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Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger

Intimate Partner Violence (IPV) has become a problem of a great magnitude as it is a leading cause of death for women according to the World Health Organization. This paper presents a novel Early Warning System (EWS) for locating and protecting potential victims of IPV in which the victims' env...

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Detalles Bibliográficos
Autor principal: Cabello, Julia García
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002844/
https://www.ncbi.nlm.nih.gov/pubmed/32051859
http://dx.doi.org/10.1016/j.heliyon.2020.e03211
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author Cabello, Julia García
author_facet Cabello, Julia García
author_sort Cabello, Julia García
collection PubMed
description Intimate Partner Violence (IPV) has become a problem of a great magnitude as it is a leading cause of death for women according to the World Health Organization. This paper presents a novel Early Warning System (EWS) for locating and protecting potential victims of IPV in which the victims' environment plays a central role. Specifically, based on Markov random fields, our EWS measures the danger through the perception that the victims' surroundings have. This new standpoint is of particular relevance since most of the IPV victims do not have a clear perception of the approaching danger. The EWS has been designed without geographical constrains to be appropriate for use in every country of the world (universal EWS). Importantly, it may be rewritten into computational terms thereby providing a real tool for police investigations. Connections with text mining and sentiment analysis may provide additional devices based on this model.
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spelling pubmed-70028442020-02-12 Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger Cabello, Julia García Heliyon Article Intimate Partner Violence (IPV) has become a problem of a great magnitude as it is a leading cause of death for women according to the World Health Organization. This paper presents a novel Early Warning System (EWS) for locating and protecting potential victims of IPV in which the victims' environment plays a central role. Specifically, based on Markov random fields, our EWS measures the danger through the perception that the victims' surroundings have. This new standpoint is of particular relevance since most of the IPV victims do not have a clear perception of the approaching danger. The EWS has been designed without geographical constrains to be appropriate for use in every country of the world (universal EWS). Importantly, it may be rewritten into computational terms thereby providing a real tool for police investigations. Connections with text mining and sentiment analysis may provide additional devices based on this model. Elsevier 2020-01-30 /pmc/articles/PMC7002844/ /pubmed/32051859 http://dx.doi.org/10.1016/j.heliyon.2020.e03211 Text en © 2020 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Cabello, Julia García
Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger
title Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger
title_full Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger
title_fullStr Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger
title_full_unstemmed Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger
title_short Intimate partner violence: A novel warning system in which the victims' environment alerts to the danger
title_sort intimate partner violence: a novel warning system in which the victims' environment alerts to the danger
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002844/
https://www.ncbi.nlm.nih.gov/pubmed/32051859
http://dx.doi.org/10.1016/j.heliyon.2020.e03211
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