Digital analysis of discrete fractional order cancer model by artificial intelligence
| dc.contributor.author | Khan, Aziz | |
| dc.contributor.author | Abdeljawad, Thabet | |
| dc.contributor.author | Abdel-Aty, Mahmoud | |
| dc.contributor.author | Almutairi, D.K. | |
| dc.date.accessioned | 2025-07-15T07:07:30Z | |
| dc.date.available | 2025-07-15T07:07:30Z | |
| dc.date.issued | 2025 | |
| dc.description | Breast cancer, one of the most predominant types of cancer affecting women globally, is indicated by four distinct stages that reveal the extent of its development and spread within the body [1]. Each stage characterizes a different level of severity and manages treatment based on factors such as metastasis, tumor size, and lymph node involvement [2]. In the first stage, known as carcinoma in situ, the cancerous cells are restricted to the lobules or ducts of the breast tissue and have not infected surrounding healthy tissue. | |
| dc.description.abstract | This article presents the approximate numerical solutions of the novel constructed Difference Caputo Order Cancer (DCOC) model utilizing the iterative numerical method combined with the neural network approach. The DCOC model is systemized for five variables and is further classified into four stages for investigation. Various fractional orders are considered to study the different scenarios of the DCOC model. The iterative systems of the DCOC model have been formulated by the iterative numerical method with neural networks associated with the computational operation of the Bayesian Regularization (BR), referred to as ENNs-BR. All the results of the DCOC model are compared by their performance. For the statistical study, the data of the model considered is partitioned into 75% and 25% in two parts. Finally, the results for performance, training state, error histogram, regression, and fit are illustrated graphically. keywords: Bayesian regularization, Discrete fractional order Caputo Cancer model, Iterative numerical method, Neural networks, Training state | |
| dc.identifier.citation | Khan, A., Abdeljawad, T., Abdel-Aty, M., & Almutairi, D. K. (2025). Digital analysis of discrete fractional order cancer model by artificial intelligence. Alexandria Engineering Journal, 118, 115-124. | |
| dc.identifier.doi | https://doi.org/10.1016/j.aej.2025.01.036 | |
| dc.identifier.uri | https://repository.adu.ac.ae/handle/1/7273 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.title | Digital analysis of discrete fractional order cancer model by artificial intelligence | |
| dc.type | Article |