A numerical simulation of the fractional order Leptospirosis model using the supervise neural network
The aim of this work is to present the numerical simulations of the novel designed fractional order Leptospirosis model (FOLM) by using the strength of stochastic numerical supervised neural networks. This novel work provides the numerical study of the Leptospirosis model, which is classified into f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822003866 |
| _version_ | 1797978787896360960 |
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| author | Kanit Mukdasai Zulqurnain Sabir Muhammad Asif Zahoor Raja R. Sadat Mohamed R. Ali Peerapongpat Singkibud |
| author_facet | Kanit Mukdasai Zulqurnain Sabir Muhammad Asif Zahoor Raja R. Sadat Mohamed R. Ali Peerapongpat Singkibud |
| author_sort | Kanit Mukdasai |
| collection | DOAJ |
| description | The aim of this work is to present the numerical simulations of the novel designed fractional order Leptospirosis model (FOLM) by using the strength of stochastic numerical supervised neural networks. This novel work provides the numerical study of the Leptospirosis model, which is classified into five dynamics. Different values of the fractional order derivatives have been provided to solve the biological FOLM. The numerical formulations of the FOLM are obtained through the supervised neural networks (SNNs) along with the computational performances of the Levenberg-Marquardt backpropagation (LVMBP), i.e., SNNs-LVMBP. The correctness of the procedure is observed by using the comparative performances of the obtained and reference solutions. The statics are performed for these investigations as 74% and 13% for both certification and learning. The process of error histograms (EHs), recurrence, MSE, correlation, and state transitions (STs) will be performed to authenticate the capability, steadiness, accuracy, reliability, and fitness of the proposed procedure. |
| first_indexed | 2024-04-11T05:28:35Z |
| format | Article |
| id | doaj.art-c6b3836b2bde4a2a8fd3992f5dbeb278 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-11T05:28:35Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-c6b3836b2bde4a2a8fd3992f5dbeb2782022-12-23T04:39:30ZengElsevierAlexandria Engineering Journal1110-01682022-12-0161121243112441A numerical simulation of the fractional order Leptospirosis model using the supervise neural networkKanit Mukdasai0Zulqurnain Sabir1Muhammad Asif Zahoor Raja2R. Sadat3Mohamed R. Ali4Peerapongpat Singkibud5Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Mathematical Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates; Department 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, Zagazig Faculty of Engineering, Zagazig University, EgyptFaculty of Engineering and Technology, Future University, Cairo, EgyptDepartment of Applied Mathematics and Statistics, Faculty of Sciences and Liberal Arts, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand; Corresponding author.The aim of this work is to present the numerical simulations of the novel designed fractional order Leptospirosis model (FOLM) by using the strength of stochastic numerical supervised neural networks. This novel work provides the numerical study of the Leptospirosis model, which is classified into five dynamics. Different values of the fractional order derivatives have been provided to solve the biological FOLM. The numerical formulations of the FOLM are obtained through the supervised neural networks (SNNs) along with the computational performances of the Levenberg-Marquardt backpropagation (LVMBP), i.e., SNNs-LVMBP. The correctness of the procedure is observed by using the comparative performances of the obtained and reference solutions. The statics are performed for these investigations as 74% and 13% for both certification and learning. The process of error histograms (EHs), recurrence, MSE, correlation, and state transitions (STs) will be performed to authenticate the capability, steadiness, accuracy, reliability, and fitness of the proposed procedure.http://www.sciencedirect.com/science/article/pii/S1110016822003866Biological systemFractional order Leptospirosis modelSupervised neural networksNumerical resultsLevenberg-Marquardt backpropagation |
| spellingShingle | Kanit Mukdasai Zulqurnain Sabir Muhammad Asif Zahoor Raja R. Sadat Mohamed R. Ali Peerapongpat Singkibud A numerical simulation of the fractional order Leptospirosis model using the supervise neural network Alexandria Engineering Journal Biological system Fractional order Leptospirosis model Supervised neural networks Numerical results Levenberg-Marquardt backpropagation |
| title | A numerical simulation of the fractional order Leptospirosis model using the supervise neural network |
| title_full | A numerical simulation of the fractional order Leptospirosis model using the supervise neural network |
| title_fullStr | A numerical simulation of the fractional order Leptospirosis model using the supervise neural network |
| title_full_unstemmed | A numerical simulation of the fractional order Leptospirosis model using the supervise neural network |
| title_short | A numerical simulation of the fractional order Leptospirosis model using the supervise neural network |
| title_sort | numerical simulation of the fractional order leptospirosis model using the supervise neural network |
| topic | Biological system Fractional order Leptospirosis model Supervised neural networks Numerical results Levenberg-Marquardt backpropagation |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822003866 |
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