An analysis of fractional piecewise derivative models of dengue transmission using deep neural network
This manuscript investigates a fractional piecewise dengue transmission model using singular and non-singular kernels. The existence results and uniqueness of the solution are established by using the approach of fixed point and in the framework of piecewise derivative and integral. To obtain the ap...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Taibah University for Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/16583655.2024.2340871 |
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| author | Mati ur Rahman Saira Tabassum Ali Althobaiti Waseem Saad Althobaiti |
| author_facet | Mati ur Rahman Saira Tabassum Ali Althobaiti Waseem Saad Althobaiti |
| author_sort | Mati ur Rahman |
| collection | DOAJ |
| description | This manuscript investigates a fractional piecewise dengue transmission model using singular and non-singular kernels. The existence results and uniqueness of the solution are established by using the approach of fixed point and in the framework of piecewise derivative and integral. To obtain the approximate solution of the considered models we apply a piecewise numerical iteration scheme which is based on Newton interpolation polynomials. Furthermore, the numerical scheme for piecewise derivatives encompasses singular and non-singular kernels. This study aims to enhance our understanding of dengue internal transmission dynamics by using a novel piecewise derivative approach that considers both singular and non-singular kernels. This work contributes to clarifying the concept of piecewise derivatives and their significance in understanding crossover dynamics. Moreover, a deep neural network approach is employed with high accuracy in training, testing, and validation of data to investigate the specified disease problem. This methodology is employed to thoroughly investigate the intricacies of the specified disease problem. |
| format | Article |
| id | doaj-art-9dcb8952b1594105a4d68a4e9ab77a03 |
| institution | OA Journals |
| issn | 1658-3655 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Taibah University for Science |
| spelling | doaj-art-9dcb8952b1594105a4d68a4e9ab77a032025-08-20T02:36:44ZengTaylor & Francis GroupJournal of Taibah University for Science1658-36552024-12-0118110.1080/16583655.2024.2340871An analysis of fractional piecewise derivative models of dengue transmission using deep neural networkMati ur Rahman0Saira Tabassum1Ali Althobaiti2Waseem3Saad Althobaiti4School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu, People's Republic of ChinaDepartment of Applied Sciences, National Textile University, Faisalabad, PakistanDepartment of Mathematics, College of Science, Taif University, Taif, Saudi ArabiaSchool of Mechnical Engineering, Jiangsu University, Zhenjiang, Jiangsu, People's Republic of ChinaDepartment of Sciences and Technology, Ranyah University Collage, Taif University, Taif, Saudi ArabiaThis manuscript investigates a fractional piecewise dengue transmission model using singular and non-singular kernels. The existence results and uniqueness of the solution are established by using the approach of fixed point and in the framework of piecewise derivative and integral. To obtain the approximate solution of the considered models we apply a piecewise numerical iteration scheme which is based on Newton interpolation polynomials. Furthermore, the numerical scheme for piecewise derivatives encompasses singular and non-singular kernels. This study aims to enhance our understanding of dengue internal transmission dynamics by using a novel piecewise derivative approach that considers both singular and non-singular kernels. This work contributes to clarifying the concept of piecewise derivatives and their significance in understanding crossover dynamics. Moreover, a deep neural network approach is employed with high accuracy in training, testing, and validation of data to investigate the specified disease problem. This methodology is employed to thoroughly investigate the intricacies of the specified disease problem.https://www.tandfonline.com/doi/10.1080/16583655.2024.2340871Dengue modelpiecewise derivativeCaputo derivativeÄtangana–Baleanu–Caputo derivativeNewton polynomials numerical methoddeep neural network |
| spellingShingle | Mati ur Rahman Saira Tabassum Ali Althobaiti Waseem Saad Althobaiti An analysis of fractional piecewise derivative models of dengue transmission using deep neural network Journal of Taibah University for Science Dengue model piecewise derivative Caputo derivative Ätangana–Baleanu–Caputo derivative Newton polynomials numerical method deep neural network |
| title | An analysis of fractional piecewise derivative models of dengue transmission using deep neural network |
| title_full | An analysis of fractional piecewise derivative models of dengue transmission using deep neural network |
| title_fullStr | An analysis of fractional piecewise derivative models of dengue transmission using deep neural network |
| title_full_unstemmed | An analysis of fractional piecewise derivative models of dengue transmission using deep neural network |
| title_short | An analysis of fractional piecewise derivative models of dengue transmission using deep neural network |
| title_sort | analysis of fractional piecewise derivative models of dengue transmission using deep neural network |
| topic | Dengue model piecewise derivative Caputo derivative Ätangana–Baleanu–Caputo derivative Newton polynomials numerical method deep neural network |
| url | https://www.tandfonline.com/doi/10.1080/16583655.2024.2340871 |
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