Browsing by Author "Seminario-Morales, Maria Veronica"

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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.
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
Techno-Economic Energy Optimization of the Off-Grid Electrical System with Power to Gas Storage Technology
(Penerbit UTHM, 2023-08) Huamán-Romaní, Yersi Luis; García Vilela, Lucy Mariella; Agreda, Henrry Wilfredo; Seminario-Morales, Maria Veronica; Sanchez-Prieto, Maria Gregoria; Gutiérrez-Gómez, Edgar; Serna-Silva, Giovanna Jackeline; Cuba Carbajal, Nestor; Añaños Bedriñana, Marco Antonio; Zenozain-Cordero, Carmen-Rosa
Abstract: Renewable energy sources (RESs) have undeniable advantages over the recent years not only to supply electrical demand but also electrical demand. However, maximum use of the RES’s power has always been challenging as high penetration of the RESs as well as their intermittent nature might compromise the distribution networks power flow constraints. This paper proposes optimal energy operation of the off-grid distribution network (DN) with participation of the power-to-gas (PtG) storage system. In this regard, PtG system is considered as an energy supplier in the DN. The natural gas generated by using PtG is applied to backup diesel generators for meeting demand at peak times. The objective functions in the system are modeled based on technical and economic modeling including minimize the operation cost and maximize the system reliability. The optimal energy operation in the two case studies is assumed considering non-participation and participation of the PtG system. To solving of the energy optimization, particle swarm optimization algorithm is proposed. Finally, proposed case studies under numerical simulation are implemented for validation of the participation of the PtG system.