Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia
figuras, tablas
- Autores:
-
Mosquera Navarro, Rodolfo
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/79556
- Palabra clave:
- 370 - Educación
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
artificial intelligence
personal docente - aspectos sociales
personal docente - aspectos psicológicos
calidad de vida en el trabajo
rendimiento laboral
Red Neuronal artificial de retropropagación
tensión superficial física
riesgo psicosocial
docentes de colegios públicos
Red Neuronal de Tensión Superficial
Colombia
Artificial Neural Network backpropagation
physical surface tension
Classification
Prediction
psychosocial risk
State-school teachers
Physical surface tension-Neural Net
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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UNACIONAL2 |
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Universidad Nacional de Colombia |
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|
| dc.title.spa.fl_str_mv |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia |
| dc.title.translated.eng.fl_str_mv |
Classification system to the predicting of psychosocial risk level on state-school teachers in Colombia based on Artificial Intelligence |
| title |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia |
| spellingShingle |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia 370 - Educación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación artificial intelligence personal docente - aspectos sociales personal docente - aspectos psicológicos calidad de vida en el trabajo rendimiento laboral Red Neuronal artificial de retropropagación tensión superficial física riesgo psicosocial docentes de colegios públicos Red Neuronal de Tensión Superficial Colombia Artificial Neural Network backpropagation physical surface tension Classification Prediction psychosocial risk State-school teachers Physical surface tension-Neural Net |
| title_short |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia |
| title_full |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia |
| title_fullStr |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia |
| title_full_unstemmed |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia |
| title_sort |
Sistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de Colombia |
| dc.creator.fl_str_mv |
Mosquera Navarro, Rodolfo |
| dc.contributor.advisor.none.fl_str_mv |
Castrillón Gómez, Omar Danilo Parra Osorio, Liliana |
| dc.contributor.author.none.fl_str_mv |
Mosquera Navarro, Rodolfo |
| dc.subject.ddc.spa.fl_str_mv |
370 - Educación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación |
| topic |
370 - Educación 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación artificial intelligence personal docente - aspectos sociales personal docente - aspectos psicológicos calidad de vida en el trabajo rendimiento laboral Red Neuronal artificial de retropropagación tensión superficial física riesgo psicosocial docentes de colegios públicos Red Neuronal de Tensión Superficial Colombia Artificial Neural Network backpropagation physical surface tension Classification Prediction psychosocial risk State-school teachers Physical surface tension-Neural Net |
| dc.subject.lcsh.none.fl_str_mv |
artificial intelligence |
| dc.subject.lemb.none.fl_str_mv |
personal docente - aspectos sociales personal docente - aspectos psicológicos calidad de vida en el trabajo rendimiento laboral |
| dc.subject.proposal.spa.fl_str_mv |
Red Neuronal artificial de retropropagación tensión superficial física riesgo psicosocial docentes de colegios públicos Red Neuronal de Tensión Superficial Colombia |
| dc.subject.proposal.eng.fl_str_mv |
Artificial Neural Network backpropagation physical surface tension Classification Prediction psychosocial risk State-school teachers Physical surface tension-Neural Net |
| description |
figuras, tablas |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-05-25T17:04:51Z |
| dc.date.available.none.fl_str_mv |
2021-05-25T17:04:51Z |
| dc.date.issued.none.fl_str_mv |
2021-02-26 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| format |
http://purl.org/coar/resource_type/c_db06 |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/79556 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co |
| url |
https://repositorio.unal.edu.co/handle/unal/79556 https://repositorio.unal.edu.co |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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Castrillón Gómez, Omar Danilodf8b2e171ca5874bdef91d9df638b01dParra Osorio, Liliana0935319fea286159746e61043ef08292Mosquera Navarro, Rodolfob847ca85d47eec6d5bf33210afd60b8c2021-05-25T17:04:51Z2021-05-25T17:04:51Z2021-02-26https://repositorio.unal.edu.co/handle/unal/79556Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.cofiguras, tablasEl objetivo del presente trabajo es el desarrollo de un algoritmo inteligente para mejorar la predicción del riesgo psicosocial entre los docentes de colegios públicos en Colombia. El enfoque está compuesto por el modelo de redes neuronales artificiales vinculado a la teoría física de la tensión superficial en los líquidos. Para lograr los objetivos de este estudio, los docentes de colegios públicos han completado la evaluación de la Batería para la evaluación de factores de riesgo psicosocial intralaboral para la identificación del nivel de riesgo. Las variables que componen los factores de riesgo psicosocial se utilizan como entradas y el nivel de riesgo se utiliza como salida en el algoritmo. La eficiencia de la red neuronal de tensión superficial física (RNA-TS) se examina contra los algoritmos árboles de decisión (algoritmo J48), Naïve Bayes (NBC), red neuronal artificial (ANN), máquina de vectores de soporte (SVM), máquinas de vectores de soporte con función de base radial (SVM-RBF), máquina de vector de soporte con escalada de colinas (HC-SVM), el algoritmo híbrido de búsqueda de cuco junto a máquinas vectoriales de soporte (CS-SVM) y el modelo híbrido de máquina de vector de soporte con k-vecino más cercano (k-NN-SVM), utilizando el nivel de sensibilidad, la