Artificial neural network scheme to solve the hepatitis B virus model
This article aims to describe the simulation studies of the hepatitis B virus non-linear system using supervised neural networks procedures supported by Levenberg-Marquardt back propagation methodology. The proposed strategy has five distinct quantities: susceptible X(t), symptomatic infections Y(t)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2023.1072447/full |
| _version_ | 1797856243035930624 |
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| author | Qusain Haider Ali Hassan Sayed M. Eldin |
| author_facet | Qusain Haider Ali Hassan Sayed M. Eldin |
| author_sort | Qusain Haider |
| collection | DOAJ |
| description | This article aims to describe the simulation studies of the hepatitis B virus non-linear system using supervised neural networks procedures supported by Levenberg-Marquardt back propagation methodology. The proposed strategy has five distinct quantities: susceptible X(t), symptomatic infections Y(t), chronic infections W(t), recovered population R(t), and a population that has received vaccinations Z(t). The reference data set for all three distinct cases has been obtained utilizing the ND-Solver and Adams method in Mathematica software. The outcomes have been validated with performance plots for all cases. To check the accuracy and effectiveness of proposed methodology mean square error has are presented. State transition, and regression plots are illustrated to elaborated the testing, training, and validation methodology. Additionally, absolute errors for different components of hepatitis B virus model are demonstrated to depict the error occurring during distinct cases. Whereas the data assigned to training is 81%, and 9% for each testing and validation. The mean square error for all three cases is 10−12 this show the accuracy and correctness of proposed methodology. |
| first_indexed | 2024-04-09T20:37:14Z |
| format | Article |
| id | doaj.art-d8e94ba18e784106b12ba560c11612c4 |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-04-09T20:37:14Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-d8e94ba18e784106b12ba560c11612c42023-03-30T07:51:21ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872023-03-01910.3389/fams.2023.10724471072447Artificial neural network scheme to solve the hepatitis B virus modelQusain Haider0Ali Hassan1Sayed M. Eldin2Department of Mathematics, University of Gujrat, Gujrat, PakistanDepartment of Mathematics, University of Gujrat, Gujrat, PakistanCenter of Research, Faculty of Engineering, Future University in Egypt, New Cairo, EgyptThis article aims to describe the simulation studies of the hepatitis B virus non-linear system using supervised neural networks procedures supported by Levenberg-Marquardt back propagation methodology. The proposed strategy has five distinct quantities: susceptible X(t), symptomatic infections Y(t), chronic infections W(t), recovered population R(t), and a population that has received vaccinations Z(t). The reference data set for all three distinct cases has been obtained utilizing the ND-Solver and Adams method in Mathematica software. The outcomes have been validated with performance plots for all cases. To check the accuracy and effectiveness of proposed methodology mean square error has are presented. State transition, and regression plots are illustrated to elaborated the testing, training, and validation methodology. Additionally, absolute errors for different components of hepatitis B virus model are demonstrated to depict the error occurring during distinct cases. Whereas the data assigned to training is 81%, and 9% for each testing and validation. The mean square error for all three cases is 10−12 this show the accuracy and correctness of proposed methodology.https://www.frontiersin.org/articles/10.3389/fams.2023.1072447/fullnon-linear mathematical hepatitis B virus modelinteger orderLevenberg-Marquardt back propagationneural networkreference database |
| spellingShingle | Qusain Haider Ali Hassan Sayed M. Eldin Artificial neural network scheme to solve the hepatitis B virus model Frontiers in Applied Mathematics and Statistics non-linear mathematical hepatitis B virus model integer order Levenberg-Marquardt back propagation neural network reference database |
| title | Artificial neural network scheme to solve the hepatitis B virus model |
| title_full | Artificial neural network scheme to solve the hepatitis B virus model |
| title_fullStr | Artificial neural network scheme to solve the hepatitis B virus model |
| title_full_unstemmed | Artificial neural network scheme to solve the hepatitis B virus model |
| title_short | Artificial neural network scheme to solve the hepatitis B virus model |
| title_sort | artificial neural network scheme to solve the hepatitis b virus model |
| topic | non-linear mathematical hepatitis B virus model integer order Levenberg-Marquardt back propagation neural network reference database |
| url | https://www.frontiersin.org/articles/10.3389/fams.2023.1072447/full |
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