A computational stochastic procedure for solving the epidemic breathing transmission system
Abstract This work provides numerical simulations of the nonlinear breathing transmission epidemic system using the proposed stochastic scale conjugate gradient neural networks (SCGGNNs) procedure. The mathematical model categorizes the breathing transmission epidemic model into four dynamics based...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-43324-2 |
| _version_ | 1797576686691155968 |
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| author | Najah AbuAli Muhammad Bilal Khan Zulqurnain Sabir |
| author_facet | Najah AbuAli Muhammad Bilal Khan Zulqurnain Sabir |
| author_sort | Najah AbuAli |
| collection | DOAJ |
| description | Abstract This work provides numerical simulations of the nonlinear breathing transmission epidemic system using the proposed stochastic scale conjugate gradient neural networks (SCGGNNs) procedure. The mathematical model categorizes the breathing transmission epidemic model into four dynamics based on a nonlinear stiff ordinary differential system: susceptible, exposed, infected, and recovered. Three different cases of the model are taken and numerically presented by applying the stochastic SCGGNNs. An activation function ‘log-sigmoid’ uses twenty neurons in the hidden layers. The precision of SCGGNNs is obtained by comparing the proposed and database solutions. While the negligible absolute error is performed around 10–06 to 10–07, it enhances the accuracy of the scheme. The obtained results of the breathing transmission epidemic system have been provided using the training, verification, and testing procedures to reduce the mean square error. Moreover, the exactness and capability of the stochastic SCGGNNs are approved through error histograms, regression values, correlation tests, and state transitions. |
| first_indexed | 2024-03-10T21:56:24Z |
| format | Article |
| id | doaj.art-48943f659b3f422d877f674573bd29a9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:56:24Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-48943f659b3f422d877f674573bd29a92023-11-19T13:06:25ZengNature PortfolioScientific Reports2045-23222023-09-0113111210.1038/s41598-023-43324-2A computational stochastic procedure for solving the epidemic breathing transmission systemNajah AbuAli0Muhammad Bilal Khan1Zulqurnain Sabir2College of Information Technology, UAE UniversityCollege of Information Technology, UAE UniversityDepartment of Mathematical Sciences, UAE UniversityAbstract This work provides numerical simulations of the nonlinear breathing transmission epidemic system using the proposed stochastic scale conjugate gradient neural networks (SCGGNNs) procedure. The mathematical model categorizes the breathing transmission epidemic model into four dynamics based on a nonlinear stiff ordinary differential system: susceptible, exposed, infected, and recovered. Three different cases of the model are taken and numerically presented by applying the stochastic SCGGNNs. An activation function ‘log-sigmoid’ uses twenty neurons in the hidden layers. The precision of SCGGNNs is obtained by comparing the proposed and database solutions. While the negligible absolute error is performed around 10–06 to 10–07, it enhances the accuracy of the scheme. The obtained results of the breathing transmission epidemic system have been provided using the training, verification, and testing procedures to reduce the mean square error. Moreover, the exactness and capability of the stochastic SCGGNNs are approved through error histograms, regression values, correlation tests, and state transitions.https://doi.org/10.1038/s41598-023-43324-2 |
| spellingShingle | Najah AbuAli Muhammad Bilal Khan Zulqurnain Sabir A computational stochastic procedure for solving the epidemic breathing transmission system Scientific Reports |
| title | A computational stochastic procedure for solving the epidemic breathing transmission system |
| title_full | A computational stochastic procedure for solving the epidemic breathing transmission system |
| title_fullStr | A computational stochastic procedure for solving the epidemic breathing transmission system |
| title_full_unstemmed | A computational stochastic procedure for solving the epidemic breathing transmission system |
| title_short | A computational stochastic procedure for solving the epidemic breathing transmission system |
| title_sort | computational stochastic procedure for solving the epidemic breathing transmission system |
| url | https://doi.org/10.1038/s41598-023-43324-2 |
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