi, \zeta) = \varphi and \mathcal{S}(\zeta, \varphi) = \zeta .
Theorem 2.11. [15] Let (\mathcal{U}, \rho_{bcpms}) be a complete bicomplex partial metric space and \mathcal{S}, \mathcal{T} \colon \mathcal{ U} \rightarrow \mathcal{U} be two continuous mappings such that
\begin{align*} \rho_{bcpms}(\mathcal{S} \varphi, \mathcal{T} \zeta) &\preceq_{i_{2}} \mathfrak{l} \max\{ \rho_{bcpms}(\varphi, \zeta), \rho_{bcpms}(\varphi, \mathcal{S} \varphi), \rho_{bcpms}(\zeta, \mathcal{T} \zeta), \notag \\ &\; \; \; \; \dfrac{1}{2}( \rho_{bcpms}(\varphi, \mathcal{T} \zeta)+ \rho_{bcpms}(\zeta, \mathcal{S} \varphi))\}, \label{e1} \end{align*} |
for all \varphi, \zeta \in \mathcal{U} , where 0\leq \mathfrak{l} < 1 . Then, the pair (\mathcal{S}, \mathcal{T}) has a unique common fixed point, and \rho_{bcpms}(\varphi^{*}, \varphi^{*}) = 0 .
Inspired by Theorem 2.11, here we prove coupled fixed point theorems on a bicomplex partial metric space with an application.
Theorem 3.1. Let (\mathcal{U}, \rho_{bcpms}) be a complete bicomplex partial metric space. Suppose that the mapping \mathcal{S}:\mathcal{U}\times \mathcal{U}\to \mathcal{U} satisfies the following contractive condition:
\begin{equation*} \rho_{bcpms}(\mathcal{S}(\varphi , \zeta), \mathcal{S}(\nu, \mu)) \preceq_{i_{2}} \lambda\rho_{bcpms}(\mathcal{S}(\varphi, \zeta) , \varphi )+ \mathfrak{l}\rho_{bcpms}(\mathcal{S}(\nu, \mu), \nu), \end{equation*} |
for all \varphi, \zeta, \nu, \mu\in \mathcal{U} , where \lambda, \mathfrak{l} are nonnegative constants with \lambda+\mathfrak{l} < 1 . Then, \mathcal{S} has a unique coupled fixed point.
Proof. Choose \nu_{0}, \mu_{0}\in \mathcal{U} and set \nu_{1} = \mathcal{S}(\nu_{0}, \mu_{0}) and \mu_{1} = \mathcal{S}(\mu_{0}, \nu_{0}) . Continuing this process, set \nu_{\tau+1} = \mathcal{S}(\nu_{\tau}, \mu_{\tau}) and \mu_{\tau+1} = \mathcal{S}(\mu_{\tau}, \nu_{\tau}) . Then,
\begin{align*} \rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})& = \rho_{bcpms}(\mathcal{S}(\nu_{\tau-1}, \mu_{\tau-1}), \mathcal{S}(\nu_{\tau}, \mu_{\tau}))\\ &\preceq_{i_{2}} \lambda\rho_{bcpms}(\mathcal{S}(\nu_{\tau-1}, \mu_{\tau-1}), \nu_{\tau-1})+\mathfrak{l}\rho_{bcpms}(\mathcal{S}(\nu_{\tau}, \mu_{\tau}), \nu_{\tau})\\ & = \lambda\rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1})+\mathfrak{l}\rho_{bcpms}(\nu_{\tau+1}, \nu_{\tau})\\ \rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})&\preceq_{i_{2}} \frac{\lambda}{1-\mathfrak{l}}\rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1}), \end{align*} |
which implies that
\begin{align} \lvert\lvert\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})\rvert \rvert\leq \mathfrak{z} \lvert\lvert\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})\rvert\rvert \end{align} | (3.1) |
where \mathfrak{z} = \frac{\lambda}{1-\mathfrak{l}} < 1 . Similarly, one can prove that
\begin{align} ||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||\leq \mathfrak{z}\lvert \lvert\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})\rvert\rvert. \end{align} | (3.2) |
From (3.1) and (3.2), we get
\begin{align*} \lvert\lvert\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})\rvert\rvert+ ||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||&\leq \mathfrak{z} (\lvert\lvert\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})\rvert\rvert\\ &+\lvert\lvert \rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})||), \end{align*} |
where \mathfrak{z} < 1 .
Also,
\begin{align} ||\rho_{bcpms}(\nu_{\tau+1}, \nu_{\tau+2})||\leq \mathfrak{z}||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})|| \end{align} | (3.3) |
\begin{align} ||\rho_{bcpms}(\mu_{\tau+1}, \mu_{\tau+2})||\leq \mathfrak{z}||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||. \end{align} | (3.4) |
From (3.3) and (3.4), we get
\begin{align*} ||\rho_{bcpms}(\nu_{\tau+1}, \nu_{\tau+2})||+||\rho_{bcpms}(\mu_{\tau+1}, \mu_{\tau+2})||&\leq \mathfrak{z}(||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||\\ &+||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||). \end{align*} |
Repeating this way, we get
\begin{align*} ||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||+||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||&\leq \mathfrak{z}(||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})||+||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})||)\\ &\leq \mathfrak{z}^{2}(||\rho_{bcpms}(\mu_{\tau-2}, \mu_{\tau-1})||\\ &+||\rho_{bcpms}(\nu_{\tau-2}, \nu_{\tau-1})||)\\ &\leq \dots \leq \mathfrak{z}^{\tau}(||\rho_{bcpms}(\mu_{0}, \mu_{1})||\\ &+||\rho_{bcpms}(\nu_{0}, \nu_{1})||). \end{align*} |
Now, if ||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||+||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})|| = \gamma_{\tau} , then
\begin{align} \gamma_{\tau}\leq \mathfrak{z} \gamma_{\tau-1} \leq \dots \leq \mathfrak{z}^\tau\gamma_{0}. \end{align} | (3.5) |
If \gamma_{0} = 0 , then ||\rho_{bcpms}(\nu_{0}, \nu_{1})||+||\rho_{bcpms}(\mu_{0}, \mu_{1})|| = 0 . Hence, \nu_{0} = \nu_{1} = \mathcal{S}(\nu_{0}, \mu_{0}) and \mu_{0} = \mu_{1} = \mathcal{S}(\mu_{0}, \mu_{0}) , which implies that (\nu_{0}, \mu_{0}) is a coupled fixed point of \mathcal{S} . Let \gamma_{0} > 0 . For each \tau\geq \upsilon , we have
\begin{align*} \rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon})&\preceq_{i_{2}} \rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1})+\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-2})-\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-1})\\ &+\rho_{bcpms}(\nu_{\tau-2}, \nu_{\tau-3})+\rho_{bcpms}(\nu_{\tau-3}, \nu_{\tau-4})-\rho_{bcpms}(\nu_{\tau-3}, \nu_{\tau-3})\\ &+\dots +\rho_{bcpms}(\nu_{\upsilon+2}, \nu_{\upsilon+1})+\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon})-\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon+1})\\ &\preceq_{i_{2}} \rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1})+\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-2})+\dots+\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon}), \end{align*} |
which implies that
\begin{align*} ||\rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon})||&\leq ||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1})||+||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-2})||\\ &+\dots+||\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon})||. \end{align*} |
Similarly, one can prove that
\begin{align*} ||\rho_{bcpms}(\mu_{\tau}, \mu_{\upsilon})||&\leq ||\rho_{bcpms}({\mu_{\tau}, \mu_{\tau-1}})||+||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau-2})||\\ &+\dots +||\rho_{bcpms}(\mu_{\upsilon+1}, \mu_{\upsilon})||. \end{align*} |
