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dc.rights.license https://creativecommons.org/licenses/by-nc-nd/4.0 es_ES
dc.contributor Marco Antonio Aceves Fernández es_ES
dc.creator Juan Salvador Toledo Rios es_ES
dc.date.accessioned 2025-07-02T18:31:12Z
dc.date.available 2025-07-02T18:31:12Z
dc.date.issued 2026-07-24
dc.identifier.uri https://ri-ng.uaq.mx/handle/123456789/11895
dc.description Cognitive 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.format pdf es_ES
dc.format.extent 1 recurso en línea (84 páginas) es_ES
dc.format.medium computadora es_ES
dc.language.iso spa es_ES
dc.publisher Universidad Autonoma de Querétaro es_ES
dc.relation.requires Si es_ES
dc.rights embargoedAccess es_ES
dc.subject Artificial Intelligence es_ES
dc.subject Neural Networks es_ES
dc.subject Machine Learning es_ES
dc.subject Classification es_ES
dc.subject Attention es_ES
dc.subject Data Augmentation es_ES
dc.subject.classification INGENIERÍA Y TECNOLOGÍA es_ES
dc.title Development of an artificial intelligence model for the detection of attention states es_ES
dc.type Tesis de doctorado es_ES
dc.creator.tid ORCID es_ES
dc.contributor.tid ORCID es_ES
dc.creator.identificador 0009-0007-3565-6914 es_ES
dc.contributor.identificador 0000-0002-5455-0329 es_ES
dc.contributor.role Director de tesis es_ES
dc.degree.name Maestría en Ciencias en Inteligencia Artificial es_ES
dc.degree.department Facultad de Ingeniería es_ES
dc.degree.level Doctorado es_ES
dc.format.support recurso en línea es_ES
dc.matricula.creator 326992 es_ES
dc.folio IGMAC-326992 es_ES


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