A fractional order numerical study for the influenza disease mathematical model
The motive of these investigations is to present the numerical performances of the fractional order mathematical influenza disease model (FO-MIDM) by designing the computational framework based on the stochastic Levenberg-Marquardt backpropagation neural networks (LMBNNs). The fractional order deriv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822006287 |
| _version_ | 1811163489055539200 |
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| author | Zulqurnain Sabir Salem Ben Said Qasem Al-Mdallal |
| author_facet | Zulqurnain Sabir Salem Ben Said Qasem Al-Mdallal |
| author_sort | Zulqurnain Sabir |
| collection | DOAJ |
| description | The motive of these investigations is to present the numerical performances of the fractional order mathematical influenza disease model (FO-MIDM) by designing the computational framework based on the stochastic Levenberg-Marquardt backpropagation neural networks (LMBNNs). The fractional order derivatives have been used to get more accurate performances of the MIDM as compared to the integer order. The MIDM is divided into four subcategories, (i) susceptible S(q), (ii) infected I(q), (iii) recovered R(q) and (iv) cross-immune people C(q). Three different cases based FO derivatives have been numerically presented by using the MIDM. The achieved results based on the MIDM have been presented by using the computing stochastic structure LMBNNs through the process of training, confirmation and testing to decrease the mean square error (MSE) values using the reference (data-based) results. To observe the competence, precision, capability and aptitude of the proposed computing structure LMBNNs, a comprehensive investigation is accessible by performing the correlation, MSE, error histograms, information of state transitions and regression analysis. The worth of LMBNNs procedure is validated through the overlapping of the results with good measures up to the accuracy of 5 to 7 decimals for solving the MIDM. |
| first_indexed | 2024-04-10T15:05:57Z |
| format | Article |
| id | doaj.art-00e9bef9278946c2b135b78151e44571 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-10T15:05:57Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-00e9bef9278946c2b135b78151e445712023-02-15T04:26:55ZengElsevierAlexandria Engineering Journal1110-01682023-02-0165615626A fractional order numerical study for the influenza disease mathematical modelZulqurnain Sabir0Salem Ben Said1Qasem Al-Mdallal2Department of Mathematical Sciences, UAE University, P. O. Box 15551, Al Ain, United Arab EmiratesCorresponding author.; Department of Mathematical Sciences, UAE University, P. O. Box 15551, Al Ain, United Arab EmiratesDepartment of Mathematical Sciences, UAE University, P. O. Box 15551, Al Ain, United Arab EmiratesThe motive of these investigations is to present the numerical performances of the fractional order mathematical influenza disease model (FO-MIDM) by designing the computational framework based on the stochastic Levenberg-Marquardt backpropagation neural networks (LMBNNs). The fractional order derivatives have been used to get more accurate performances of the MIDM as compared to the integer order. The MIDM is divided into four subcategories, (i) susceptible S(q), (ii) infected I(q), (iii) recovered R(q) and (iv) cross-immune people C(q). Three different cases based FO derivatives have been numerically presented by using the MIDM. The achieved results based on the MIDM have been presented by using the computing stochastic structure LMBNNs through the process of training, confirmation and testing to decrease the mean square error (MSE) values using the reference (data-based) results. To observe the competence, precision, capability and aptitude of the proposed computing structure LMBNNs, a comprehensive investigation is accessible by performing the correlation, MSE, error histograms, information of state transitions and regression analysis. The worth of LMBNNs procedure is validated through the overlapping of the results with good measures up to the accuracy of 5 to 7 decimals for solving the MIDM.http://www.sciencedirect.com/science/article/pii/S1110016822006287Neural networksNonlinearInfluenzaLevenberg-Marquardt backpropagationReference solutionsNumerical simulations |
| spellingShingle | Zulqurnain Sabir Salem Ben Said Qasem Al-Mdallal A fractional order numerical study for the influenza disease mathematical model Alexandria Engineering Journal Neural networks Nonlinear Influenza Levenberg-Marquardt backpropagation Reference solutions Numerical simulations |
| title | A fractional order numerical study for the influenza disease mathematical model |
| title_full | A fractional order numerical study for the influenza disease mathematical model |
| title_fullStr | A fractional order numerical study for the influenza disease mathematical model |
| title_full_unstemmed | A fractional order numerical study for the influenza disease mathematical model |
| title_short | A fractional order numerical study for the influenza disease mathematical model |
| title_sort | fractional order numerical study for the influenza disease mathematical model |
| topic | Neural networks Nonlinear Influenza Levenberg-Marquardt backpropagation Reference solutions Numerical simulations |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822006287 |
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