Thus,
\begin{align*} ||\rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon})||+||\rho_{bcpms}(\mu_{\tau}, \mu_{\upsilon})||&\leq \gamma_{\tau-1}+\gamma_{\tau-2}+\gamma_{\tau-3}+\dots +\gamma_{\upsilon}\\ &\leq (\mathfrak{z}^{\tau-1}+\mathfrak{z}^{\tau-2}+\dots +\mathfrak{z}^{\upsilon})\gamma_{0}\\ &\leq \frac{\mathfrak{z}^{\upsilon}}{1-\mathfrak{z}}\gamma_{0}\rightarrow 0\, \, \text{as}\, \, \upsilon\rightarrow \infty, \end{align*} |
which implies that \{\nu_{\tau}\} and \{\mu_{\tau}\} are Cauchy sequences in (\mathcal{U}, \rho_{bcpms}) . Since the bicomplex partial metric space (\mathcal{U}, \rho_{bcpms}) is complete, there exist \nu, \mu\in \mathcal{U} such that \{\nu_{\tau}\}\rightarrow \nu and \{\mu_{\tau}\}\rightarrow \mu as \tau \rightarrow \infty , and
\begin{align*} \rho_{bcpms}(\nu, \nu) = \lim\limits_{\tau \rightarrow \infty }\rho_{bcpms}(\nu, \nu_{\tau}) = \lim\limits_{\tau, \upsilon \rightarrow \infty }\rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon}) = 0, \\ \rho_{bcpms}(\mu, \mu) = \lim\limits_{\tau \rightarrow \infty }\rho_{bcpms}(\mu, \mu_{\tau}) = \lim\limits_{\tau, \upsilon \rightarrow \infty }\rho_{bcpms}(\mu_{\tau}, \mu_{\upsilon}) = 0. \end{align*} |
We now show that \nu = \mathcal{S}(\nu, \mu) . We suppose on the contrary that \nu\neq \mathcal{S}(\nu, \mu) and \mu\neq \mathcal{S} (\mu, \nu) , so that 0\prec_{i_{2}} \rho_{bcpms}(\nu, \mathcal{S}(\nu, \mu)) = \mathfrak{l}_{1} and 0\prec_{i_{2}}\rho_{bcpms}(\mu, \mathcal{S}(\mu, \nu)) = \mathfrak{l}_{2} . Then,
\begin{align*} \mathfrak{l}_{1} = \rho_{bcpms}(\nu, \mathcal{S}(\nu, \mu))&\preceq_{i_{2}} \rho_{bcpms}(\nu, \nu_{\tau+1})+\rho_{bcpms}(\nu_{\tau+1}, \mathcal{S}(\nu, \mu))\\ & = \rho_{bcpms}(\nu, \nu_{\tau+1})+\rho_{bcpms}(\mathcal{S}(\nu_{\tau}, \mu_{\tau}), \mathcal{S}(\nu, \mu))\\ &\preceq_{i_{2}} \rho_{bcpms}(\nu, \nu_{\tau+1})+\lambda\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})+\mathfrak{l}\rho_{bcpms}(\mathcal{S}(\nu, \mu), \nu)\\ &\preceq_{i_{2}}\frac{1}{1-\mathfrak{l}} \rho_{bcpms}(\nu, \nu_{\tau+1})+\frac{\lambda}{1-\mathfrak{l}}\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau}), \end{align*} |
which implies that
\begin{align*} \lvert \lvert\mathfrak{l}_{1}\rvert\rvert\leq \frac{1}{1-\mathfrak{l}} \lvert\lvert\rho_{bcpms}(\nu, \nu_{\tau+1})\rvert\rvert+\frac{\lambda}{1-\mathfrak{l}}\lvert\lvert\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})\rvert\rvert. \end{align*} |
As \tau\rightarrow \infty , \lvert\lvert \mathfrak{l}_{1}\rvert\rvert\leq 0 . This is a contradiction, and therefore \lvert\lvert\rho_{bcpms}(\nu, \mathcal{S}(\nu, \mu))\rvert\rvert = 0 implies \nu = \mathcal{S}(\nu, \mu) . Similarly, we can prove that \mu = \mathcal{S}(\mu, \nu) . Thus (\nu, \mu) is a coupled fixed point of \mathcal{S} . Now, if (\mathfrak{g}, \mathfrak{h}) is another coupled fixed point of \mathcal{S} , then
\begin{align*} \rho_{bcpms}(\nu, \mathfrak{g}) = \rho_{bcpms}(\mathcal{S}(\nu, \mu), \mathcal{S}(\mathfrak{g}, \mathfrak{h})) &\preceq_{i_{2}} \lambda\rho_{bcpms}(\mathcal{S}(\nu, \mu), \nu)+\mathfrak{l}\rho_{bcpms}(\mathcal{S}(\mathfrak{g}, \mathfrak{h}), \mathfrak{g})\\ & = \lambda\rho_{bcpms}(\nu, \nu)+\mathfrak{l}\rho_{bcpms}(\mathfrak{g}, \mathfrak{g}) = 0. \end{align*} |
Thus, we have \mathfrak{g} = \nu . Similarly, we get \mathfrak{h} = \mu . Therefore \mathcal{S} has a unique coupled fixed point.
Corollary 3.2. Let (\mathcal{U}, \rho_{bcpms}) be a complete bicomplex partial metric space. Suppose that the mapping \mathcal{S}:\mathcal{U}\times \mathcal{U} \to \mathcal{U} satisfies the following contractive condition:
\begin{equation} \rho_{bcpms}(\mathcal{S}(\varphi, \zeta), \mathcal{S}(\nu, \mu))\preceq_{i_{2}} \lambda(\rho_{bcpms}(\mathcal{S}(\varphi, \zeta) , \varphi)+\rho_{bcpms}( \mathcal{S}(\nu, \mu), \nu)), \end{equation} | (3.6) |
for all \varphi, \zeta, \nu, \mu\in \mathcal{U} , where 0\leq \lambda < \frac{1 }{2} . Then, \mathcal{S} has a unique coupled fixed point.
Theorem 3.3. Let (\mathcal{U}, \rho_{bcpms}) be a complete complex partial metric space. Suppose that the mapping \mathcal{S}:\mathcal{U}\times \mathcal{U}\to \mathcal{U} satisfies the following contractive condition:
\begin{equation*} \rho_{bcpms}(\mathcal{S}(\varphi, \zeta), \mathcal{S}(\nu, \mu))\preceq_{i_{2}} \lambda\rho_{bcpms}(\varphi, \nu)+\mathfrak{l}\rho_{bcpms}(\zeta, \mu), \end{equation*} |
for all \varphi, \zeta, \nu, \mu\in \mathcal{U} , where \lambda, \mathfrak{l} are nonnegative constants with \lambda+\mathfrak{l} < 1 . Then, \mathcal{S} has a unique coupled fixed point.
Proof. Choose \nu_{0}, \mu_{0}\in \mathcal{U} and set \nu_{1} = \mathcal{S}(\nu_{0}, \mu_{0}) and \mu_{1} = \mathcal{S}(\mu_{0}, \nu_{0}) . Continuing this process, set \nu_{\tau+1} = \mathcal{S}(\nu_{\tau}, \mu_{\tau}) and \mu_{\tau+1} = \mathcal{S}(\mu_{\tau}, \nu_{\tau}) . Then,
\begin{align*} \rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})& = \rho_{bcpms}(\mathcal{S}(\nu_{\tau-1}, \mu_{\tau-1}), \mathcal{S}(\nu_{\tau}, \mu_{\tau}))\\ &\preceq_{i_{2}} \lambda\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})+\mathfrak{l}\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau}), \end{align*} |
which implies that
\begin{align} ||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||\leq \lambda||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})||+\mathfrak{l}||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})||. \end{align} | (3.7) |
Similarly, one can prove that
\begin{align} ||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||\leq \lambda||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})||+\mathfrak{l}||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})||. \end{align} | (3.8) |
From (3.7) and (3.8), we get
\begin{align*} ||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||+||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||&\leq (\lambda+\mathfrak{l})(||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})||\\ &+||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})||)\\ & = \alpha(||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})||\\ &+||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})||), \end{align*} |
where \alpha = \lambda+\mathfrak{l} < 1 . Also,
\begin{align} ||\rho_{bcpms}(\nu_{\tau+1}, \nu_{\tau+2})||\leq \lambda||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||+\mathfrak{l}||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})|| \end{align} | (3.9) |
