Browsing by Author "Sabir, Zulqurnain"

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A neuro Meyer wavelet neural network procedure for solving the nonlinear Leptospirosis model.
(Elsevier, 2023-06) Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Ali, Mohamed R.; Sadat, Rahma; Fathurrochman, Irwan; Sandoval Núñez, Rafaél Artidoro; Bhat, Shahid Ahmad
The aim of such work is to design a Meyer wavelet neural network (WNN) for solving the mathematical form of the Leptospirosis disease model (LDM). The global and local search optimization schemes based on the genetic algorithm (GA) and active-set algorithm (ASA) are used in this study. Leptospirosis is an infection spread by rodents, which is found in the world and causes fatalities in humans. The mathematical LDM model form consists of susceptible-infected-recovered (SIR), which is based on the disease spread processes. A fitness function is designed by using the mathematical LMD and then optimized by the hybridization of the GAASA. For the correctness, and capability of the Meyer WNN along with the procedures of GAASA, the comparison of the obtained and reference results is provided. Moreover, the reducible absolute error provides the efficiency of the proposed Meyer WNN along with the procedures of GAASA. The statistical observations are also provided to authenticate the convergence of the stochastic Meyer WNN along with the procedures of GAASA.
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 intelligent investigations for the dynamics of the bone transformation mathematical model.
(Elsevier, 2022-10) Cholamjiak, Watcharaporn; Sabir, Zulqurnain; Zahoor Raja, Muhammad Asif; Sánchez-Chero, Manuel Jesus; Oseda Gago, Dulio; Sánchez-Chero, José Antonio; Seminario-Morales, Maria Veronica; Oseda Gago, Marco Antonio; Agurto Cherre, Cesar Augusto; Cieza Altamirano, Gilder
In this study, the stochastic numerical solutions of the fractional myeloma bone disease system (FMBDS) have been presented. The fractional order investigation provides more accurate solutions of the FMBDS. The FMBDS is classified into three dynamics and the solution of each class is presented by using the artificial neural network enhanced by the scale conjugate gradient procedures (ANN-SCGPs). Three different fractional order performances have been used to present the solutions of the FMBDS by applying the ANN-SCGPs. The statics is chosen as 11%, 12% and 77% for training, testing and verification. Twelve number of hidden neurons with input and output layers have been proposed for the FMBDS. The comparison of proposed and reference solutions is performed that shows the accuracy of the ANN-SCGPs. The consistency, validity, precision, and capability of the ANN-SCGPs can be judged based on the state transitions values, regression actions, correlation behaviors, error histograms, and mean square error data.
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.
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.
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.
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.
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