An Appropriate Fractional Narayana Polynomials Neural Network Method for a Mathematical Model of the Lung Cancer
Iranian Journal of Mathematical Chemistry, cilt.17, sa.2, ss.217-232, 2026 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 17 Sayı: 2
- Basım Tarihi: 2026
- Doi Numarası: 10.22052/ijmc.2026.258001.2093
- Dergi Adı: Iranian Journal of Mathematical Chemistry
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
- Sayfa Sayıları: ss.217-232
- Anahtar Kelimeler: Cancer cells, Fractional Narayana polynomials neural network, Immune cells, Lung cancer, Optimization algorithm
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
A mathematical model of lung cancer is used to analyze the dynamics of tumor growth and the interactions between cancer cells and immune cells. To obtain approximate solutions and improve understanding of the behavior of the state functions, a fractional Narayana polynomials neural network (FNPNN) with higher accuracy and better efficiency is proposed. For this purpose, we develop a method using a three-layer artificial neural network, which includes an input layer, a hidden layer, and an output layer. The fractional Narayana polynomials and arcsinh(t) function are utilized as activation functions for the hidden and output layers of the network, respectively. The lung cancer model is reduced to the problem of solving a system of algebraic equations through the use of FNPNN and the Lagrange multipliers method. All computations are performed using Maple and MATLAB software. The convergence analysis is discussed. The efficiency and versatility of our suggested approach are confirmed by numerical modeling examples. The technique proposed in this work can be effortlessly applied to other scientific or engineering problems, providing the potential for substantial efficiency gains while keeping accuracy at an acceptable level.