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Support vector regression for the temperature-stimulated drug release

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  • Ahadian, P.
  • Parand, K.

Abstract

This paper presents a new coupled nonlinear convection–diffusion–reaction model for the temperature-stimulated drug release and a numerical algorithm based on a machine learning technique. By incorporating the fractional derivatives into the model, we take advantage of anomalous diffusion in biological tissues. In the proposed technique a least-squares support vector regression is developed for the numerical simulation of the problem. The Bernstein polynomials are used as the kernel function and some training points are considered for the residual system of the coupled problem in an inverse process. Some numerical experiments are carried out to support the method and its spectral convergence.

Suggested Citation

  • Ahadian, P. & Parand, K., 2022. "Support vector regression for the temperature-stimulated drug release," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p2:s0960077922010505
    DOI: 10.1016/j.chaos.2022.112871
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    References listed on IDEAS

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    1. Parand, K. & Aghaei, A.A. & Jani, M. & Ghodsi, A., 2021. "A new approach to the numerical solution of Fredholm integral equations using least squares-support vector regression," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 180(C), pages 114-128.
    2. Pakniyat, A. & Parand, K. & Jani, M., 2021. "Least squares support vector regression for differential equations on unbounded domains," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
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    Cited by:

    1. Ali Mehrban & Pegah Ahadian, 2024. "An adaptive network-based approach for advanced forecasting of cryptocurrency values," Papers 2401.05441, arXiv.org, revised Feb 2024.

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