\begin{align} ||\rho_{bcpms}(\mu_{\tau+1}, \mu_{\tau+2})||\leq \lambda||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||+\mathfrak{l}||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||. \end{align} | (3.10) |
From (3.9) and (3.10), we get
\begin{align*} ||\rho_{bcpms}(\nu_{\tau+1}, \nu_{\tau+2})||+||\rho_{bcpms}(\mu_{\tau+1}, \mu_{\tau+2})||&\leq (\lambda+\mathfrak{l})(||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||\\ &+||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||)\\ & = \alpha(||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||\\ &+||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||). \end{align*} |
Repeating this way, we get
\begin{align*} ||\rho_{bcpms}(\nu_{\tau}, \nu_{n+1})||+||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})||&\leq\alpha(||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau})||\\ &+||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau})||)\\ &\leq \alpha^{2}(||\rho_{bcpms}(\mu_{\tau-2}, \mu_{\tau-1})||\\ &+||\rho_{bcpms}(\nu_{\tau-2}, \nu_{\tau-1})||)\\ &\leq \dots \leq \alpha^{\tau}(||\rho_{bcpms}(\mu_{0}, \mu_{1})||\\ &+||\rho_{bcpms}(\nu_{0}, \nu_{1})||). \end{align*} |
Now, if ||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau+1})||+||\rho_{bcpms}(\mu_{\tau}, \mu_{\tau+1})|| = \gamma_{\tau} , then
\begin{align} \gamma_{\tau}\leq \alpha \gamma_{\tau-1} \leq \dots \leq \alpha^{\tau}\gamma_{0}. \end{align} | (3.11) |
If \gamma_{0} = 0 , then ||\rho_{bcpms}(\nu_{0}, \nu_{1})||+||\rho_{bcpms}(\mu_{0}, \mu_{1})|| = 0 . Hence, \nu_{0} = \nu_{1} = \mathcal{S}(\nu_{0}, \mu_{0}) and \mu_{0} = \mu_{1} = \mathcal{S}(\mu_{0}, \nu_{0}) , which implies that (\nu_{0}, \mu_{0}) is a coupled fixed point of \mathcal{S} . Let \gamma_{0} > 0 . For each \tau\geq \upsilon , we have
\begin{align*} \rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon})&\preceq_{i_{2}} \rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1})+\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-2})-\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-1})\\ &+\rho_{bcpms}(\nu_{\tau-2}, \nu_{\tau-3})+\rho_{bcpms}(\nu_{\tau-3}, \nu_{\tau-4})-\rho_{bcpms}(\nu_{\tau-3}, \nu_{\tau-3})\\ &+\dots +\rho_{bcpms}(\nu_{\upsilon+2}, \nu_{\upsilon+1})+\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon})-\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon+1})\\ &\preceq_{i_{2}} \rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1})+\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-2})+\dots+\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon}), \end{align*} |
which implies that
\begin{align*} ||\rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon})||&\leq ||\rho_{bcpms}(\nu_{\tau}, \nu_{\tau-1})||+||\rho_{bcpms}(\nu_{\tau-1}, \nu_{\tau-2})||\\ &+\dots+||\rho_{bcpms}(\nu_{\upsilon+1}, \nu_{\upsilon})||. \end{align*} |
Similarly, one can prove that
\begin{align*} ||\rho_{bcpms}(\mu_{\tau}, \mu_{\upsilon})||&\leq ||\rho_{bcpms}({\mu_{\tau}, \mu_{\tau-1}})||+||\rho_{bcpms}(\mu_{\tau-1}, \mu_{\tau-2})||\\ &+\dots+||\rho_{bcpms}(\mu_{\upsilon+1}, \mu_{\upsilon})||. \end{align*} |
Thus,
\begin{align*} ||\rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon})||+||\rho_{bcpms}(\mu_{\tau}, \mu_{\upsilon})||&\leq \gamma_{\tau-1}+\gamma_{\tau-2}+\gamma_{\tau-3}+\dots +\gamma_{\upsilon}\\ &\leq (\alpha^{\tau-1}+\alpha^{\tau-2}+\dots +\alpha^{\upsilon})\gamma_{0}\\ &\leq \frac{\alpha^{\upsilon}}{1-\alpha}\gamma_{0}\, \, {\rm{as}}\, \, \tau\to \infty, \end{align*} |
which implies that \{\nu_{\tau}\} and \{\mu_{\tau}\} are Cauchy sequences in (\mathcal{U}, \rho_{bcpms}) . Since the bicomplex partial metric space (\mathcal{U}, \rho_{bcpms}) is complete, there exist \nu, \mu\in \mathcal{U} such that \{\nu_{\tau}\}\rightarrow \nu and \{\mu_{\tau}\}\rightarrow \mu as \tau \rightarrow \infty , and
\begin{align*} \rho_{bcpms}(\nu, \nu) = \lim\limits_{\tau \rightarrow \infty }\rho_{bcpms}(\nu, \nu_{\tau}) = \lim\limits_{\tau, \upsilon \rightarrow \infty }\rho_{bcpms}(\nu_{\tau}, \nu_{\upsilon}) = 0, \\ \rho_{bcpms}(\mu, \mu) = \lim\limits_{\tau \rightarrow \infty }\rho_{bcpms}(\mu, \mu_{\tau}) = \lim\limits_{\tau, \upsilon \rightarrow \infty }\rho_{bcpms}(\mu_{\tau}, \mu_{\upsilon}) = 0. \end{align*} |
Therefore,
\begin{align*} \rho_{bcpms}(\mathcal{S}(\nu, \mu), \nu)&\leq \rho_{bcpms}(\mathcal{S}(\nu, \mu), \nu_{\tau+1})+\rho_{bcpms}(\nu_{\tau+1}, \nu)-\rho_{bcpms}(\nu_{\tau+1}, \nu_{\tau+1}), \\ &\leq \rho_{bcpms}(\mathcal{S}(\nu, \mu)), \mathcal{S}(\nu_{\tau}, \mu_{\tau})+\rho_{bcpms}(\nu_{\tau+1}, \nu)\\ &\leq \lambda\rho_{bcpms}(\nu_{\tau}, \nu)+\mathfrak{l}\rho_{bcpms}(\mu_{\tau}, \mu)+\rho_{bcpms}(\nu_{\tau+1}, \nu). \end{align*} |
As \tau \rightarrow \infty , from (3.6) and (3.12) we obtain \rho_{bcpms}(\mathcal{S}(\nu, \mu), \nu) = 0 . Therefore \mathcal{S}(\nu, \mu) = \nu . Similarly, we can prove \mathcal{S}(\mu, \nu) = \mu , which implies that (\nu, \mu) is a coupled fixed point of \mathcal{S} . Now, if (\mathfrak{g}_{1}, \mathfrak{h}_{1}) is another coupled fixed point of \mathcal{S} , then
\begin{align*} \rho_{bcpms}(\mathfrak{g}_{1}, \nu) = \rho_{bcpms}(\mathcal{S}(\mathfrak{g}_{1}, \mathfrak{h}_{1}), \mathcal{S}(\nu, \mu)) &\preceq_{i_{2}} \lambda\rho_{bcpms}(\mathfrak{g}_{1}, \nu)+\mathfrak{l}\rho_{bcpms}(\mathfrak{h}_{1}, \mu), \\ \rho_{bcpms}(\mathfrak{h}_{1}, \mu) = \rho_{bcpms}(\mathcal{S}(\mathfrak{h}_{1}, \mathfrak{g}_{1}), \mathcal{S}(\mu, \nu))&\preceq_{i_{2}} \lambda\rho_{bcpms}(\mathfrak{h}_{1}, \mu)+\mathfrak{l}\rho_{bcpms}(\mathfrak{g}_{1}, \nu), \end{align*} |
which implies that
\begin{align} \lvert\lvert\rho_{bcpms}(\mathfrak{g}_{1}, \nu)\rvert\rvert&\leq \lambda\lvert\lvert\rho_{bcpms}(\mathfrak{g}_{1}, \nu)\rvert\rvert+\mathfrak{l}\lvert\lvert\rho_{bcpms}(\mathfrak{h}_{1}, \mu)\rvert\rvert, \end{align} | (3.12) |
\begin{align} \lvert\lvert\rho_{bcpms}(\mathfrak{h}_{1}, \mu)\rvert\rvert&\leq \lambda\lvert\lvert\rho_{bcpms}(\mathfrak{h}_{1}, \mu)\rvert\rvert+\mathfrak{l}\lvert\lvert\rho_{bcpms}(\mathfrak{g}_{1}, \nu)\rvert\rvert. \end{align} | (3.13) |
From (3.12) and (3.13), we get
\begin{align*} \lvert\lvert\rho_{bcpms}(\mathfrak{g}_{1}, \nu)\rvert\rvert+\lvert\lvert\rho_{bcpms}(\mathfrak{h}_{1}, \mu)\rvert\rvert\leq (\lambda+\mathfrak{l})[\lvert\lvert\rho_{bcpms}(\mathfrak{g}_{1}, \nu)\rvert\rvert+\lvert\lvert\rho_{bcpms}(\mathfrak{h}_{1}, \mu)\rvert\rvert]. \end{align*} |
Since \lambda+\mathfrak{l} < 1 , this implies that \lvert\lvert\rho_{bcpms}(\mathfrak{g}_{1}, \nu)\rvert\rvert+\lvert\lvert\rho_{bcpms}(\mathfrak{h}_{1}, \mu)\rvert\rvert = 0 . Therefore, \nu = \mathfrak{g}_{1} and \mu = \mathfrak{h}_{1} . Thus, \mathcal{S} has a unique coupled fixed point.
