Browsing by Author "Wajaree Weera"

Now showing 1 - 2 of 2

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses.
(Elsevier, 2022-08) Botmart, Thongchai;; Sabir, Zulqurnain; Javeed, Shumaila; Sandoval Núñez, Rafaél Artidoro; Wajaree Weera; Ali, Mohamed R.; Sadat, Rahma
The current work aims to design a computational framework based on artificial neural networks (ANNs) and the optimization procedures of global and local search approach to solve the nonlinear dynamics of the spread of COVID-19, i.e., the SEIR-NDC model. The combination of the Genetic algorithm (GA) and active-set approach (ASA), i.e., GA-ASA, works as a global-local search scheme to solve the SEIR-NDC model. An error-based fitness function is optimized through the hybrid combination of the GA-ASA by using the differential SEIR-NDC model and its initial conditions. The numerical performances of the SEIR-NDC nonlinear model are presented through the procedures of ANNs along with GA-ASA by taking ten neurons. The correctness of the designed scheme is observed by comparing the obtained results based on the SEIR-NDC model and the reference Adams method. The absolute error performances are performed in suitable ranges for each dynamic of the SEIR-NDC model. The statistical analysis is provided to authenticate the reliability of the proposed scheme. Moreover, performance indices graphs and convergence measures are provided to authenticate the exactness and constancy of the proposed stochastic scheme.
Stochastic Computational Heuristic for the Fractional Biological Model Based on Leptospirosis.
(Tech Science Press, 2022-10) Sabir, Zulqurnain; Sánchez-Chero, Manuel Jesus; Zahoor Raja, Muhammad Asif; Cieza Altamirano, Gilder; Seminario-Morales, Maria Veronica; Fernández Vásquez, José Arquímedes; Purihuamán Leonardo, Celso Nazario; Thongchai Botmart; Wajaree Weera
The purpose of these investigations is to find the numerical outcomes of the fractional kind of biological system based on Leptospirosis by exploiting the strength of artificial neural networks aided by scale conjugate gradient, called ANNs-SCG. The fractional derivatives have been applied to get more reliable performances of the system. The mathematical form of the biological Leptospirosis system is divided into five categories, and the numerical performances of each model class will be provided by using the ANNs-SCG. The exactness of the ANNs-SCG is performed using the comparison of the reference and obtained results. The reference solutions have been obtained by using the Adams numerical scheme. For these investigations, the data selection is performed at 82% for training, while the statics for both testing and authentication is selected as 9%. The procedures based on the,recurrence, mean square error, error histograms, regression, state transitions, and correlation will be accomplished to validate the fitness, accuracy, and reliability of the ANNs-SCG scheme.