1. Repositorio Institucional UAQ
  2. Tesis
  3. Facultad de Ingeniería
  4. Maestría
  5. Maestría en Ciencias en Inteligencia Artificial
Por favor, use este identificador para citar o enlazar este ítem: https://ri-ng.uaq.mx/handle/123456789/11895
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dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0es_ES
dc.contributorMarco Antonio Aceves Fernándezes_ES
dc.creatorJuan Salvador Toledo Rioses_ES
dc.date.accessioned2025-07-02T18:31:12Z-
dc.date.available2025-07-02T18:31:12Z-
dc.date.issued2026-07-24-
dc.identifier.urihttps://ri-ng.uaq.mx/handle/123456789/11895-
dc.descriptionCognitive ability is a very complex neurological process and at the same time very studied, especially in the process of attention due to the high impact it has on the quality of life in people, however, the detection and classification of attention levels remain a challenge due to the scarcity of data to work with, as well as the subjectivity of the indicators when measuring the levels of attention. The present work intends to develop an Artificial Intelligence model applied to eye trajectory data for the classification of attention levels. We worked with a public database, using a Convolutional Neural Network to extract the features of the trajectories, using the feature vectors as input data in different Machine Learning models; in addition, a methodology was proposed to generate and validate the increase of data due to the imbalance of classes in the database. The logistic regression model was selected to perform the classification due to its adaptability to the data after testing, in addition to being optimized using the Grid Search technique. In conclusion, a hybrid model was developed and optimized incorporating Deep Learning techniques with Machine Learning, proving that a model can accurately classify reaching an accuracy higher than 95%, surpassing previous research. This study demonstrated the potential of working with non-invasive techniques using Artificial Intelligence in the classification of attention levels.es_ES
dc.formatpdfes_ES
dc.format.extent1 recurso en línea (84 páginas)es_ES
dc.format.mediumcomputadoraes_ES
dc.language.isospaes_ES
dc.publisherUniversidad Autonoma de Querétaroes_ES
dc.relation.requiresSies_ES
dc.rightsembargoedAccesses_ES
dc.subjectArtificial Intelligencees_ES
dc.subjectNeural Networkses_ES
dc.subjectMachine Learninges_ES
dc.subjectClassificationes_ES
dc.subjectAttentiones_ES
dc.subjectData Augmentationes_ES
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes_ES
dc.titleDevelopment of an artificial intelligence model for the detection of attention stateses_ES
dc.typeTesis de doctoradoes_ES
dc.creator.tidORCIDes_ES
dc.contributor.tidORCIDes_ES
dc.creator.identificador0009-0007-3565-6914es_ES
dc.contributor.identificador0000-0002-5455-0329es_ES
dc.contributor.roleDirector de tesises_ES
dc.degree.nameMaestría en Ciencias en Inteligencia Artificiales_ES
dc.degree.departmentFacultad de Ingenieríaes_ES
dc.degree.levelDoctoradoes_ES
dc.format.supportrecurso en líneaes_ES
dc.matricula.creator326992es_ES
dc.folioIGMAC-326992es_ES
Aparece en: Maestría en Ciencias en Inteligencia Artificial

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