Artificial intelligence computing analysis of fractional order COVID-19 epidemic model
Artificial intelligence plays a very prominent role in many fields, and of late, this term has been gaining much more popularity due to recent advances in machine learning. Machine learning is a sphere of artificial intelligence where machines are responsible for doing daily chores and are believed...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2023-08-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0163868 |
| _version_ | 1797689645669023744 |
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| author | Ali Raza Dumitru Baleanu Tahir Nawaz Cheema Emad Fadhal Rashid I. H. Ibrahim Nouara Abdelli |
| author_facet | Ali Raza Dumitru Baleanu Tahir Nawaz Cheema Emad Fadhal Rashid I. H. Ibrahim Nouara Abdelli |
| author_sort | Ali Raza |
| collection | DOAJ |
| description | Artificial intelligence plays a very prominent role in many fields, and of late, this term has been gaining much more popularity due to recent advances in machine learning. Machine learning is a sphere of artificial intelligence where machines are responsible for doing daily chores and are believed to be more intelligent than humans. Furthermore, artificial intelligence is significant in behavioral, social, physical, and biological engineering, biomathematical sciences, and many more disciplines. Fractional-order modeling of a real-world problem is a powerful tool for understanding the dynamics of the problem. In this study, an investigation into a fractional-order epidemic model of the novel coronavirus (COVID-19) is presented using intelligent computing through Bayesian-regularization backpropagation networks (BRBFNs). The designed BRBFNs are exploited to predict the transmission dynamics of COVID-19 disease by taking the dataset from a fractional numerical method based on the Grünwald–Letnikov backward finite difference. The datasets for the fractional-order mathematical model of COVID-19 for Wuhan and Karachi metropolitan cities are trained with BRBFNs for biased and unbiased input and target values. The proposed technique (BRBFNs) is implemented to estimate the integer and fractional-order COVID-19 spread dynamics. Its reliability, effectiveness, and validation are verified through consistently achieved accuracy metrics that depend on error histograms, regression studies, and mean squared error. |
| first_indexed | 2024-03-12T01:49:08Z |
| format | Article |
| id | doaj.art-3d3de117d52c44d68a075f48b36d7993 |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-03-12T01:49:08Z |
| publishDate | 2023-08-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-3d3de117d52c44d68a075f48b36d79932023-09-08T16:03:29ZengAIP Publishing LLCAIP Advances2158-32262023-08-01138085017085017-910.1063/5.0163868Artificial intelligence computing analysis of fractional order COVID-19 epidemic modelAli Raza0Dumitru Baleanu1Tahir Nawaz Cheema2Emad Fadhal3Rashid I. H. Ibrahim4Nouara Abdelli5Department of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonDepartment of Computer Science and Mathematics, Lebanese American University, Beirut, LebanonDepartment of Mathematics, University of Gujrat, Gujrat 52700, PakistanDepartment of Mathematics & Statistics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa, Hofuf 31982, Saudi ArabiaDepartment of Biological Sciences, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa, Hofuf 31982, Saudi ArabiaDepartment of Basic Science, King Faisal University, P.O. Box 400, Al-Ahsa, Hofuf 31982, Saudi ArabiaArtificial intelligence plays a very prominent role in many fields, and of late, this term has been gaining much more popularity due to recent advances in machine learning. Machine learning is a sphere of artificial intelligence where machines are responsible for doing daily chores and are believed to be more intelligent than humans. Furthermore, artificial intelligence is significant in behavioral, social, physical, and biological engineering, biomathematical sciences, and many more disciplines. Fractional-order modeling of a real-world problem is a powerful tool for understanding the dynamics of the problem. In this study, an investigation into a fractional-order epidemic model of the novel coronavirus (COVID-19) is presented using intelligent computing through Bayesian-regularization backpropagation networks (BRBFNs). The designed BRBFNs are exploited to predict the transmission dynamics of COVID-19 disease by taking the dataset from a fractional numerical method based on the Grünwald–Letnikov backward finite difference. The datasets for the fractional-order mathematical model of COVID-19 for Wuhan and Karachi metropolitan cities are trained with BRBFNs for biased and unbiased input and target values. The proposed technique (BRBFNs) is implemented to estimate the integer and fractional-order COVID-19 spread dynamics. Its reliability, effectiveness, and validation are verified through consistently achieved accuracy metrics that depend on error histograms, regression studies, and mean squared error.http://dx.doi.org/10.1063/5.0163868 |
| spellingShingle | Ali Raza Dumitru Baleanu Tahir Nawaz Cheema Emad Fadhal Rashid I. H. Ibrahim Nouara Abdelli Artificial intelligence computing analysis of fractional order COVID-19 epidemic model AIP Advances |
| title | Artificial intelligence computing analysis of fractional order COVID-19 epidemic model |
| title_full | Artificial intelligence computing analysis of fractional order COVID-19 epidemic model |
| title_fullStr | Artificial intelligence computing analysis of fractional order COVID-19 epidemic model |
| title_full_unstemmed | Artificial intelligence computing analysis of fractional order COVID-19 epidemic model |
| title_short | Artificial intelligence computing analysis of fractional order COVID-19 epidemic model |
| title_sort | artificial intelligence computing analysis of fractional order covid 19 epidemic model |
| url | http://dx.doi.org/10.1063/5.0163868 |
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