Corollary 3.4. Let (\mathcal{U}, \rho_{bcpms}) be a complete bicomplex partial metric space. Suppose that the mapping \mathcal{S}:\mathcal{U}\times \mathcal{U} \to \mathcal{U} satisfies the following contractive condition:
\begin{equation} \rho_{bcpms}(\mathcal{S}(\varphi, \zeta), \mathcal{S}(\nu, \mu))\preceq_{i_{2}} \lambda(\rho_{bcpms}(\varphi, \nu)+\rho_{bcpms}(\zeta, \mu)), \end{equation} | (3.14) |
for all \varphi, \zeta, \nu, \mu\in \mathcal{U} , where 0\leq \lambda < \frac{1 }{2} . Then, \mathcal{S} has a unique coupled fixed point.
Example 3.5. Let \mathcal{U} = [0, \infty) and define the bicomplex partial metric \rho_{bcpms} :\mathcal{U}\times\mathcal{U}\to \mathscr{C}_{2}^{+} defined by
\begin{align*} \rho_{bcpms}(\varphi, \zeta) = \max\{\varphi, \zeta\}e^{i_{2}\theta}, \, \, 0\leq\theta\leq\frac{\pi}{2}. \end{align*} |
We define a partial order \preceq in \mathscr{C}_{2}^{+} as \varphi \preceq \zeta iff \varphi \leq \zeta . Clearly, (\mathcal{U}, \rho_{bcpms}) is a complete bicomplex partial metric space.
Consider the mapping \mathcal{S}:\mathcal{U}\times \mathcal{U}\to \mathcal{U } defined by
\begin{align*} \mathcal{S}(\varphi, \zeta) = \frac{\varphi+\zeta}{4}\, \, \, \forall \varphi, \zeta\in \mathcal{U}. \end{align*} |
Now,
\begin{align*} \rho_{bcpms}(\mathcal{S}(\varphi, \zeta), \mathcal{S}(\nu, \mu))& = \rho_{bcpms} \bigg(\frac{\varphi+\zeta}{4}, \frac{\nu+\mu}{4}\bigg) \\ & = \frac{1}{4}\max\{\varphi+\zeta, \nu+\mu\} e^{i_{2}\theta} \\ &\preceq_{i_{2}}\frac{1}{4}\bigg[\max\{\varphi, \nu\}+\max\{\zeta, \mu\}\bigg] e^{i_{2}\theta} \\ & = \frac{1}{4}\bigg[\rho_{bcpms}(\varphi, \nu)+\rho_{bcpms}(\zeta, \mu)\bigg] \\ & = \lambda\bigg(\rho_{bcpms}(\varphi, \nu)+\rho_{bcpms}(\zeta, \mu)\bigg), \end{align*} |
for all \varphi, \zeta, \nu, \mu\in \mathcal{U} , where 0\leq\lambda = \frac{1 }{4} < \frac{1}{2} . Therefore, all the conditions of Corollary 3.4 are satisfied, then the mapping \mathcal{S} has a unique coupled fixed point (0, 0) in \mathcal{U} .
As an application of Theorem 3.3, we find an existence and uniqueness result for a type of the following system of nonlinear integral equations:
\begin{align} \varphi (\mu )& = \int_{0}^{\mathcal{M}}\kappa (\mu , \mathfrak{p})[\mathcal{G} _{1}(\mathfrak{p}, \varphi (\mathfrak{p}))+\mathcal{G}_{2}(\mathfrak{p}, \zeta (\mathfrak{p}))]d\mathfrak{p}+\delta (\mu ), \\ \zeta (\mu )& = \int_{0}^{\mathcal{M}}\kappa (\mu , \mathfrak{p})[\mathcal{G} _{1}(\mathfrak{p}, \zeta (\mathfrak{p}))+\mathcal{G}_{2}(\mathfrak{p}, \varphi (\mathfrak{p}))]d\mathfrak{p}+\delta (\mu ), \, \, \mu , \in \lbrack 0, \mathcal{M }], \mathcal{M}\geq1. \end{align} | (4.1) |
Let \mathcal{U} = C([0, \mathcal{M}], \mathbb{R}) be the class of all real valued continuous functions on [0, \mathcal{M}] . We define a partial order \preceq in \mathscr{C}_{2}^{+} as x\preceq y iff x \leq y . Define \mathcal{S}:\mathcal{U}\times\mathcal{U}\to \mathcal{U} by
\begin{align*} \mathcal{S}(\varphi, \zeta)(\mu) = \int^{\mathcal{M}}_{0}\kappa(\mu, \mathfrak{p} )[\mathcal{G}_{1}(\mathfrak{p}, \varphi(\mathfrak{p}))+\mathcal{G}_{2}( \mathfrak{p}, \zeta(\mathfrak{p}))] d\mathfrak{p}+\delta(\mu). \end{align*} |
Obviously, (\varphi(\mu), \zeta(\mu)) is a solution of system of nonlinear integral equations (4.1) iff (\varphi(\mu), \zeta(\mu)) is a coupled fixed point of \mathcal{S} . Define \rho _{bcpms}:\mathcal{U} \times \mathcal{U}\rightarrow \mathscr{C}_{2} by
\begin{equation*} \rho _{bcpms}(\varphi , \zeta ) = (|\varphi -\zeta |+1)e^{i_{2}\theta }, \end{equation*} |
for all \varphi, \zeta \in \mathcal{U} , where 0\leq \theta \leq \frac{\pi }{2} . Now, we state and prove our result as follows.
Theorem 4.1. Suppose the following:
1. The mappings \mathcal{G}_{1}:[0, \mathcal{M}]\times\mathbb{R}\to \mathbb{R} , \mathcal{G}_{2}:[0, \mathcal{M}]\times\mathbb{R}\to\mathbb{R} , \delta:[0, \mathcal{M}]\to \mathbb{R} and \kappa:[0, \mathcal{M}]\times \mathbb{R}\to [0, \infty) are continuous.
2. There exists \eta > 0 , and \lambda, \mathfrak{l} are nonnegative constants with \lambda+ \mathfrak{l} < 1 , such that
\begin{align*} |\mathcal{G}_{1}(\mathfrak{p}, \varphi(\mathfrak{p}))-\mathcal{G}_{1}( \mathfrak{p}, \zeta(\mathfrak{p}))|&\preceq_{i_{2}}\eta\lambda (|\varphi-\zeta|+1)-\frac{1}{2}, \\ |\mathcal{G}_{2}(\mathfrak{p}, \zeta(\mathfrak{p}))-\mathcal{G}_{2}(\mathfrak{ p}, \varphi(\mathfrak{p}))|&\preceq_{i_{2}}\eta\mathfrak{l} (|\zeta-\varphi|+1)-\frac{1}{2}. \end{align*} |
3. \int^{\mathcal{M}}_{0}\eta|\kappa(\mu, \mathfrak{p})|d\mathfrak{p} \preceq_{i_{2}}1 .
Then, the integral equation (4.1) has a unique solution in \mathcal{U} .
Proof. Consider
\begin{align*} \rho_{bcpms}(\mathcal{S}(\varphi, \zeta), \mathcal{S}(\nu, \varPhi))& = (|\mathcal{S}(\varphi, \zeta)-\mathcal{S}(\nu, \varPhi)|+1)e^{i_{2}\theta}\\ & = \bigg(|\int^{\mathcal{M}}_{0}\kappa(\mu, \mathfrak{p})[\mathcal{G}_{1}(\mathfrak{p}, \varphi(\mathfrak{p}))+\mathcal{G}_{2}(\mathfrak{p}, \zeta(\mathfrak{p}))] d\mathfrak{p}+\delta(\mu)\\ &-\bigg(\int^{\mathcal{M}}_{0}\kappa(\mu, \mathfrak{p})[\mathcal{G}_{1}(\mathfrak{p}, \nu(\mathfrak{p}))+\mathcal{G}_{2}(\mathfrak{p}, \varPhi(\mathfrak{p}))] d\mathfrak{p}+\delta(\mu)\bigg)|+1\bigg)e^{i_{2}\theta}\\ & = \bigg(|\int^{\mathcal{M}}_{0}\kappa(\mu, \mathfrak{p})[\mathcal{G}_{1}(\mathfrak{p}, \varphi(\mathfrak{p}))-\mathcal{G}_{1}(\mathfrak{p}, \nu(\mathfrak{p}))\\ &+\mathcal{G}_{2}(\mathfrak{p}, \zeta(\mathfrak{p}))-\mathcal{G}_{2}(\mathfrak{p}, \varPhi(\mathfrak{p}))] d\mathfrak{p}|+1\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}}\bigg(\int^{\mathcal{M}}_{0}|\kappa(\mu, \mathfrak{p})|[|\mathcal{G}_{1}(\mathfrak{p}, \varphi(\mathfrak{p}))-\mathcal{G}_{1}(\mathfrak{p}, \nu(\mathfrak{p}))|\\ &+|\mathcal{G}_{2}(\mathfrak{p}, \zeta(\mathfrak{p}))-\mathcal{G}_{2}(\mathfrak{p}, \varPhi(\mathfrak{p}))|] d\mathfrak{p}+1\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}}\bigg(\int^{\mathcal{M}}_{0}|\kappa(\mu, \mathfrak{p})|d\mathfrak{p}(\eta\lambda (|\varphi-\nu|+1)-\frac{1}{2}\\ &+\eta\mathfrak{l} (|\zeta-\varPhi|+1)-\frac{1}{2})+1\bigg)e^{i_{2}\theta}\\ & = \bigg(\int^{\mathcal{M}}_{0}\eta|\kappa(\mu, \mathfrak{p})|d\mathfrak{p}(\lambda (|\varphi-\nu|+1)\\ &+\mathfrak{l} (|\zeta-\varPhi|+1))\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}} \bigg(\lambda (|\varphi-\nu|+1)+\mathfrak{l} (|\zeta-\varPhi|+1)\bigg)e^{i_{2}\theta}\\ & = \lambda\rho_{bcpms}(\varphi, \nu)+\mathfrak{l}\rho_{bcpms}(\zeta, \varPhi) \end{align*} |
for all \varphi, \zeta, \nu, \varPhi\in \mathcal{U} . Hence, all the hypotheses of Theorem 3.3 are verified, and consequently, the integral equation (4.1) has a unique solution.
