Design of Morlet wavelet neural networks integrated with sequential quadratic programming to analyze the dynamics of Ebola virus disease
•Investigates the design of an integrated intelligent computing strategy in the framework of morlet wavelet neural networks.•Predicting solutions for Ebola virus disease.•Hybrid optimization algorithm with MWNNs and a genetic algorithm integrated with sequential quadratic programming.•Efficiency of...
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| Published in: | Results in engineering Vol. 26; p. 105396 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.06.2025
Elsevier |
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| ISSN: | 2590-1230, 2590-1230 |
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| Abstract | •Investigates the design of an integrated intelligent computing strategy in the framework of morlet wavelet neural networks.•Predicting solutions for Ebola virus disease.•Hybrid optimization algorithm with MWNNs and a genetic algorithm integrated with sequential quadratic programming.•Efficiency of the proposed technique is confirmed by comparing results with a reference solution.•The accuracy, convergence, and stability of the proposed technique and prove that it can be applied to other non-linear disease models.
Ebola virus disease is caused by a virus that originates in flying foxes, or fruit bats. These bats may spread the virus to humans. The proposed model partitions the infected population into unaware and aware, and has a hospitalized compartment. Unlike previous models, which focused mostly on the general population, our model provides a more in-depth study of public health measures. Artificial neural networks (ANNs) are a highly effective approach for modeling intricate systems due to their ability to accurately approximate nonlinear functions. Their versatility and adaptability make them well-suited for addressing complex challenges in a variety of domains, such as epidemiology. The study aims to investigate the design of an integrated intelligent computing strategy in the framework of Morlet wavelet neural networks (MWNNs) for predicting solutions for an Ebola virus disease (EVD) model. For the investigation of this model, we use a hybrid optimization algorithm with MWNNs and a genetic algorithm (GA) integrated with sequential quadratic programming (SQP). The effectiveness of the developed MWNN-GA-SQP framework is thoroughly evaluated using a variety of error metrics such as mean absolute error (MAE), mean square error (MSE), and Theil’s inequality coefficient (TIC) to ensure a detailed and multidimensional performance assessment. A comparative evaluation with the conventional RK-4 numerical method as a reference solution is conducted with results illustrated through a variety of graphical tools, including histograms, box plots, and loss curves. The MWNN-GA-SQP framework's accuracy, consistency, and resilience are confirmed by thorough validation and verification against reference solutions, demonstrating its effectiveness in capturing the complex dynamics of epidemiological systems. Furthermore, the statistical analyses affirm the model's capability to precisely describe the nonlinear EVD model using an exponential incidence function, highlighting its potential as a powerful computational tool for analyzing infectious disease dynamics. |
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| AbstractList | Ebola virus disease is caused by a virus that originates in flying foxes, or fruit bats. These bats may spread the virus to humans. The proposed model partitions the infected population into unaware and aware, and has a hospitalized compartment. Unlike previous models, which focused mostly on the general population, our model provides a more in-depth study of public health measures. Artificial neural networks (ANNs) are a highly effective approach for modeling intricate systems due to their ability to accurately approximate nonlinear functions. Their versatility and adaptability make them well-suited for addressing complex challenges in a variety of domains, such as epidemiology. The study aims to investigate the design of an integrated intelligent computing strategy in the framework of Morlet wavelet neural networks (MWNNs) for predicting solutions for an Ebola virus disease (EVD) model. For the investigation of this model, we use a hybrid optimization algorithm with MWNNs and a genetic algorithm (GA) integrated with sequential quadratic programming (SQP). The effectiveness of the developed MWNN-GA-SQP framework is thoroughly evaluated using a variety of error metrics such as mean absolute error (MAE), mean square error (MSE), and Theil’s inequality coefficient (TIC) to ensure a detailed and multidimensional performance assessment. A comparative evaluation with the conventional RK-4 numerical method as a reference solution is conducted with results illustrated through a variety of graphical tools, including histograms, box plots, and loss curves. The MWNN-GA-SQP framework's accuracy, consistency, and resilience are confirmed by thorough validation and verification against reference solutions, demonstrating its effectiveness in capturing the complex dynamics of epidemiological systems. Furthermore, the statistical analyses affirm the model's capability to precisely describe the nonlinear EVD model using an exponential incidence function, highlighting its potential as a powerful computational tool for analyzing infectious disease dynamics. •Investigates the design of an integrated intelligent computing strategy in the framework of morlet wavelet neural networks.