A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells
The aim of this study is to provide the numerical outcomes of a nonlinear HIV infection system of latently infected CD4+ T cells exists in bioinformatics using Morlet wavelet (MW) artificial neural networks (ANNs) optimized initially with global search of genetic algorithms (GAs) hybridized for spee...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-06-01
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| Series: | Results in Physics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379721003776 |
| _version_ | 1819295370537074688 |
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| author | Muhammad Umar Zulqurnain Sabir Muhammad Asif Zahoor Raja Haci Mehmet Baskonus Shao-Wen Yao Esin Ilhan |
| author_facet | Muhammad Umar Zulqurnain Sabir Muhammad Asif Zahoor Raja Haci Mehmet Baskonus Shao-Wen Yao Esin Ilhan |
| author_sort | Muhammad Umar |
| collection | DOAJ |
| description | The aim of this study is to provide the numerical outcomes of a nonlinear HIV infection system of latently infected CD4+ T cells exists in bioinformatics using Morlet wavelet (MW) artificial neural networks (ANNs) optimized initially with global search of genetic algorithms (GAs) hybridized for speedy local search of sequential quadratic programming (SQP), i.e., MW-ANN-GA-SQP. The design of an error function is presented by designing the MW-ANN models for the differential equations along with the initial conditions that represent the HIV infection system involving latently infected CD4+ T cells. The precision and persistence of the presented approach MW-ANN-GA-SQP are recognized through comparative studies from the results of the Runge-Kutta numerical scheme for solving the HIV infection spread system in case of single and multiple trails of the MW-ANN-GA-SQP. Statistical estimates with ‘Theil’s inequality coefficient’ and ‘root mean square error’ based indices further validate the sustainability and applicability of proposed MW-ANN-GA-SQP solver. |
| first_indexed | 2024-12-24T04:41:08Z |
| format | Article |
| id | doaj.art-96b6eeaf7c184b27a6565b2d6108a6b6 |
| institution | Directory Open Access Journal |
| issn | 2211-3797 |
| language | English |
| last_indexed | 2024-12-24T04:41:08Z |
| publishDate | 2021-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj.art-96b6eeaf7c184b27a6565b2d6108a6b62022-12-21T17:14:49ZengElsevierResults in Physics2211-37972021-06-0125104235A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cellsMuhammad Umar0Zulqurnain Sabir1Muhammad Asif Zahoor Raja2Haci Mehmet Baskonus3Shao-Wen Yao4Esin Ilhan5Department of Mathematics and Statistics, Hazara University, Mansehra, PakistanDepartment of Mathematics and Statistics, Hazara University, Mansehra, PakistanFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROCDepartment of Mathematics and Science Education Harran University, Sanliurfa, TurkeySchool of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo 454000, China; Corresponding author.Kirsehir Ahi Evran University, Kirsehir, TurkeyThe aim of this study is to provide the numerical outcomes of a nonlinear HIV infection system of latently infected CD4+ T cells exists in bioinformatics using Morlet wavelet (MW) artificial neural networks (ANNs) optimized initially with global search of genetic algorithms (GAs) hybridized for speedy local search of sequential quadratic programming (SQP), i.e., MW-ANN-GA-SQP. The design of an error function is presented by designing the MW-ANN models for the differential equations along with the initial conditions that represent the HIV infection system involving latently infected CD4+ T cells. The precision and persistence of the presented approach MW-ANN-GA-SQP are recognized through comparative studies from the results of the Runge-Kutta numerical scheme for solving the HIV infection spread system in case of single and multiple trails of the MW-ANN-GA-SQP. Statistical estimates with ‘Theil’s inequality coefficient’ and ‘root mean square error’ based indices further validate the sustainability and applicability of proposed MW-ANN-GA-SQP solver.http://www.sciencedirect.com/science/article/pii/S2211379721003776Morlet waveletsHIV infection modelsGenetic algorithmsNeural networksSequential quadratic programmingBioinformatics |
| spellingShingle | Muhammad Umar Zulqurnain Sabir Muhammad Asif Zahoor Raja Haci Mehmet Baskonus Shao-Wen Yao Esin Ilhan A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells Results in Physics Morlet wavelets HIV infection models Genetic algorithms Neural networks Sequential quadratic programming Bioinformatics |
| title | A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells |
| title_full | A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells |
| title_fullStr | A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells |
| title_full_unstemmed | A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells |
| title_short | A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells |
| title_sort | novel study of morlet neural networks to solve the nonlinear hiv infection system of latently infected cells |
| topic | Morlet wavelets HIV infection models Genetic algorithms Neural networks Sequential quadratic programming Bioinformatics |
| url | http://www.sciencedirect.com/science/article/pii/S2211379721003776 |
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