Example 4.2. Let \mathcal{U} = C([0, 1], \mathbb{R}) . Now, consider the integral equation in \mathcal{U} as
\begin{align} \varphi(\mu) = \int_{0}^{1}\frac{\mathfrak{\mu p}}{23(\mu+5)}\bigg[\frac{1}{ 1+\varphi(\mathfrak{p})}+\frac{1}{2+\zeta(\mathfrak{p})}\bigg]d\mathfrak{p}+ \frac{6\mu^{2}}{5} \\ \zeta(\mu) = \int_{0}^{1}\frac{ \mathfrak{\mu p}}{23(\mu+5)}\bigg[\frac{1}{ 1+\zeta(\mathfrak{p})}+\frac{1}{ 2+\varphi(\mathfrak{p})}\bigg]d\mathfrak{p}+ \frac{6\mu^{2}}{5}. \end{align} | (4.2) |
Then, clearly the above equation is in the form of the following equation:
\begin{align} \varphi (\mu )& = \int_{0}^{\mathcal{M}}\kappa (\mu , \mathfrak{p})[\mathcal{G} _{1}(\mathfrak{p}, \varphi (\mathfrak{p}))+\mathcal{G}_{2}(\mathfrak{p}, \zeta (\mathfrak{p}))]d\mathfrak{p}+\delta (\mu ), \\ \zeta (\mu )& = \int_{0}^{\mathcal{M}}\kappa (\mu , \mathfrak{p})[\mathcal{G} _{1}(\mathfrak{p}, \zeta (\mathfrak{p}))+\mathcal{G}_{2}(\mathfrak{p}, \varphi (\mathfrak{p}))]d\mathfrak{p}+\delta (\mu ), \, \, \mu , \in \lbrack 0, \mathcal{M }], \end{align} | (4.3) |
where \delta(\mu) = \frac{6\mu^{2}}{5} , \kappa(\mu, \mathfrak{p}) = \frac{ \mathfrak{\mu p}}{23(\mu+5)} , \mathcal{G}_{1}(\mathfrak{p}, \mu) = \frac{1}{ 1+\mu} , \mathcal{G}_{2}(\mathfrak{p}, \mu) = \frac{1}{2+\mu} and \mathcal{M} = 1 . That is, (4.2) is a special case of (4.1) in Theorem 4.1. Here, it is easy to verify that the functions \delta(\mu) , \kappa(\mu, \mathfrak{p}) , \mathcal{G}_{1}(\mathfrak{p}, \mu) and \mathcal{ G}_{2}(\mathfrak{p}, \mu) are continuous. Moreover, there exist \eta = 10 , \lambda = \frac{1}{3} and \mathfrak{l} = \frac{1}{4} with \lambda+\mathfrak{l } < 1 such that
\begin{align*} |\mathcal{G}_{1}(\mathfrak{p}, \varphi)-\mathcal{G}_{1}(\mathfrak{p} , \zeta)|&\leq\eta\lambda (|\varphi-\zeta|+1)-\frac{1}{2}, \\ |\mathcal{G}_{2}(\mathfrak{p}, \zeta)-\mathcal{G}_{2}(\mathfrak{p} , \varphi)|&\leq\eta\mathfrak{l} (|\zeta-\varphi|+1)-\frac{1}{2} \end{align*} |
and \int^{\mathcal{M}}_{0}\eta|\kappa(\mu, \mathfrak{p})|d\mathfrak{p} = \int^{1}_{0}\frac{\eta\mu\mathfrak{p}}{23(\mu+5)}d\mathfrak{p} = \frac{\mu\eta }{23(\mu+5)} < 1 . Therefore, all the conditions of Theorem 3.3 are satisfied. Hence, system (4.2) has a unique solution (\varphi^{*}, \zeta^{*}) in \mathcal{U}\times\mathcal{U} .
As an application of Corollary 3.4, we find an existence and uniqueness result for a type of the following system of Fredholm integral equations:
\begin{align} \varphi (\mu )& = \int_{\mathcal{E}}\mathcal{G}(\mu , \mathfrak{p}, \varphi ( \mathfrak{p}), \zeta (\mathfrak{p}))d\mathfrak{p}+\delta (\mu ), \, \, \mu , \mathfrak{p}\in \mathcal{E}, \\ \zeta (\mu )& = \int_{\mathcal{E}}\mathcal{G}(\mu , \mathfrak{p}, \zeta ( \mathfrak{p}), \varphi (\mathfrak{p}))d\mathfrak{p}+\delta (\mu ), \, \, \mu , \mathfrak{p}\in \mathcal{E}, \end{align} | (4.4) |
where \mathcal{E} is a measurable, \mathcal{G}:\mathcal{E}\times \mathcal{ E}\times \mathbb{R}\times \mathbb{R}\rightarrow \mathbb{R} , and \delta \in \mathcal{L}^{\infty }(\mathcal{E}) . Let \mathcal{U} = \mathcal{L}^{\infty }(\mathcal{E}) . We define a partial order \preceq in \mathscr{C}_{2}^{+} as x\preceq y iff x\leq y. Define \mathcal{S}:\mathcal{U}\times \mathcal{U}\to \mathcal{U} by
\begin{align*} \mathcal{S}(\varphi, \zeta)(\mu) = \int_{\mathcal{E}}\mathcal{G}(\mu , \mathfrak{ p}, \varphi (\mathfrak{p}), \zeta (\mathfrak{p}))d\mathfrak{p}+\delta (\mu ). \end{align*} |
Obviously, (\varphi(\mu), \zeta(\mu)) is a solution of the system of Fredholm integral equations (4.4) iff (\varphi(\mu), \zeta(\mu)) is a coupled fixed point of \mathcal{S} . Define \rho _{bcpms}:\mathcal{U}\times \mathcal{U}\rightarrow \mathscr{C}_{2} by
\begin{equation*} \rho _{bcpms}(\varphi , \zeta ) = (|\varphi -\zeta |+1)e^{i_{2}\theta }, \end{equation*} |
for all \varphi, \zeta \in \mathcal{U} , where 0\leq \theta \leq \frac{\pi }{2} . Now, we state and prove our result as follows.
Theorem 4.3. Suppose the following:
1. There exists a continuous function \kappa:\mathcal{E}\times\mathcal{E} \to \mathbb{R} such that
\begin{align*} |\mathcal{G}(\mu, \mathfrak{p}, \varphi(\mathfrak{p}), \zeta(\mathfrak{p}))- \mathcal{G}(\mu, \mathfrak{p}, \nu(\mathfrak{p}), \varPhi(\mathfrak{p} ))|&\preceq_{i_{2}} |\kappa(\mu, \mathfrak{p})|(|\varphi(\mathfrak{p})-\nu( \mathfrak{p})| \\ &+|\zeta(\mathfrak{p})-\varPhi(\mathfrak{p})|-2), \end{align*} |
for all \varphi, \zeta, \nu, \varPhi\in \mathcal{U} , \mu, \mathfrak{p}\in \mathcal{E} .
2. \int_{\mathcal{E}}|\kappa(\mu, \mathfrak{p})|d\mathfrak{p} \preceq_{i_{2}} \frac{1}{4}\preceq_{i_{2}}1 .
Then, the integral equation (4.4) has a unique solution in \mathcal{U} .