•Predicting solutions for Ebola virus disease.•Hybrid optimization algorithm with MWNNs and a genetic algorithm integrated with sequential quadratic programming.•Efficiency of the proposed technique is confirmed by comparing results with a reference solution.•The accuracy, convergence, and stability of the proposed technique and prove that it can be applied to other non-linear disease models. Ebola virus disease is caused by a virus that originates in flying foxes, or fruit bats. These bats may spread the virus to humans. The proposed model partitions the infected population into unaware and aware, and has a hospitalized compartment. Unlike previous models, which focused mostly on the general population, our model provides a more in-depth study of public health measures. Artificial neural networks (ANNs) are a highly effective approach for modeling intricate systems due to their ability to accurately approximate nonlinear functions. Their versatility and adaptability make them well-suited for addressing complex challenges in a variety of domains, such as epidemiology. The study aims to investigate the design of an integrated intelligent computing strategy in the framework of Morlet wavelet neural networks (MWNNs) for predicting solutions for an Ebola virus disease (EVD) model. For the investigation of this model, we use a hybrid optimization algorithm with MWNNs and a genetic algorithm (GA) integrated with sequential quadratic programming (SQP). The effectiveness of the developed MWNN-GA-SQP framework is thoroughly evaluated using a variety of error metrics such as mean absolute error (MAE), mean square error (MSE), and Theil’s inequality coefficient (TIC) to ensure a detailed and multidimensional performance assessment. A comparative evaluation with the conventional RK-4 numerical method as a reference solution is conducted with results illustrated through a variety of graphical tools, including histograms, box plots, and loss curves. The MWNN-GA-SQP framework's accuracy, consistency, and resilience are confirmed by thorough validation and verification against reference solutions, demonstrating its effectiveness in capturing the complex dynamics of epidemiological systems. Furthermore, the statistical analyses affirm the model's capability to precisely describe the nonlinear EVD model using an exponential incidence function, highlighting its potential as a powerful computational tool for analyzing infectious disease dynamics. |
| ArticleNumber | 105396 |
| Author | Virk, Abaid Ur Rehman Samy Mohamed Elwahsh, Ahmed Ashraf, Asifa Ishtiaq, Umar Popa, Ioan-Lucian |
| Author_xml | – sequence: 1 givenname: Asifa surname: Ashraf fullname: Ashraf, Asifa email: s2023265008@umt.edu.pk organization: Department of Mathematics, University of Management and Technology, Lahore, Pakistan – sequence: 2 givenname: Abaid Ur Rehman surname: Virk fullname: Virk, Abaid Ur Rehman email: abaidrehman@umt.edu.pk organization: Department of Mathematics, University of Management and Technology, Lahore, Pakistan – sequence: 3 givenname: Umar orcidid: 0000-0002-5228-1073 surname: Ishtiaq fullname: Ishtiaq, Umar email: umarishtiaq000@gmail.com organization: Office of Research, Innovation and Commercialization, University of Management and Technology, Lahore 54770, Pakistan – sequence: 4 givenname: Ahmed surname: Samy Mohamed Elwahsh fullname: Samy Mohamed Elwahsh, Ahmed email: aelwahsh@ksu.edu.sa organization: Deanship of Scientific Research, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia – sequence: 5 givenname: Ioan-Lucian surname: Popa fullname: Popa, Ioan-Lucian email: lucian.popa@uab.ro organization: Department of Computing, Mathematics, and Electronics, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania |
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| Keywords | Statistical analysis Sequential quadratic programming Genetic algorithm Morlet wavelet neural network Ebola virus disease |
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| Snippet | •Investigates the design of an integrated intelligent computing strategy in the framework of morlet wavelet neural networks.•Predicting solutions for Ebola... Ebola virus disease is caused by a virus that originates in flying foxes, or fruit bats. These bats may spread the virus to humans. The proposed model... |
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| SubjectTerms | Ebola virus disease Genetic algorithm Morlet wavelet neural network Sequential quadratic programming Statistical analysis |
| Title | Design of Morlet wavelet neural networks integrated with sequential quadratic programming to analyze the dynamics of Ebola virus disease |
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