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فهرست نشریات دارای اعتبار وزارت علوم، تحقیقات و فناوری
پایگاه استنادی علوم جهان اسلام (ISC)

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Hassani, Hossein, Avazzadeh, Z., Ezzatzadegan Jahromi, Sh., Ebadi, M. J.. (1405). Optimal solution of kidney function model via fractional clique polynomials neural network. سامانه مدیریت نشریات علمی, (), -. doi: 10.22034/cmde.2026.68172.3290
Hossein Hassani; Z. Avazzadeh; Sh. Ezzatzadegan Jahromi; M. J. Ebadi. "Optimal solution of kidney function model via fractional clique polynomials neural network". سامانه مدیریت نشریات علمی, , , 1405, -. doi: 10.22034/cmde.2026.68172.3290
Hassani, Hossein, Avazzadeh, Z., Ezzatzadegan Jahromi, Sh., Ebadi, M. J.. (1405). 'Optimal solution of kidney function model via fractional clique polynomials neural network', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22034/cmde.2026.68172.3290
Hassani, Hossein, Avazzadeh, Z., Ezzatzadegan Jahromi, Sh., Ebadi, M. J.. Optimal solution of kidney function model via fractional clique polynomials neural network. سامانه مدیریت نشریات علمی, 1405; (): -. doi: 10.22034/cmde.2026.68172.3290

Optimal solution of kidney function model via fractional clique polynomials neural network

Computational Methods for Differential Equations
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 اردیبهشت 1405    اصل مقاله (1.12 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2026.68172.3290
نویسندگان
Hossein Hassani* 1؛ Z. Avazzadeh2؛ Sh. Ezzatzadegan Jahromi3؛ M. J. Ebadi4
1Department of Mathematics, Anand International College of Engineering, Jaipur 303012, India.
2Stony Brook Institute at Anhui University, Anhui University, Hefei 230601, China.
3Department of Medicine, School of Medicine, Shiraz Nephro-Urology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
4DICEAM Department, Mediterranean University of Reggio Calabria, Via Graziella Feo di Vito, 89060 Reggio Calabria, Italy.
چکیده
We propose the fractional clique polynomials neural network (FCPNN), a hybrid architecture integrating neural networks with fractional clique polynomials (FCPs), to solve mathematical kidney function models (MKFMs) critical for clinical disease modeling. The FCPNN method employs time (in months) as an input variable, with FCPs as activation functions in the hidden layer and the arcsinh(t) function in the output layer, enhancing adaptability to nonlinear biological dynamics. We rigorously establish the method’s theoretical foundations through convergence analysis and proofs of solution existence and uniqueness. By incorporating Lagrange multipliers for optimization, FCPNN improves constraint handling and prediction accuracy. This work advances computational tools for kidney disease modeling, offering a robust framework for personalized medical applications.
کلیدواژه‌ها
Fractional clique polynomials neural network؛ Mathematical kidney function model؛ Optimization algorithm
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