An Artificial Intelligence Approach to Modeling in Social Science.

Autores
Vázquez , Juan Carlos; Castillo, Julio; Constable, Leticia; Cárdenas, Marina; Vázquez, Juan Carlos Guillermo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.
Fil: Vázquez, Juan Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Castillo, Julio. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Constable, Leticia. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Cárdenas, Marina. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Vázquez, Juan Carlos Guillermo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fuente
Journal of Health and Environmental Research, 2021; 7 (1).
Materia
Artificial Neural Network
Family Clinic History
Risk
Health
Housing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Attribution-NonCommercial-NoDerivatives 4.0 International
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/13822
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling An Artificial Intelligence Approach to Modeling in Social Science.Vázquez , Juan CarlosCastillo, JulioConstable, LeticiaCárdenas, MarinaVázquez, Juan Carlos GuillermoArtificial Neural NetworkFamily Clinic HistoryRiskHealthHousingComputer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.Fil: Vázquez, Juan Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.Fil: Castillo, Julio. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.Fil: Constable, Leticia. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.Fil: Cárdenas, Marina. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.Fil: Vázquez, Juan Carlos Guillermo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.Journal of Health and Environmental Research.2025-09-24T19:29:08Z2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfJournal of Health and Environmental Research 2021; 7 (1): 58-68.https://www.sciencepublishinggroup.com/j/jherhttps://hdl.handle.net/20.500.12272/13822Journal of Health and Environmental Research, 2021; 7 (1).reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacionalenginfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Vázquez, Juan Carlos; Constable, Leticia; Castillo, Julio; Cárdenas, Marina; Vázquez, Juan Carlos Guillermo.https://creativecommons.org/licenses/by-nc-nd/4.0/2026-09-24T12:44:19Zoai:ria.utn.edu.ar:20.500.12272/13822instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:44:20.314Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv An Artificial Intelligence Approach to Modeling in Social Science.
title An Artificial Intelligence Approach to Modeling in Social Science.
spellingShingle An Artificial Intelligence Approach to Modeling in Social Science.
Vázquez , Juan Carlos
Artificial Neural Network
Family Clinic History
Risk
Health
Housing
title_short An Artificial Intelligence Approach to Modeling in Social Science.
title_full An Artificial Intelligence Approach to Modeling in Social Science.
title_fullStr An Artificial Intelligence Approach to Modeling in Social Science.
title_full_unstemmed An Artificial Intelligence Approach to Modeling in Social Science.
title_sort An Artificial Intelligence Approach to Modeling in Social Science.
dc.creator.none.fl_str_mv Vázquez , Juan Carlos
Castillo, Julio
Constable, Leticia
Cárdenas, Marina
Vázquez, Juan Carlos Guillermo
author Vázquez , Juan Carlos
author_facet Vázquez , Juan Carlos
Castillo, Julio
Constable, Leticia
Cárdenas, Marina
Vázquez, Juan Carlos Guillermo
author_role author
author2 Castillo, Julio
Constable, Leticia
Cárdenas, Marina
Vázquez, Juan Carlos Guillermo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Artificial Neural Network
Family Clinic History
Risk
Health
Housing
topic Artificial Neural Network
Family Clinic History
Risk
Health
Housing
dc.description.none.fl_txt_mv Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.
Fil: Vázquez, Juan Carlos. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Castillo, Julio. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Constable, Leticia. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Cárdenas, Marina. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
Fil: Vázquez, Juan Carlos Guillermo. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación, Desarrollo y Transferencia de Aprendizaje Automático; Argentina.
description Computer Science has contributed to social sciences since decades ago: connecting people that build virtual communities where the interactions can be investigated, developing tools for statistically analytics, designing models that allow the analysis and simulation of the most diverse types, among many others. In this article, we describe an artificial neural network to model a theoretical framework for risk, housing, and health problematic, called DRVS (Diagnostic methodology for risk determination of urban housing for health), which uses a holistic approach for community and environmental health. The methodology also exposes digital clinic history for families and communities, developed to support the acquisition of necessary data. This software has advantages for the transference and application of the DRVS in different locations since it constitutes an expert system for the determination of local social indexes and supports the quantitative validation process for the underlying social theory. On the other hand, as many artificial intelligence techniques, it has constraints: unlike explicit logic inferences, artificial neural networks work as «black boxes», not explaining how they got the result; they have a strong dependency of the representativeness of training data and introducing new knowledge that may improve their results and performance is difficult (new data, addition or remotion of determining factors for the underlying social model, weighting factors, etc.). This article also shows some techniques and ideas on how to deal with the identified constraints.
publishDate 2021
dc.date.none.fl_str_mv 2021
2025-09-24T19:29:08Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv Journal of Health and Environmental Research 2021; 7 (1): 58-68.
https://www.sciencepublishinggroup.com/j/jher
https://hdl.handle.net/20.500.12272/13822
identifier_str_mv Journal of Health and Environmental Research 2021; 7 (1): 58-68.
url https://www.sciencepublishinggroup.com/j/jher
https://hdl.handle.net/20.500.12272/13822
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Vázquez, Juan Carlos; Constable, Leticia; Castillo, Julio; Cárdenas, Marina; Vázquez, Juan Carlos Guillermo.
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Vázquez, Juan Carlos; Constable, Leticia; Castillo, Julio; Cárdenas, Marina; Vázquez, Juan Carlos Guillermo.
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv Journal of Health and Environmental Research.
publisher.none.fl_str_mv Journal of Health and Environmental Research.
dc.source.none.fl_str_mv Journal of Health and Environmental Research, 2021; 7 (1).
reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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