Proof. Consider
\begin{align*} \rho_{bcpms}(\mathcal{S}(\varphi, \zeta), \mathcal{S}(\nu, \varPhi))& = (|\mathcal{S}(\varphi, \zeta)-\mathcal{S}(\nu, \varPhi)|+1)e^{i_{2}\theta}\\ & = \bigg(|\int_{\mathcal{E}}\mathcal{G}(\mu, \mathfrak{p}, \varphi(\mathfrak{p}), \zeta(\mathfrak{p}))d\mathfrak{p}+\delta(\mu)\\ &-\bigg(\int_{\mathcal{E}}\mathcal{G}(\mu, \mathfrak{p}, \nu(\mathfrak{p}), \varPhi(\mathfrak{p}))d\mathfrak{p}+\delta(\mu)\bigg)|+1\bigg)e^{i_{2}\theta}\\ & = \bigg(|\int_{\mathcal{E}}\bigg(\mathcal{G}(\mu, \mathfrak{p}, \varphi(\mathfrak{p}), \zeta(\mathfrak{p}))\\ &-\mathcal{G}(\mu, \mathfrak{p}, \nu(\mathfrak{p}), \varPhi(\mathfrak{p}))\bigg)d\mathfrak{p}|+1\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}}\bigg(\int_{\mathcal{E}}|\mathcal{G}(\mu, \mathfrak{p}, \varphi(\mathfrak{p}), \zeta(\mathfrak{p}))-\mathcal{G}(\mu, \mathfrak{p}, \nu(\mathfrak{p}), \varPhi(\mathfrak{p}))|d\mathfrak{p}+1\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}}\bigg(\int_{\mathcal{E}}|\kappa(\mu, \mathfrak{p})|(|\varphi(\mathfrak{p})-\nu(\mathfrak{p})|+|\zeta(\mathfrak{p})-\varPhi(\mathfrak{p})|-2)d\mathfrak{p}+1\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}}\bigg(\int_{\mathcal{E}}|\kappa(\mu, \mathfrak{p})|d\mathfrak{p}(|\varphi(\mathfrak{p})-\nu(\mathfrak{p})|+|\zeta(\mathfrak{p})-\varPhi(\mathfrak{p})|-2)+1\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}} \frac{1}{4}\bigg(|\varphi(\mathfrak{p})-\nu(\mathfrak{p})|+|\zeta(\mathfrak{p})-\varPhi(\mathfrak{p})|-2+4\bigg)e^{i_{2}\theta}\\ &\preceq_{i_{2}} \frac{1}{4}(\rho_{bcpms}(\varphi, \nu)+\rho_{bcpms}(\zeta, \varPhi))\\ & = \lambda(\rho_{bcpms}(\varphi, \nu)+\rho_{bcpms}(\zeta, \varPhi)), \end{align*} |
for all \varphi, \zeta, \nu, \varPhi\in \mathcal{U} , where 0\leq\lambda = \frac{1}{4} < \frac{1}{2} . Hence, all the hypotheses of Corollary 3.4 are verified, and consequently, the integral equation (4.4) has a unique solution.
In this paper, we proved coupled fixed point theorems on a bicomplex partial metric space. An illustrative example and an application on a bicomplex partial metric space were given.
The authors declare no conflict of interest.
[1] |
J. H. Joloudari, E. H. Joloudari, H. Saadatfar, M. Ghasemigol, S. M. Razavi, A. Mosavi, et al., Coronary artery disease dagnosis; ranking the significant features using a random trees model, Int. J. Environ. Res. Public Health, 17 (2020), 731. https://doi.org/10.3390/ijerph17030731. doi: 10.3390/ijerph17030731
![]() |
[2] | M. Aazam, E. N. Huh, Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT, in 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, IEEE, (2015), 687-694. https://doi.org/10.1109/AINA.2015.254. |
[3] |
W. Cooper, S. Hernandez-Diaz, P. Arbogast, Myocarditis, N. Engl. J. Med., 354 (2006), 2443-2451. https://doi.org/10.1056/NEJMoa055202. doi: 10.1056/NEJMoa055202
![]() |
[4] |
L. A. Blauwet, L. T. Cooper, Myocarditis, Prog. Cardiovasc. Dis., 52 (2010), 274-288. https://doi.org/10.1016/j.pcad.2009.11.006. doi: 10.1016/j.pcad.2009.11.006
![]() |
[5] |
A. M. Feldman, D. McNamara, Myocarditis, N. Engl. J. Med., 343 (2000) 1388-1398. https://doi.org/10.1056/NEJM200011093431908. doi: 10.1056/NEJM200011093431908
![]() |
[6] |
R. Alizadehsani, M. H. Zangooei, M. J. Hosseini, J. Habibi, A. Khosravi, M. Roshanzamir, et al., Coronary artery disease detection using computational intelligence methods, Knowl. Based Syst., 109 (2016), 187-197. https://doi.org/10.1016/j.knosys.2016.07.004. doi: 10.1016/j.knosys.2016.07.004
![]() |
[7] |
E. Nasarian, M. Abdar, M. A. Fahami, R. Alizadehsani, S. Hussain, M. E. Basiri, et al., Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach, Pattern Recognit. Lett., 133 (2020), 33-40. https://doi.org/10.1016/j.patrec.2020.02.010. doi: 10.1016/j.patrec.2020.02.010
![]() |
[8] |
R. Alizadehsani, M. Roshanzamir, M. Abdar, A. Beykikhoshk, A. Khosravi, S. Nahavandi, et al., Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries, Expert Syst., 2020. https://doi.org/10.1111/exsy.12573. doi: 10.1111/exsy.12573
![]() |
[9] |
R. Alizadehsani, M. Roshanzamir, M. Abdar, A. Beykikhoshk, M. H. Zangooei, A. Khosravi, et al., Model uncertainty quantification for diagnosis of each main coronary artery stenosis, Soft Comput., 24 (2020) 10149-10160. https://doi.org/10.1007/s00500-019-04531-0. doi: 10.1007/s00500-019-04531-0
![]() |
[10] |
H. Greenspan, B. Van Ginneken, R. M. Summers, Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique, IEEE Trans. Med. Imaging, 35 (2016), 1153-1159. https://doi.org/10.1109/TMI.2016.2553401. doi: 10.1109/TMI.2016.2553401
![]() |
[11] |
B. Baeßler, M. Mannil, D. Maintz, H. Alkadhi, R. Manka, Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results, Eur. J. Radiol., 102 (2018), 61-67. https://doi.org/10.1016/j.ejrad.2018.03.013. doi: 10.1016/j.ejrad.2018.03.013
![]() |
[12] | M. Ovreiu, D. Simon, Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease, in Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, (2010), 1235-1242. https://doi.org/10.1145/1830483.1830706. |
[13] | M. Ali, M. F. Rani, A. H. Jahidin, M. F. Saaid, M. Z. H. Noor, Identification of cardiomyopathy disease using hybrid multilayered perceptron network, in 2012 IEEE International Conference on Control System, Computing and Engineering, IEEE, (2013), 23-27. https://doi.org/10.1109/ICCSCE.2012.6487109. |
[14] |
D. Alis, A. Guler, M. Yergin, O. Asmakutlu, Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI, Diagn. Interv. Imaging, 101 (2020), 137-146. https://doi.org/10.1016/j.diii.2019.10.005. doi: 10.1016/j.diii.2019.10.005
![]() |
[15] | S. Borkar, M. N. Annadate, Supervised machine learning algorithm for detection of cardiac disorders, in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), IEEE, (2018), 1-4.. https://doi.org/10.1109/ICCUBEA.2018.8697795. |
[16] |
P. P. Sengupta, Y. M. Huang, M. Bansal, A. Ashrafi, M. Fisher, K. Shameer, et al., Cognitive machine-learning algorithm for cardiac imaging, Circ. Cardiovasc. Imaging, 9 (2016), e004330. https://doi.org/10.1161/CIRCIMAGING.115.004330. doi: 10.1161/CIRCIMAGING.115.004330
![]() |
[17] |
R. Begum, M. Ramesh, Detection of cardiomyopathy using support vector machine and artificial neural network, Int. J. Comput. Appl., 133 (2016), 29-34. https://doi.org/10.5120/ijca2016908178. doi: 10.5120/ijca2016908178
![]() |
[18] |
J. H. Joloudari, H. Saadatfar, A. Dehzangi, S. Shamshirband, Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection, Inf. Med. Unlocked, 17 (2019), 100255. https://doi.org/10.1016/j.imu.2019.100255. doi: 10.1016/j.imu.2019.100255
![]() |
[19] |
E. M. Green, R. Van Mourik, C. Wolfus, S. B. Heitner, O. Dur, M. J. Semigran, Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor, NPJ Digit. Med., 2 (2019), 57. https://doi.org/10.1038/s41746-019-0130-0. doi: 10.1038/s41746-019-0130-0
![]() |
[20] |
D. Y. Tsai, K. Kojima, Measurements of texture features of medical images and its application to computer-aided diagnosis in cardiomyopathy, Measurement, 37 (2005), 284-292. https://doi.org/10.1016/j.measurement.2004.11.015. doi: 10.1016/j.measurement.2004.11.015
![]() |
[21] |
