Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning
In this article application of neural network using Levenberg–Marquardt Back-propagation is implemented on differential model to study and analyze ringworm infectious disease. The formulated system of differential equations is consisting of following parts, namely; S(t) population which is at verge...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Partial Differential Equations in Applied Mathematics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666818123000578 |
| _version_ | 1797390367117541376 |
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| author | Najah Alsubaie Qusain Haider Ali Hassan Ahmed M. Hassan Abdulkafi Mohammed Saeed |
| author_facet | Najah Alsubaie Qusain Haider Ali Hassan Ahmed M. Hassan Abdulkafi Mohammed Saeed |
| author_sort | Najah Alsubaie |
| collection | DOAJ |
| description | In this article application of neural network using Levenberg–Marquardt Back-propagation is implemented on differential model to study and analyze ringworm infectious disease. The formulated system of differential equations is consisting of following parts, namely; S(t) population which is at verge of being infected by ringworm, E(t) shows the environment effected by dermetophytosis fungus, I(t) whereas represent the infected individuals, R(t) and shows the population which has recovered from the infection. The solutions of different categories are represented by considering distinct datasets modeled and designed using LMB neural network. The numerical scheme Adam has been employed to establish a reference data set of the designed LMB neural network. The approximate outcomes of the SEIR based on dispersing and curing are discussed using the authentication, testing and training procedures to truncate the mean square error in function with help of LMB. The mean square error, regression, error histograms are generated to produce efficiency, effectiveness and correctness of proposed LMB neural network scheme. |
| first_indexed | 2024-03-08T23:10:41Z |
| format | Article |
| id | doaj.art-acfd1e5d81f9412bb2bad53dbdd56cf2 |
| institution | Directory Open Access Journal |
| issn | 2666-8181 |
| language | English |
| last_indexed | 2024-03-08T23:10:41Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Partial Differential Equations in Applied Mathematics |
| spelling | doaj.art-acfd1e5d81f9412bb2bad53dbdd56cf22023-12-15T07:26:44ZengElsevierPartial Differential Equations in Applied Mathematics2666-81812023-12-018100544Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learningNajah Alsubaie0Qusain Haider1Ali Hassan2Ahmed M. Hassan3Abdulkafi Mohammed Saeed4Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Mathematics, University of Gujrat, Gujrat 50700, Pakistan; Corresponding authors.Department of Mathematics, University of Gujrat, Gujrat 50700, Pakistan; Corresponding authors.Faculty of engineering, Future University in Egypt, EgyptDepartment of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi ArabiaIn this article application of neural network using Levenberg–Marquardt Back-propagation is implemented on differential model to study and analyze ringworm infectious disease. The formulated system of differential equations is consisting of following parts, namely; S(t) population which is at verge of being infected by ringworm, E(t) shows the environment effected by dermetophytosis fungus, I(t) whereas represent the infected individuals, R(t) and shows the population which has recovered from the infection. The solutions of different categories are represented by considering distinct datasets modeled and designed using LMB neural network. The numerical scheme Adam has been employed to establish a reference data set of the designed LMB neural network. The approximate outcomes of the SEIR based on dispersing and curing are discussed using the authentication, testing and training procedures to truncate the mean square error in function with help of LMB. The mean square error, regression, error histograms are generated to produce efficiency, effectiveness and correctness of proposed LMB neural network scheme.http://www.sciencedirect.com/science/article/pii/S2666818123000578Biological modelRingworm infectionArtificial neural networksReference resultsLevenberg–Marquardt back-propagation |
| spellingShingle | Najah Alsubaie Qusain Haider Ali Hassan Ahmed M. Hassan Abdulkafi Mohammed Saeed Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning Partial Differential Equations in Applied Mathematics Biological model Ringworm infection Artificial neural networks Reference results Levenberg–Marquardt back-propagation |
| title | Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning |
| title_full | Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning |
| title_fullStr | Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning |
| title_full_unstemmed | Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning |
| title_short | Analysis of non-linear RIM system and neural computing of ringworm spread using the Levenberg–Marquardt back propagated scheme: Supervised learning |
| title_sort | analysis of non linear rim system and neural computing of ringworm spread using the levenberg marquardt back propagated scheme supervised learning |
| topic | Biological model Ringworm infection Artificial neural networks Reference results Levenberg–Marquardt back-propagation |
| url | http://www.sciencedirect.com/science/article/pii/S2666818123000578 |
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