Mohamed Reda Ali Mohamed
Asst. Professor
An efficient computational procedure to solve the biological nonlinear Leptospirosis model using the genetic algorithms
Soft Computing • 2024
Publication Information
Authors
1,6
Zulqurnain Sabir
2,3
•
Mohamed R. Ali
•
Muhammad Asif Zahoor Raja
4
5
•
R. Sadat
Keywords
Keywords Leptospirosis disease model Nonlinear Artificial neural networks Active-set algorithm Reference
solutions Statistical procedures
Journal
Soft Computing
Publisher
Not Available
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
Abstract
The purpose of this work is to present the numerical solutions of the nonlinear mathematical Leptospirosis disease (LD)
model using the computational performances of the artificial neural networks (ANNs) along with the optimization procedures
based on the global search genetic algorithm (GA) and local search active-set algorithm (ASA) scheme, i.e.,
ANNs-GA-ASA. LD is a zoonotic disease that is occurring in the whole world, obtained by rodents that become the reason
of death in the people. The LD model based on the susceptible-infected-recovered, i.e., SIR, is used to solve the mechanisms
of disease spread. The optimization of an error-based fitness function, which is constructed through the differential
mathematical system using the hybrid computing proficiencies of the ANNs-GA-ASA for solving the LD model. The
stochastic ANNs-GA-ASA procedures are applied to the LD model to authenticate the precision, exactness, reliability and
competence of the ANNs-GA-ASA. The obtained results using the ANNs-GA-ASA of the LD model will be compared
using the Runge–Kutta scheme, which authenticate the importance of the ANNs-GA-ASA. Furthermore, statistical analysis
through different actions for the LD model approve the convergence and precision of the ANNs-GA-ASA.
The purpose of this work is to present the numerical solutions of the nonlinear mathematical Leptospirosis disease (LD)
model using the computational performances of the artificial neural networks (ANNs) along with the optimization procedures
based on the global search genetic algorithm (GA) and local search active-set algorithm (ASA) scheme, i.e.,
ANNs-GA-ASA. LD is a zoonotic disease that is occurring in the whole world, obtained by rodents that become the reason
of death in the people. The LD model based on the susceptible-infected-recovered, i.e., SIR, is used to solve the mechanisms
of disease spread. The optimization of an error-based fitness function, which is constructed through the differential
mathematical system using the hybrid computing proficiencies of the ANNs-GA-ASA for solving the LD model. The
stochastic ANNs-GA-ASA procedures are applied to the LD model to authenticate the precision, exactness, reliability and
competence of the ANNs-GA-ASA. The obtained results using the ANNs-GA-ASA of the LD model will be compared
using the Runge–Kutta scheme, which authenticate the importance of the ANNs-GA-ASA. Furthermore, statistical analysis
through different actions for the LD model approve the convergence and precision of the ANNs-GA-ASA.