A computational framework to solve the nonlinear dengue fever SIR system

Muhammad Umar, Kusen, Muhammad Asif Zahoor Raja, Zulqurnain Sabir, Qasem Al-Mdallal

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

This study is relevant to present the numerical form of the nonlinear dengue fever SIR system are presented using the artificial neural networks along with the Levenberg-Marquardt backpropagation technique, i.e. ANNs-LMB. The procedures of ANNs-LMB are applied with three different sample data scales based on testing, training and authentication. The statistics to solve three cases of the nonlinear dengue fever based on susceptible, infected and recovered system are selected with 75%, 15% and 10% for training, validation and testing, respectively. To find the numerical results of the nonlinear dengue fever system, the reference dataset is designed on the basis of Adams scheme for the numerical solution. The numerical results based on the nonlinear dengue fever system are obtained through the ANNs-LMB to reduce the mean square error. In order to check the exactness, reliability, effectiveness and competence of the proposed ANNs-LMB, the numerical outcomes are proficient to the proportional measures using the topographies of the fitness attained in mean squared error sense, correlation, error histograms and regression.

Original languageEnglish
Pages (from-to)1821-1834
Number of pages14
JournalComputer Methods in Biomechanics and Biomedical Engineering
Volume25
Issue number16
DOIs
Publication statusPublished - 2022

Keywords

  • Levenberg-Marquardt backpropagation
  • Nonlinear dengue fever
  • SIR system
  • numerical measures
  • reference dataset

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Computer Science Applications

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