especificidad, el nivel de exactitud, el porcentaje de error de clasificación y la curva ROC. Los resultados concluyeron que RNA-TS proporciona una mejor clasificación que los demás algoritmos. Por tanto, la RNA-TS se utiliza para predecir y clasificar el nivel de riesgo psicosocial de los docentes de colegios públicos en su actividad laboral. Uno de los temas importantes para aplicar la teoría de la tensión superficial en el entorno del mundo real es desarrollar un modelo que soporte el modelo de aprendizaje automático para reflejar toda la complejidad de los factores psicosociales del mundo en los entornos laborales y permitir su predicción. Los profesores que presentan un riesgo psicosocial muy alto y alto son identificados con un 97,37% de exactitud. Esto ayudará a los gerentes a prever si los trabajadores están satisfechos con su carga de trabajo en el contexto de la higiene y la seguridad laboral. Finalmente, este es el primer estudio que introduce un algoritmo de tensión superficial física adaptado como clasificador inteligente para la predicción eficiente de factores de riesgo psicosocial en docentes de colegios públicos en sistemas académicos.The main goal of this research is to develop an intelligent algorithm to improve the prediction of psychosocial risk among the state-school teachers in Colombia. The model is composed of artificial neural network backpropagation linked to the physical theory of surface tension in liquids. To achieve the goals of this study, state school teachers have carried out the evaluation of the battery for the evaluation of intra-occupational psychosocial risk factors to identify the risk level. The variables that make up the psychosocial risk factors are used as inputs and the risk level is used as output in the algorithm. The efficiency of the physical surface tension neural network (PST-NN) is examined against decision trees algorithm (algorithm J48), Naïve Bayes (NBC), artificial neural network (ANN), support vector machine (SVM), support vector machines with radial basis function (SVM-RBF), hill climbing random restart - support vector machine hybrid model (HCRR-SVM), cuckoo search - support vector machines hybrid algorithm (CS-SVM) and the k-nearest neighbor – support vector machine hybrid model (k-NN-SVM), for metric evaluation were used, the sensitivity level, specificity, accuracy level, percentage of classification error, and ROC curve. The results concluded that PST-NN provides a better classification than the other algorithms. Therefore, the PST-NN is used to predict and classify the level of psychosocial risk of state-school teachers in their work activity. One of the important topics for applying the theory of surface tension in the real-world environment is to develop a model that supports the machine learning model to reflect all the complexity of the psychosocial factors of the world in work environments and allow its prediction. Teachers who present a very high and high psychosocial risk level were identified with 97.37% accuracy. This will help managers to predict whether workers are satisfied with their workload in the context of occupational hygiene and safety. Finally, this is the first study that introduces a physical surface tension algorithm adapted as an intelligent classifier for the efficient prediction of psychosocial risk factors in state-school teachers in academic systems.DoctoradoDoctor en IngenieríaOrganizaciones, sistemas y gestión de la tecnología, la Información, el conocimiento y la innovación tecnológica205 p.application/pdfspaUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Industria y OrganizacionesDepartamento de Ingeniería IndustrialFacultad de Ingeniería y ArquitecturaManizalesUniversidad Nacional de Colombia - Sede Manizaleshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2370 - Educación620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónartificial intelligencepersonal docente - aspectos socialespersonal docente - aspectos psicológicoscalidad de vida en el trabajorendimiento laboralRed Neuronal artificial de retropropagacióntensión superficial físicariesgo psicosocialdocentes de colegios públicosRed Neuronal de Tensión SuperficialColombiaArtificial Neural Network backpropagationphysical surface tensionClassificationPredictionpsychosocial riskState-school teachersPhysical surface tension-Neural NetSistema de clasificación basado en técnicas inteligentes para identificar el grado de riesgo psicosocial en docentes de educación básica primaria y secundaria en colegios públicos de ColombiaClassification system to the predicting of psychosocial risk level on state-school teachers in Colombia based on Artificial IntelligenceTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06TextColombiaAbma, F. 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Biomedical Signal Processing and Control, 57, 101736. https://doi.org/10.1016/j.bspc.2019.101736“Convocatoria Nacional para el Apoyo al Desarrollo de Tesis de Posgrado o de Trabajos Finales de Especialidades en el área de la Salud de la Universidad Nacional de Colombia 2017-2018” Resolución 21 de 2017. Oficina de la Vice-rectoría de Investigación (21 de diciembre de 2017), proyecto seleccionado, financiado y ejecutado con recursos de la Universidad Nacional de Colombia con el número de identificación 40976 en el Sistema de Información Hermes. Anexo A.MinCiencias programa de créditos condonables, convocatoria 647 para adelantar estudios doctorales a nivel nacional.Universidad Nacional de Colombia - Vicerrectoría de InvestigaciónMinCienciasCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.unal.edu.co/bitstream/unal/79556/3/license_rdf4460e5956bc1d1639be9ae6146a50347MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79556/4/license.txtcccfe52f796b7c63423298c2d3365fc6MD54ORIGINAL94527819.2021.pdf94527819.2021.pdfTesis de Doctorado en Ingeniería - Industria y Organizacionesapplication/pdf4048785https://repositorio.unal.edu.co/bitstream/unal/79556/5/94527819.2021.pdfd43459971e59828684b4424b22976515MD55THUMBNAIL94527819.2021.pdf.jpg94527819.2021.pdf.jpgGenerated 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