Browsing by Author "Botmart, Thongchai"

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A stochastic computational scheme for the computer epidemic virus with delay effects.
(American Institute of Mathematical Sciences, 2022-09) Weera, Wajaree; Botmart, Thongchai; La-Inchua, Teerapong; Sabir, Zulqurnain; Sandoval Núñez, Rafaél Artidoro; Abukhaled, Marwan I.; Guirao, Juan Luis García
This work aims to provide the numerical performances of the computer epidemic virus model with the time delay effects using the stochastic Levenberg-Marquardt backpropagation neural networks (LMBP-NNs). The computer epidemic virus model with the time delay effects is categorized into four dynamics, the uninfected S(x) computers, the latently infected L(x) computers, the breakingout B(x) computers, and the antivirus PC’s aptitude R(x). The LMBP-NNs approach has been used to numerically simulate three cases of the computer virus epidemic system with delay effects. The stochastic framework for the computer epidemic virus system with the time delay effects is provided using the selection of data with 11%, 13%, and 76% for testing, training, and verification together with 15 neurons. The proposed and data-based Adam technique is overlapped to execute the LMBP-NNs method’s exactness. The constancy, authentication, precision, and capability of the LMBP-NNs scheme are perceived with the analysis of the state transition measures, regression actions, correlation performances, error histograms, and mean square error measures.
A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III
(Tech Science Press, 2022-12) Ruttanaprommarin, Naret; Sabir, Zulqurnain; Sandoval Núñez, Rafaél Artidoro; Az-Zo’bi, Emad A.; Weera, Wajaree; T.; Botmart, Thongchai; Zamart, Chantapish
The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs). The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past. Three different cases based on the delay differential system with the Holling 3rd type of the functional response have been used to solve through the proposed LVMBPNNs solver. The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification. Thirteen numbers of neurons have been used based on the input, hidden, and output layers structure for solving the delay differential model with the Holling 3rd type of functional response. The correctness of the proposed stochastic scheme is observed by using the comparison performances of the proposed and reference data-based Adam numerical results. The authentication and precision of the proposed solver are approved by analyzing the state transitions, regression performances, correlation actions, mean square error, and error histograms.
Artificial neural network procedures for the waterborne spread and control of diseases.
(American Institute of Mathematical Sciences, 2022-11) Ruttanaprommarin, Naret; Sabir, Zulqurnain; Sandoval Núñez, Rafaél Artidoro; Salahshour, Soheil; García Guirao, Juan Luis; Weera, Wajaree; Botmart, Thongchai; Klamnoi, Anucha
bstract: In this study, a nonlinear mathematical SIR system is explored numerically based on the dynamics of the waterborne disease, e.g., cholera, that is used to incorporate the delay factor through the antiseptics for disease control. The nonlinear mathematical SIR system is divided into five dynamics, susceptible X(u), infective Y(u), recovered Z(u) along with the B(u) and Ch(u) be the contaminated water density. Three cases of the SIR system are observed using the artificial neural network (ANN) along with the computational Levenberg-Marquardt backpropagation (LMB) called ANNLMB. The statistical performances of the SIR model are provided by the selection of the data as 74% for authentication and 13% for both training and testing, together with 12 numbers of neurons. The exactness of the designed ANNLMB procedure is pragmatic through the comparison procedures of the proposed and reference results based on the Adam method. The substantiation, constancy, reliability, precision, and ability of the proposed ANNLMB technique are observed based on the state transitions measures, error histograms, regression, correlation performances, and mean square error values.
Dynamics of Fractional Differential Model for Schistosomiasis Disease.
(Tech Science Press, 2022-03) Botmart, Thongchai; Weera, Wajaree; Zahoor Raja, Muhammad Asif; Sabir, Zulqurnain; Hiader, Qusain; Cieza Altamirano, Gilder; Muro Solano, Plinio Junior; Tesén Arroyo, Alfonso
In the present study, a design of a fractional order mathematical model is presented based on the schistosomiasis disease. To observe more accurate performances of the results, the use of fractional order derivatives in the mathematical model is introduce based on the schistosomiasis disease is executed. The preliminary design of the fractional order mathematical model focused on schistosomiasis disease is classified as follows: uninfected with schistosomiasis, infected with schistosomiasis, recovered from infection, susceptible snail unafflicted with schistosomiasis disease and susceptible snail afflicted with this disease. The solutions to the proposed system of the fractional order mathematical model will be presented using stochastic artificial neural network (ANN) techniques in conjunction with the LevenbergMarquardt backpropagation (LMBP), referred to as ANN-LMBP. To illustrate the preciseness of the ANN-LMBP method, mathematical presentations of three different values focused on fractional order will be performed. These statics performances are taken in these investigations are 78% and 11% for both learning and certification. The accuracy of the ANN-LMBP method is determined by comparing the values obtained by the database Adams-Bash forth-Moulton scheme. The simulation-based error histograms (EHs), MSE, recurrence, and state transitions (STs) will be offered to achieve the capability,m accuracy, steadiness, abilities, and finesse of the ANN-LMBP method.
Swarming Computational Techniques for the Influenza Disease System
(Tech Science Press, 2022-05) Noinang, Sakda; Sabir, Zulqurnain; Cieza Altamirano, Gilder; Zahoor Raja, Muhammad Asif; Sánchez-Chero, Manuel Jesus; Seminario-Morales, Maria Veronica; Weera, Wajaree; Botmart, Thongchai
Abstract: The current study relates to designing a swarming computational paradigm to solve the influenza disease system (IDS). The nonlinear system’s mathematical form depends upon four classes: susceptible individuals, infected people, recovered individuals and cross-immune people. The solutions of the IDS are provided by using the artificial neural networks (ANNs) together with the swarming computational paradigm-based particle swarm optimization (PSO) and interior-point scheme (IPA) that are the global and local search approaches. The ANNs-PSO-IPA has never been applied to solve the IDS. Instead a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the PSOIPA. The correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions (reference solutions). An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the PSOIPA. Furthermore, the reliability and competence of the proposed computing method are enhanced through the statistical performances