https://journals.aijr.org/index.php/jmsm/issue/feed Journal of Modeling and Simulation of Materials 2021-03-30T14:25:04+00:00 Editorial Office [JMSM] jmsm@aijr.in Open Journal Systems <p align="justify"><a title="Click for Journal homepage" href="https://doi.org/10.21467/jmsm" target="_blank" rel="noopener"><img style="float: right; padding-left: 15px; padding-right: 5px;" src="/public/site/images/aabahishti/cover_page_JMSM.jpg" alt="JMSM"></a>Journal of Modeling and Simulation of Materials (JMSM) is an international journal dedicated to the latest advancements in modeling and simulation of materials published by&nbsp;AIJR Publisher. JMSM (<em>J. Mod. Sim. Mater.</em>) invites scientists and engineers in all aspects of modeling and simulation of materials in chemistry, physics, material sciences, engineering and technology to publish the original full-length research papers, timely state-of-the-art reviews and short communications covering the fundamental and applied research.&nbsp;<br>Journal of Modeling and Simulation of Materials is registered with CrossRef with doi:10.21467/jmsm having&nbsp;ISSN:&nbsp;2582-2365 [online].</p> https://journals.aijr.org/index.php/jmsm/article/view/3386 Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators 2020-11-30T16:25:16+00:00 Donata D Acula ddacula@ust.edu.ph <p>This paper employed the intelligent approach based on machine learning categorized as base and ensemble methods in classifying the disaster risk in the Philippines. It focused on the Decision Trees, Support Vector Machine, Adaptive Boosting Algorithm with Decision Trees, and Support Vector Machine as base estimators. The research used the Exponential Regression for missing value imputation and converted the number of casualties, damaged houses, and properties into five (5) risk levels using Quantile Method. The 10-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Decision Trees and Adaptive Decision Trees are the most suitable models for the disaster data with the score of more than 90%, more than 75%, more than 75% in all the classification metrics (accuracy, precision, recall f1-score) when applied to classification risk levels of casualties, damaged houses and damaged properties respectively.</p> 2021-06-03T00:00:00+00:00 Copyright (c) 2021 Donata D Acula https://journals.aijr.org/index.php/jmsm/article/view/3572 Ground-state Shallow-donor Binding Energy in (In,Ga)N/GaN Double QWs Under Temperature, Size, and the Impurity Position Effects 2021-02-25T18:44:34+00:00 Redouane En-nadir redouane.en-nadir@usmba.ac.ma Haddou El Ghazi redouane.en-nadir@usmba.ac.ma Anouar Jorio a_jorio@hotmail.com Izeddine Zorkani izorkani@hotmail.com <p>In this paper, we study the hydrogen-like donor-impurity binding energy of the ground-state change as a function of the well width under the effect of temperature, size, and impurity position. Within the framework of the effective mass approximation, the Schrodinger-Poisson equation has been solved taken account an on-center hydrogen-like impurity in double QWs with rectangular finite confinement potential profile for 10% of indium concentration in the (well region). The eigenvalues and their correspondent eigenvectors have been obtained by the fined element method (FEM). The obtained results are in good agreement with the literature and show that the temperature, size, and the impurity position have a significant impact on the binding energy of a hydrogen-like impurity in symmetric double coupled quantum wells based on non-polar wurtzite (In,Ga) N/GaN core/Shell.</p> 2021-03-30T00:00:00+00:00 Copyright (c) 2021 Redouane En-nadir, Haddou El Ghazi, Anouar Jorio, Izeddine Zorkani