S. Narula, K. Shameer, A. M. Salem Omar, J. T. Dudley, P. P. Sengupta, Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography, J. Am. Coll. Cardiol., 68 (2016), 2287. https://doi.org/10.1016/j.jacc.2016.08.062. doi: 10.1016/j.jacc.2016.08.062
![]() |
[22] |
Q. A. Rahman, L. G. Tereshchenko, M. Kongkatong, T. Abraham, M. R. Abraham, H. Shatkay, Utilizing ECG-based heartbeat classification for hypertrophic cardiomyopathy identification, IEEE Trans. Nanobiosci., 14 (2015), 505-512. https://doi.org/10.1109/TNB.2015.2426213. doi: 10.1109/TNB.2015.2426213
![]() |
[23] |
X. Shao, Y. Sun, K. Xiao, Y. Zhang, W. Zhang, Z. Kou, et al., Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach, Medicine, 97 (2018), e12246. https://doi.org/10.1097/MD.0000000000012246. doi: 10.1097/MD.0000000000012246
![]() |
[24] |
G. Captur, W. Heywood, C. Coats, S. Rosmini, V. Patel, L. R. Lopes, et al., Identification of a multiplex biomarker panel for hypertrophic cardiomyopathy using quantitative proteomics and machine learning, Mol. Cell. Proteomics, 19 (2020), 114. https://doi.org/10.1074/mcp.RA119.001586. doi: 10.1074/mcp.RA119.001586
![]() |
[25] |
F. Ali, S. El-Sappagh, S. R. Islam, D. Kwak, A. Ali, M. Imran, et al., A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion, Inf. Fusion, 63 (2020), 208-222. https://doi.org/10.1016/j.inffus.2020.06.008. doi: 10.1016/j.inffus.2020.06.008
![]() |
[26] |
A. Baccouche, B. Garcia-Zapirain, C. Castillo Olea, A. Elmaghraby, Ensemble deep learning models for heart disease classification: A case study from Mexico, Information, 11 (2020), 207. https://doi.org/10.3390/info11040207. doi: 10.3390/info11040207
![]() |
[27] | T. Chokwijitkul, A. Nguyen, H. Hassanzadeh, S. Perez, Identifying risk factors for heart disease in electronic medical records: A deep learning approach, in Proceedings of the BioNLP 2018 Workshop, (2018), 18-27. https://doi.org/10.18653/v1/W18-2303. |
[28] |
Y. S. Su, T. J. Ding, M. Y. Chen, Deep learning methods in internet of medical things for valvular heart disease screening system, IEEE Internet Things J., 99 (2021), 1. https://doi.org/10.1109/JIOT.2021.3053420. doi: 10.1109/JIOT.2021.3053420
![]() |
[29] |
S. S. Sarmah, An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural network, IEEE Access, 8 (2020), 135784-135797. https://doi.org/10.1109/ACCESS.2020.3007561. doi: 10.1109/ACCESS.2020.3007561
![]() |
[30] |
S. A. Morris, K. N. Lopez, Deep learning for detecting congenital heart disease in the fetus, Nat. Med., 27 (2021), 764-765. https://doi.org/10.1038/s41591-021-01354-1. doi: 10.1038/s41591-021-01354-1
![]() |
[31] | S. Narmadha, S. Gokulan, M. Pavithra, R. Rajmohan, T. Ananthkumar, Determination of various deep learning parameters to predict heart disease for diabetes patients, in 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), IEEE, (2020), 1-6. https://doi.org/10.1109/ICSCAN49426.2020.9262317. |
[32] |
R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, P. Singh, Prediction of heart disease using a combination of machine learning and deep learning, Comput. Intell. Neurosci., 2021 (2021), 8387680. https://doi.org/10.1155/2021/8387680. doi: 10.1155/2021/8387680
![]() |
[33] |
J. M. Kwon, K. H. Kim, K. H. Jeon, J. Park, Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography, Echocardiography, 36 (2019), 213-218. https://doi.org/10.1111/echo.14220. doi: 10.1111/echo.14220
![]() |
[34] |
S. Sharma, M. Parmar, Heart diseases prediction using deep learning neural network model., Int. J. Innovative Technol. Explor. Eng., 9 (2020), 2278-3075. https://doi.org/10.35940/ijitee.C9009.019320. doi: 10.35940/ijitee.C9009.019320
![]() |
[35] |
R. Poplin, A. V. Varadarajan, K. Blumer, Y. Liu, M. V. McConnell, G. S. Corrado, et al., Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning, Nat. Biomed. Eng., 2 (2018) 158-164. https://doi.org/10.1038/s41551-018-0195-0. doi: 10.1038/s41551-018-0195-0
![]() |
[36] |
M. Chetrit, M. G. Friedrich, The unique role of cardiovascular magnetic resonance imaging in acute myocarditis, F1000Research, 7 (2018), 1153. https://doi.org/10.12688/f1000research.14857.1. doi: 10.12688/f1000research.14857.1
![]() |
[37] |
M. D. Cornicelli, C. K. Rigsby, K. Rychlik, E. Pahl, J. D. Robinson, Diagnostic performance of cardiovascular magnetic resonance native T1 and T2 mapping in pediatric patients with acute myocarditis, J. Cardiovasc. Magn. Reson., 21 (2019), 40-48. https://doi.org/10.1186/s12968-019-0550-7. doi: 10.1186/s12968-019-0550-7
![]() |
[38] |
M. A. G. M. Olimulder, J. Van Es, M. A. Galjee, The importance of cardiac MRI as a diagnostic tool in viral myocarditis-induced cardiomyopathy, Neth. Heart J., 17 (2009), 481-486. https://doi.org/10.1007/BF03086308. doi: 10.1007/BF03086308
![]() |
[39] |
C. Moenninghoff, L. Umutlu, C. Kloeters, A. Ringelstein, M. E. Ladd, A. Sombetzki, et al., Workflow efficiency of two 1.5 T MR scanners with and without an automated user interface for head examinations, Acad. Radiol., 20 (2013), 721-730. https://doi.org/10.1016/j.acra.2013.01.004. doi: 10.1016/j.acra.2013.01.004
![]() |
[40] |
M. Khodatars, A. Shoeibi, N. Ghassemi, M. Jafari, A. Khadem, D. Sadeghi, et al., Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review, Comput. Biol. Med., 139 (2021). https://doi.org/10.1016/j.compbiomed.2021.104949. doi: 10.1016/j.compbiomed.2021.104949
![]() |
[41] |
N. Q. K. Le, Q. T. Ho, E. K. Y. Yapp, Y. Y. Ou, H. Y. Yeh, DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes, Neurocomputing, 375 (2020), 71-79. https://doi.org/10.1016/j.neucom.2019.09.070. doi: 10.1016/j.neucom.2019.09.070
![]() |
[42] |
J. N. Sua, S. Y. Lim, M. H. Yulius, X. Su, E. K. Y. Yapp, N. Q. K. Le, et al., Incorporating convolutional neural networks and sequence graph transform for identifying multilabel protein lysine ptm sites, Chemom. Intell. Lab. Syst., 206 (2020), 104171. https://doi.org/10.1016/j.chemolab.2020.104171. doi: 10.1016/j.chemolab.2020.104171
![]() |
[43] | N. Ghassemi, H. Mahami, M. T. Darbandi, A. Shoeibi, S. Hussain, F. Nasirzadeh, et al., Material recognition for automated progress monitoring using deep learning methods, preprint, arXiv: 2006.16344. |
[44] |
A. Shoeibi, M. Khodatars, N. Ghassemi, M. Jafari, P. Moridian, R. Alizadehsani, et al., Epileptic seizure detection using deep learning techniques: a review, Int. J. Environ. Res. Public Health, 18 (2021), 5780. https://doi.org/10.3390/ijerph18115780. doi: 10.3390/ijerph18115780
![]() |
[45] |
L. Fu, B. Lu, B. Nie, Z. Peng, H. Liu, X. Pi, Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals, Sensors, 20 (2020), 1020. https://doi.org/10.3390/s20041020. doi: 10.3390/s20041020
![]() |
[46] | Y. Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, et al., Handwritten digit recognition with a back-propagation network, in Proceedings of the 2nd International Conference on Neural Information Processing, (1990), 396-404. Available from: https://papers.nips.cc/paper/1989/file/53c3bce66e43be4f209556518c2fcb54-Paper.pdf. |
[47] |
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25 (2012), 1097-1105. https://doi.org/10.1145/3065386. doi: 10.1145/3065386
