Sleep apnea (SA) is a common respiratory disorder, especially among obese people. It is caused by either the relaxation of the upper respiratory tract muscles or the failure of the neural signal to reach the muscles r...
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Sleep apnea (SA) is a common respiratory disorder, especially among obese people. It is caused by either the relaxation of the upper respiratory tract muscles or the failure of the neural signal to reach the muscles responsible for breathing, both of which interrupt the patient's sleep-wake cycles. The traditional method for diagnosing this disorder, based on polysomnography, is complicated, vexing, expensive, time-consuming, and requires both sleep centers and specialized staff capable of connecting electrodes to the patient's body. This paper proposes an SA prediction system based on merging five soft computing algorithms, specifically, combining the multi-verse optimizer (MVO) with an artificial neural network (ANN) to leverage measurements from heart rate, SpO(2), and chest movement sensors. The most substantial novelty of this research is the hybridization of MVO and ANN (MVO-ANN), which improves the ANN performance by selecting the best learning rate and number of neurons in hidden ANN layers. This enables highly accurate prediction of sleep apnea events. This work's experimental results reveal that the MVO-ANN performs better than other algorithms, with mean absolute errors of 0.042, 0.202, and 0.166 for training, testing, and validation of the ANN. In addition, the SA prediction system achieved an accuracy of 98.67%, a sensitivity of 96.71%, and a specificity of 99.24%. These results provide good evidence that the proposed method can reliably predict respiratory events in people suffering from SA.
In this paper, a novel soft computing algorithm is designed for the numerical solution of third-order nonlinear multi-singular Emden-Fowler equation (TONMS-EFE) using the strength of universal approximation capabiliti...
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In this paper, a novel soft computing algorithm is designed for the numerical solution of third-order nonlinear multi-singular Emden-Fowler equation (TONMS-EFE) using the strength of universal approximation capabilities of Legendre polynomials based Legendre neural networks supported with optimization power of the Whale Optimization algorithm (WOA) and Nelder-Mead (NM) algorithm. Unsupervised error functions are constructed in terms of mean square error for governing TONMS-EF equations of first and second order. Unknown designed parameters in LeNN structure are optimized initially by WOA for global search while NM algorithm further enhances the rapid local search convergence. The proposed algorithm's objective is to show the accuracy and robustness in solving challenging problems like TONMS-EFE. To study our designed scheme's performance and effectiveness, LeNN-WOA-NM is implemented on four cases of TONMS-EFE. The results obtained by the proposed algorithm are compared with the Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm (CSA), and WOA. Extensive graphical and statistical analysis for fitness value, absolute errors, and performance indicators in terms of mean, median, and standard deviations show the proposed algorithm's efficiency and accuracy.
The flow of fluids in multi-phase porous media results due to many interesting natural phenomena. The counter-current water imbibition phenomena, that occur during oil extraction through a cylindrical well is an inter...
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The flow of fluids in multi-phase porous media results due to many interesting natural phenomena. The counter-current water imbibition phenomena, that occur during oil extraction through a cylindrical well is an interesting problem in petroleum engineering. During the secondary oil recovery process, water is injected into a porous media having heterogenous and homogenous characteristics. Due to the difference in viscosities of fluids in oil wells, the counter-current imbibition phenomenon occurs. At that moment, the imbibition equation V-i= -V-n is satisfied by the viscosities of oil and water. In this article, we have analyzed the governing mathematical model of the imbibition phenomenon occurring during the secondary oil recovery process. A new soft computing algorithm is designed and adapted to analyze the mathematical model of dual-phase flow in detail. Weighted Legendre polynomials based artificial neural networks are hybridized with an efficient global optimizer the Whale Optimization algorithm (WOA) and a local optimizer the Nelder-Mead algorithm. It is established, that our algorithm LeNN-WOA-NM is efficient and reliable in calculating high-quality solutions in less time. We have compared our experimental outcome with state-of-the-art results. The quality of our solutions is judged based on values of absolute errors, MAD, TIC, and ENSE. It is obvious that LeNN-WOA-NM algorithm can solve real application problems efficiently and accurately.
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