Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Adaptive learning for providing inclusive contents based on student profile in digital education(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Alvarado Reyes, Ignacio; Molina Espinosa, José Martín; emipsanchez; Icaza Longoria, Inés Alvarez; Suárez Brito, Paloma; School of Engineering and Sciences; Campus Estado de MéxicoThe application of artificial intelligence technologies in educational fields has been increasing in the last years, especially with the implementation of adaptive learning technologies, designed to monitor different characteristics of students and provide them with content and suggestions aimed at improving their performance and avoiding problems they may have on digital platforms. In this study, the reference framework for student classification was explored with a proposal of the contents and accessibility functions that could be applied based on their learning characteristics, complemented by an implementation of adaptive learning technologies consisting of a classifier based on the decision tree algorithm that automatically processes student data and classify them within the classes defined in the framework. For the implementation of the classifier, it was trained with two data sets, initially with data generated in the laboratory and later with experimental data, obtained through a survey aimed at higher education students. Both instances of the trained algorithm demonstrated high accuracy for the classification process (99.98% with synthetic data and 95.94% with experimental data). Subsequently, through the same survey, the suggestions related to the classes assigned to the students were validated, as well as the suggested accessibility features and content. The suggestions seem to have a favorable acceptance range with rejection percentages between 0% and 6% for the content selections and between 14% and 34% for the accessibility options. With this dynamic implementation of educational content and digital accessibility features, we seek to provide personalized learning for different student profiles while seeking to implement more features related to compliance with concerns about diversity and inclusion.
- A two-phase optimization model that considers risk and accessibility for vaccine allocation and health-care units location(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-03) Martínez Fantini, Linda Sofía; REGIS HERNANDEZ, FABIOLA; 331834; Regis Hernández, Fabiola; puemcuervo, emipsanchez; Mora Ochomogo, Elma Irais; School of Engineering and Sciences; Campus Monterrey; Mora Vargas, JaimeIn December 2019, the novel virus SARS-CoV-2 appeared causing the COVID-19 pandemic. Healthcare systems globally were primary affected by it due to the increase on medical attention and Intensive Care Units (ICUs) demand. Particularly, Mexico belonged among the top 10 coun- tries with the highest number of confirmed cases, and within the top 5 in global deaths. These indicators required federal authorities to mount complex response actions to eradicate it. One of the concerns to deal with hospitals overload is an effective vaccination plan. For this, we propose a two-phase model to prioritize the Mexican entities with higher levels of vulnerability and risk of hospitalization to address the demand allocation problem in an equitable way. The vulnerability index is obtained through data analysis of a Mexican open database, and the model considers a two-dose vaccination program, and a monthly time horizon. The second phase con- sists of a Maximal Covering Location problem that maximizes a set of accessibility indicators to locate the facilities to cover the maximum Mexican population possible. The models are solved in Gurobi Optimizer commercial software and provides the optimal solution within seconds. The results obtained in the allocation model showed that the first vaccine lots are assigned to Ciudad de Me ́xico which is the entity with highest risk of exposure, and, by August 2021 at least 71% of the population is immunized with vaccine. The second model exposes that when enabling 100% of the available facilities, only 57% of the municipalities has access to vaccination. Moreover a sensitivity analysis is employed to evaluate the effect of the radii and the weights in the solution. For future work, we propose the implementation of mobile units to enhance the accessibility. This way providing an effective and equitable solution for Mexico.