![]() |
[48] | K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, Comput. Sci., preprint, arXiv: 14091556. |
[49] | C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going deeper with convolutions; in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), 1-9. https://doi.org/10.1109/CVPR.2015.7298594. |
[50] | K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770-778. https://doi.org/10.1109/CVPR.2016.90. |
[51] |
S. Lawrence, C. L. Giles, T. Ah Chung, A. D. Back, Face recognition: a convolutional neural-network approach, IEEE Trans. Neural Networks, 8 (1997), 98-113. https://doi.org/10.1109/72.554195. doi: 10.1109/72.554195
![]() |
[52] |
R. Alizadehsani, M. Roshanzamir, S. Hussain, A. Khosravi, A. Koohestani, M. H. Zangooei, et al., Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020), Ann. Oper. Res., (2021), 1-42. https://doi.org/10.1007/s10479-021-04006-2. doi: 10.1007/s10479-021-04006-2
![]() |
[53] |
H. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, et al., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Med. Imaging, 35 (2016), 1285-1298. https://doi.org/10.1109/TMI.2016.2528162. doi: 10.1109/TMI.2016.2528162
![]() |
[54] |
U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, et al., Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals, Appl. Intell., 49 (2019), 16-27. https://doi.org/10.1007/s10489-018-1179-1. doi: 10.1007/s10489-018-1179-1
![]() |
[55] |
U. R. Acharya, H. Fujita, O. S. Lih, M. Adam, J. H. Tan, C. K. Chua, Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network, Knowl. Based Syst., 132 (2017), 62-71. https://doi.org/10.1016/j.knosys.2017.06.003. doi: 10.1016/j.knosys.2017.06.003
![]() |
[56] |
J. H. Tan, Y. Hagiwara, W. Pang, I. Lim, S. L. Oh, M. Adam, et al., Application of stacked convolutional and long short-term memory network for accurate identification of CADECG signals, Comput, Biol. Med., 94 (2018), 19-26. https://doi.org/10.1016/j.compbiomed.2017.12.023. doi: 10.1016/j.compbiomed.2017.12.023
![]() |
[57] |
A. Shoeibi, N. Ghassemi, R. Alizadehsani, M. Rouhani, H. Hosseini-Nejad, A. Khosravi, et al., A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals, Expert Syst. Appl., 163 (2021), 113788. https://doi.org/10.1016/j.eswa.2020.113788. doi: 10.1016/j.eswa.2020.113788
![]() |
[58] | K. Wagstaff, C. Cardie, S. Rogers, S. Schrödl, Constrained k-means clustering with background knowledge, (2001), 577-584. Available from: http://www.litech.org/~wkiri/Papers/wagstaff-kmeans-01.pdf. |
[59] |
A. K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognit. Lett., 31 (2010), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011. doi: 10.1016/j.patrec.2009.09.011
![]() |
[60] |
R. Alizadehsani, M. Roshanzamir, M. Abdar, A. Beykikhoshk, A. Khosravi, M. Panahiazar, et al., A database for using machine learning and data mining techniques for coronary artery disease diagnosis, Sci. Data, 6 (2019), 227. https://doi.org/10.1038/s41597-019-0206-3. doi: 10.1038/s41597-019-0206-3
![]() |
[61] |
G. Muhammad, M. S. Hossain, COVID-19 and non-COVID-19 classification using multi-layers fusion from lung ultrasound images, Inf. Fusion, 72 (2021), 80-88. https://doi.org/10.1016/j.inffus.2021.02.013. doi: 10.1016/j.inffus.2021.02.013
![]() |
[62] |
S. Hussain, G. Hazarika, Educational data mining model using rattle, Int. J. Adv. Comput. Sci. Appl., 5 (2014). https://doi.org/10.14569/IJACSA.2014.050605. doi: 10.14569/IJACSA.2014.050605
![]() |
[63] |
E. Haghighat, R. Juanes, Sciann: A keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks, Comput. Methods Appl. Mech. Eng., 373 (2021), 113552. https://doi.org/10.1016/j.cma.2020.113552. doi: 10.1016/j.cma.2020.113552
![]() |
[64] |
R. Kumar, W. Wang, J. Kumar, T. Yang, A. Khan, W. Ali, et al., An integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals, Comput. Med. Imaging Graph., 87 (2021), 101812. https://doi.org/10.1016/j.compmedimag.2020.101812. doi: 10.1016/j.compmedimag.2020.101812
![]() |
[65] |
R. Yamashita, J. Long, A. Saleem, D. L. Rubin, J. Shen, Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images, Sci. Rep., 11 (2021), 2047. https://doi.org/10.1038/s41598-021-81506-y. doi: 10.1038/s41598-021-81506-y
![]() |
[66] | F. V. Jensen, F. Jensen, An introduction to Bayesian networks, Springer, 2014. https://doi.org/10.1007/978-3-642-54157-5_5. |
[67] |
H. M. Afify, M. S. Zanaty, Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms, Med. Biol. Eng. Comput., 59 (2021), 1723-1734. https://doi.org/10.21203/rs.3.rs-34004/v2. doi: 10.1007/s11517-021-02412-z
![]() |
[68] | F. Gorunescu, Data Mining: Concepts, models and techniques, Springer, 2011. https://doi.org/10.1007/978-3-642-19721-5. |
[69] |
J. H. Joloudari, E. H. Joloudari, H. Saadatfar, M. GhasemiGol, S. M. Razavi, A. Mosavi, et al., Coronary artery disease diagnosis; ranking the significant features using a random trees model, Int. J. Environ. Res. Public Health, 17 (2020), 731. https://doi.org/10.3390/ijerph17030731. doi: 10.3390/ijerph17030731
![]() |
[70] |
I. Ruczinski, C. Kooperberg, M. LeBlanc, Logic regression, J. Comput. Graph. Stat., 12 (2003), 475-511. https://doi.org/10.1198/1061860032238. doi: 10.1198/1061860032238
![]() |
[71] |
G. Jones, J. Parr, P. Nithiarasu, S. Pant, A proof of concept study for machine learning application to stenosis detection, Med. Biol. Eng. Comput., 2021. https://doi.org/10.1007/s11517-021-02424-9. doi: 10.1007/s11517-021-02424-9
![]() |
[72] |
L. Breiman, Random forests, Mach. Learn., 45 (2001), 5-32. https://doi.org/10.1023/A:1010933404324. doi: 10.1023/A:1010933404324
![]() |
[73] |
I. Kindermann, C. Barth, F. Mahfoud, C. Ukena, M. Lenski, A. Yilmaz, et al., Update on myocarditis, J. Am. Coll. Cardiol., 59 (2012), 779. https://doi.org/10.1016/j.jacc.2011.09.074. doi: 10.1016/j.jacc.2011.09.074
![]() |
[74] |
T. S. Kafil, N. Tzemos, Myocarditis in 2020: advancements in imaging and clinical management, JACC Case Rep., 2 (2020), 178-179. https://doi.org/10.1016/j.jaccas.2020.01.004. doi: 10.1016/j.jaccas.2020.01.004
![]() |
[75] |
A. Roos, Diagnosis of myocarditis at cardiac MRI: the continuing quest for improved tissue characterization, Radiology, 292 (2019), 618-619. https://doi.org/10.1148/radiol.2019191476. doi: 10.1148/radiol.2019191476
![]() |
[76] |
F. Dominguez, U. Kühl, B. Pieske, P. Garcia-Pavia, C. Tschöpe, Update on myocarditis and inflammatory cardiomyopathy: reemergence of endomyocardial biopsy, Revista Española Cardiología, 69 (2016), 178-187. https://doi.org/10.1016/j.rec.2015.10.015. doi: 10.1016/j.rec.2015.10.015
![]() |
[77] |
C. Buttà, L. Zappia, G. Laterra, M. Roberto, Diagnostic and prognostic role of electrocardiogram in acute myocarditis: A comprehensive review, Ann. Noninvasive Electrocardiol., 1 (2020), 1-10. https://doi.org/10.1111/anec.12726. doi: 10.1111/anec.12726
![]() |
[78] |
P. Bholowalia, A. Kumar, EBK-means: A clustering technique based on elbow method and k-means in WSN, Int. J. Comput. Appl., 105 (2014), 17-24. https://doi.org/10.5120/18405-9674. doi: 10.5120/18405-9674
![]() |
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