Annals of Mathematical Sciences and Applications

Volume 9 (2024)

Number 2

Uncertainty analysis of the tumour population logistic model in a fuzzy context

Pages: 309 – 339

DOI: https://dx.doi.org/10.4310/AMSA.2024.v9.n2.a2

Authors

Aadil Rashid Sheergojri (Department of Mathematics and Actuarial Science, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India)

Pervaiz Iqbal (Department of Mathematics and Actuarial Science, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India)

Shahid Ahmad Bhat (LUT Business School, Lappeenranta-Lahti University of Technology (LUT), Lappeenranta, Finland)

Rubeena Khaliq (Department of Mathematics Chandigarh University, Gharuan, Mohali, India)

J. F. Gómez-Aguilar (CONACyT-Tecnológico Nacional de México/CENIDET, Cuernavaca, Morelos, Mexico)

Abstract

This research depicts the tumour logistic equation in an uncertain environment, with the tumour inception, growth rate, both the inception and growth rate being fuzzy, and all the parameters beingfuzzy determinants to minimise tumour ambiguity and get the degree of accuracy. The author here perceives the cumulative tumour at a specific time as fuzzy, with the possibility distribution function(PDF) being analysed by the tumour inception $(n_0)$ growth rate $(\gamma)$, and threshold $(K)$. Additionally, this work indicates the tumour’s anticipated cell population in the maximal time interval. Finally, numerically simulated results relying on the logistic tumour growth model in an uncertain environment are been acquired using MATLAB and illustrated to verify the model’s accuracy.

Keywords

tumour modelling, fuzzy mathematics, fuzzy logistic growth equation, possibility distribution function

2010 Mathematics Subject Classification

Primary 34A07, 90Cxx, 93A30. Secondary 00A71, 03E72.

Full Text (PDF format)

Received 5 April 2023

Accepted 15 May 2023

Published 15 August 2024