TY - JOUR
T1 - Digital analysis of discrete fractional order cancer model by artificial intelligence
AU - Khan, Aziz
AU - Abdeljawad, Thabet
AU - Abdel-Aty, Mahmoud
AU - Almutairi, D. K.
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/4
Y1 - 2025/4
N2 - 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.
AB - 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.
KW - Bayesian regularization
KW - Discrete fractional order Caputo Cancer model
KW - Iterative numerical method
KW - Neural networks
KW - Training state
UR - http://www.scopus.com/inward/record.url?scp=85215421622&partnerID=8YFLogxK
U2 - 10.1016/j.aej.2025.01.036
DO - 10.1016/j.aej.2025.01.036
M3 - Article
AN - SCOPUS:85215421622
SN - 1110-0168
VL - 118
SP - 115
EP - 124
JO - Alexandria Engineering Journal
JF - Alexandria Engineering Journal
ER -
