Support vector regression for the temperature-stimulated drug release

P. Ahadian and K. Parand

Chaos, Solitons & Fractals, 2022, vol. 165, issue P2

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.

Keywords: Temperate-stimulated drug release; Collocation LS-SVR; Bernstein Kernel; Fractional PDEs; Nonlinear dynamics (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077922010505
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:165:y:2022:i:p2:s0960077922010505

DOI: 10.1016/j.chaos.2022.112871

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().