Analysis of Oscillatory Behavior of Heart by Using a Novel Neuroevolutionary Approach

Khan, Adnan; Sulaiman, Muhammad; Alhakami, Hosam; Alhindi, Ahmad

doi:10.1109/access.2020.2992281

Cited by 23 publications

(18 citation statements)

References 64 publications

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“…To verify the stability and robustness of the proposed technique, we got better values of global performance indicators; global mean absolute error (G MAE ) as in Eq (50) and mean of fitness values denoted as (M fit ) as in Eq (51). All results in terms of these global performance indicators, see Tables 8, 9, and 10 revealed the fact that our approach is better than state-ofthe-art approaches reported in the literature [33].…”

Section: Discussionmentioning

confidence: 82%

Investigation of singular ordinary differential equations by a neuroevolutionary approach

et al. 2020

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In this research, we have investigated doubly singular ordinary differential equations and a real application problem of studying the temperature profile in a porous fin model. We have suggested a novel soft computing strategy for the training of unknown weights involved in the feed-forward artificial neural networks (ANNs). Our neuroevolutionary approach is used to suggest approximate solutions to a highly nonlinear doubly singular type of differential equations. We have considered a real application from thermodynamics, which analyses the temperature profile in porous fins. For this purpose, we have used the optimizer, namely, the fractional-order particle swarm optimization technique (FO-DPSO), to minimize errors in solutions through fitness functions. ANNs are used to design the approximate series of solutions to problems considered in this paper. We find the values of unknown weights such that the approximate solutions to these problems have a minimum residual error. For global search in the domain, we have initialized FO-DPSO with random solutions, and it collects best so far solutions in each generation/ iteration. In the second phase, we have fine-tuned our algorithm by initializing FO-DPSO with the collection of best so far solutions. It is graphically illustrated that this strategy is very efficient in terms of convergence and minimum mean squared error in its best solutions. We can use this strategy for the higher-order system of differential equations modeling different important real applications.

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Section: Discussionmentioning

confidence: 82%

Investigation of singular ordinary differential equations by a neuroevolutionary approach

et al. 2020

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“…In electrical engineering, several methodologies are used to solve complex optimization problems [23]. Optimal design and temperature distribution of heat fin is solved by using hybridization of artificial neural networks and metaheuristic algorithms [24,25] and, furthermore, oscillatory behavior of heart beat [26]. Most of the real-application problems are highly nonlinear ODEs and provide less information about the continuity and differentiability of resulting solutions in solution space.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Beam‐Column Designs by Varying Axial Load with Internal Forces and Bending Rigidity Using a New Soft Computing Technique

et al. 2021

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Design problems in structural engineering are often modeled as differential equations. These problems are posed as initial or boundary value problems with several possible variations in structural designs. In this paper, we have derived a mathematical model that represents different structures of beam-columns by varying axial load with or without internal forces including bending rigidity. We have also developed a novel solver, the LeNN-NM algorithm, which consists of weighted Legendre polynomials, and a single path following optimizer, the Nelder–Mead (NM) algorithm. To evaluate the performance of our solver, we have considered three design problems representing beam-columns. The values of performance indicators, MAD, TIC, NSE, and ENSE, are calculated for a hundred simulations. The outcome of our statistical analysis points to the superiority of the LeNN-NM algorithm. Graphical illustrations are presented to further elaborate on our claims.

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“…Recently, numerous applications of Nature-inspired algorithms appeared in literature [27]- [33]. These techniques are used to obtain solutions to optimization problems involving ODEs and nonlinear complex objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…A hybrid soft computing technique combining the artificial neural networks and fractional-order DPSO algorithm is applied to analyze the corneal shape model of eye surgery [28]. A neuroevolutionary approach is used to analyze the oscillatory behavior of heart [27].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Temperature Profiles in Longitudinal Fin Designs by a Novel Neuroevolutionary Approach

et al. 2020

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Real application problems in physics, engineering, economics, and other disciplines are often modeled as differential equations. Classical numerical techniques are computationally expensive when we require solutions to our mathematical problems with no prior information. Hence, researchers are more interested in developing numerical methods that can obtain better solutions with fewer efforts and computational time. Heuristic algorithms are considered suitable candidates for such type of problems. In this research, we have developed a new neuroevolutionary algorithm that combines the power of feed-forward artificial neural networks (ANNs) and a modern metaheuristic, the Symbiotic Organism Search (SOS) algorithm. With our new approach, we have analyzed the simultaneous surface convection and radiation during heat transfer in different models of fins/ heat exchangers. Longitudinal fins are considered with concave parabolic, rectangular and trapezoidal shapes. We have analyzed our problem in two scenarios and six sub-cases. Our solutions are of high quality, with minimum residual errors in all cases. We have established the quality of our results by calculating values of different performance indicators like Root-mean-square error (RMSE), Absolute error (AE), Generational distance (GD), Mean absolute deviation (MAD), Nash-Sutcliffe efficiency (NSE), Error in Nash-Sutcliffe efficiency (ENSE). Statistical and graphical analysis of our results suggests that our approach is suitable for handling real application problems. We have compared our results with state-of-the-art results, and the outcome of our analysis points to the superiority of our approach. INDEX TERMS Approximate solutions, Artificial neural networks, Heat transfer analyses, Longitudinal fins, Metaheuristics, Symbiotic organism search optimizer.

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Analysis of Oscillatory Behavior of Heart by Using a Novel Neuroevolutionary Approach

Cited by 23 publications

References 64 publications

Investigation of singular ordinary differential equations by a neuroevolutionary approach

Investigation of singular ordinary differential equations by a neuroevolutionary approach

Analysis of Beam‐Column Designs by Varying Axial Load with Internal Forces and Bending Rigidity Using a New Soft Computing Technique

Analysis of Temperature Profiles in Longitudinal Fin Designs by a Novel Neuroevolutionary Approach

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