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Deep Learning Implementation for Pattern and Incidences Identification of Gender Violence in Mexican Contexts

Deep Learning Implementation for Pattern and Incidences Identification of Gender Violence in Mexican Contexts
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Author(s): Juan Carlos Garduño (Universidad Autónoma del Estado de México, Mexico), David Valle-Cruz (Universidad Autónoma del Estado de México, Mexico), Rafael Valentin Mendoza-Mendez (Universidad Autónoma del Estado de México, Mexico)and Ernesto Joel Dorantes-Coronado (Universidad Autónoma del Estado de México, Mexico)
Copyright: 2023
Pages: 27
Source title: Handbook of Research on Applied Artificial Intelligence and Robotics for Government Processes
Source Author(s)/Editor(s): David Valle-Cruz (Universidad Autónoma del Estado de México, Mexico), Nely Plata-Cesar (Universidad Autónoma del Estado de México, Mexico)and Jacobo Leonardo González-Ruíz (Universidad Autónoma del Estado de México, Mexico)
DOI: 10.4018/978-1-6684-5624-8.ch015

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Abstract

This research has as its central theme gender violence in the Mexican context. It is based on the application of two data collection instruments whose objective is, in the first instance, to identify factors and essential elements that characterize cases of gender violence in higher education institutions and as a second point to obtain information from students who are victims of gender violence. The implementation of deep learning techniques is a key point in the research because thanks to it, it has been possible to identify the percentage in which emotions are presented in the texts collected with the second data collection instrument. Among the data obtained, it stands out that the female gender is the most violent, but at the same time, it is considered the most violent, a fact that gives the opportunity to continue with this research in future works, or the fact that 91.9% of the women surveyed have suffered at least one case of violence in their lives. In addition to this, it was found that anger is the most influential emotion in the texts collected with a presence of 56.3%.

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