NUMERICAL ASSESSMENT OF THE BRAIN TUMOR GROWTH MODEL VIA FIBONACCI AND HAAR WAVELETS

Naied Ahmad Nayied (), Firdous Ahmad Shah, Kottakkaran Sooppy Nisar (), Mukhtar Ahmad Khanday () and Saima Habeeb ()
Additional contact information
Naied Ahmad Nayied: Department of Mathematics, University of Kashmir, Srinagar 190006, Jammu and kashmir, India
Firdous Ahmad Shah: ��Department of Mathematics, University of Kashmir, Anantnag 192101, Jammu and Kashmir, India
Kottakkaran Sooppy Nisar: ��Department of Mathematics, College of Arts and Science, Prince Sattam bin Abdulaziz University, Wadi Aldawaser, Saudi Arabia
Mukhtar Ahmad Khanday: Department of Mathematics, University of Kashmir, Srinagar 190006, Jammu and kashmir, India
Saima Habeeb: �Rufaida College of Nursing, Jamia Hamdard, New Delhi 110019, India

FRACTALS (fractals), 2023, vol. 31, issue 02, 1-17

Abstract: The main goal of this paper is to present a novel numerical scheme based on the Fibonacci wavelets for solving the brain tumor growth model governed by the Burgess equation. At the first instance, the Fibonacci-wavelet-based operational matrices of integration are obtained by following the well-known Chen–Hsiao technique. These matrices play a vital role in converting the said model into an algebraic system, which could be handled with any standard numerical method. To access the effect of medical treatment over the brain tumor growth, we have investigated both the linear and nonlinear cases of Burgess equation. The nonlinearity arising in the Burgess equation is handled by invoking the quasilinearization technique. In order to compare the efficiency of the Fibonacci-wavelet-based numerical technique, we formulated an analogous numerical scheme based on the Haar wavelets. Subsequently, both the methods are testified on several test problems and it is demonstrated that the Fibonacci wavelet method yields a much more stable solution and a better approximation than the Haar wavelet method.

Keywords: Brain Tumor; Gliomas; Burgess Equation; Fibonacci Wavelet; Haar Wavelet; Operational Matrices (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X23400170
Access to full text is restricted to subscribers

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:wsi:fracta:v:31:y:2023:i:02:n:s0218348x23400170

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X23400170

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().