Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model
The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e.,...
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| Format: | Article |
| Language: | English English |
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Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | https://eprints.ums.edu.my/id/eprint/32623/1/Soft%20computing%20paradigms%20to%20find%20the%20numerical%20solutions%20of%20a%20nonlinear%20influenza%20disease%20model.pdf https://eprints.ums.edu.my/id/eprint/32623/3/Soft%20computing%20paradigms%20to%20find%20the%20numerical%20solutions%20of%20a%20nonlinear%20influenza%20disease%20model%20_ABSTRACT.pdf |
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| author | Zulqurnain Sabir Ag. Asri Ag. Ibrahim Muhammad Asif Zahoor Raja Kashif Nisar Muhammad Umar Joel J. P. C. Rodrigues Samy R. Mahmoud |
| author_facet | Zulqurnain Sabir Ag. Asri Ag. Ibrahim Muhammad Asif Zahoor Raja Kashif Nisar Muhammad Umar Joel J. P. C. Rodrigues Samy R. Mahmoud |
| author_sort | Zulqurnain Sabir |
| collection | UMS |
| description | The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system. |
| first_indexed | 2024-03-06T03:16:05Z |
| format | Article |
| id | ums.eprints-32623 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:16:05Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | ums.eprints-326232022-05-19T01:26:57Z https://eprints.ums.edu.my/id/eprint/32623/ Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model Zulqurnain Sabir Ag. Asri Ag. Ibrahim Muhammad Asif Zahoor Raja Kashif Nisar Muhammad Umar Joel J. P. C. Rodrigues Samy R. Mahmoud QA71-90 Instruments and machines The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32623/1/Soft%20computing%20paradigms%20to%20find%20the%20numerical%20solutions%20of%20a%20nonlinear%20influenza%20disease%20model.pdf text en https://eprints.ums.edu.my/id/eprint/32623/3/Soft%20computing%20paradigms%20to%20find%20the%20numerical%20solutions%20of%20a%20nonlinear%20influenza%20disease%20model%20_ABSTRACT.pdf Zulqurnain Sabir and Ag. Asri Ag. Ibrahim and Muhammad Asif Zahoor Raja and Kashif Nisar and Muhammad Umar and Joel J. P. C. Rodrigues and Samy R. Mahmoud (2021) Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model. Applied Sciences (Switzerland), 11. pp. 1-16. ISSN 2076-3417 https://www.mdpi.com/2076-3417/11/18/8549/htm https://doi.org/10.3390/app11188549 https://doi.org/10.3390/app11188549 |
| spellingShingle | QA71-90 Instruments and machines Zulqurnain Sabir Ag. Asri Ag. Ibrahim Muhammad Asif Zahoor Raja Kashif Nisar Muhammad Umar Joel J. P. C. Rodrigues Samy R. Mahmoud Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model |
| title | Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model |
| title_full | Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model |
| title_fullStr | Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model |
| title_full_unstemmed | Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model |
| title_short | Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model |
| title_sort | soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model |
| topic | QA71-90 Instruments and machines |
| url | https://eprints.ums.edu.my/id/eprint/32623/1/Soft%20computing%20paradigms%20to%20find%20the%20numerical%20solutions%20of%20a%20nonlinear%20influenza%20disease%20model.pdf https://eprints.ums.edu.my/id/eprint/32623/3/Soft%20computing%20paradigms%20to%20find%20the%20numerical%20solutions%20of%20a%20nonlinear%20influenza%20disease%20model%20_ABSTRACT.pdf |
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