Communications in computer and information science, 2016
This research presents a method to describe Learning Objects as Semantic Web compatible Ontologie... more This research presents a method to describe Learning Objects as Semantic Web compatible Ontologies. The proposed method divides the Ontologies among three layers. The first is composed by the knowledge domain, the second by the LOs and their relations, and the third is responsible for knowledge inference and reasoning. As study case, it is presented the Ontologies of LOM and OBAA metadata standards as part of the Layer One. The layer two is composed by the description of sample Learning Objects based on the properties and restrictions defines by the layer one ontologies. The layer three describes the knowledge inference axioms, which we defined as Application Profiles. Our current results can be resumed as a contribution to Ontology Engineering for Semantic Web applied to Digital Education.
Background: Low back pain (LBP) is the leading cause of years lived with disability worldwide. Pu... more Background: Low back pain (LBP) is the leading cause of years lived with disability worldwide. Public safety workers are highly exposed to physically demanding activities and inappropriate postures, which can increase the LBP experience in this population. Smartphone app-based self-managed interventions may be an alternative for chronic non-specific LBP (CNSLBP) treatment. This study aims to develop and test the effectiveness of an 8-week smartphone app-based self-managed exercise program plus health education, compared to a health education program alone, on neuromuscular and perceptual outcomes in police officers and firefighters with CNSLBP. Methods: This is a parallel, two-armed, blinded evaluator randomized clinical trial. Police officers and firefighters (from public safety institutions in the Rio Grande do Sul state, Brazil) will be randomly assigned to m-health self-managed exercise program (twice a week) plus health education or health education alone. Self-management exercise program components are mobility and core resistance exercises, available on the app. Follow-ups will be conducted post-treatment (8 weeks) and 16 weeks after randomization. The primary outcomes will be pain intensity and disability post-treatment (8 weeks). Secondary outcomes will be biopsychosocial factors related to CNSLBP. Discussion: Our hypothesis is of superiority for the effects of a smartphone app-based self-managed exercise program on primary and secondary outcomes, compared to the effects of only the health education intervention in police officers and firefighters with CNSLBP. Trial registration: The study was prospectively registered at ClinicalTrials.gov (NCT05481996. Registered on August 01, 2022).
Mining Chat Discussions (9781605660103): Stanley Loh Daniel Licthnow, Thyago Borges Tiago Primo: ... more Mining Chat Discussions (9781605660103): Stanley Loh Daniel Licthnow, Thyago Borges Tiago Primo: Book Chapters.
This research presents a method to describe Learning Objects as Semantic Web compatible Ontologie... more This research presents a method to describe Learning Objects as Semantic Web compatible Ontologies. The proposed method divides the Ontologies among three layers. The first is composed of the knowledge domain, the second by the Learning Objects (LOs) and their relations, and the third is responsible for knowledge inference and reasoning. As study case, we present the Ontologies of Learning Object Metadata (LOM) and Brazilian Metadata for Learning Objects (OBAA) metadata standards as part of the Layer One. The Layer Two composed by the description of sample Learning Objects based on the properties and restrictions defined by the Layer One ontologies. Layer Three describes the knowledge inference axioms, which we defined as Application Profiles. Our current results can summarize a contribution to Ontology Engineering for Semantic Web applied to Digital Education.
Este trabalho apresenta a implementacao de uma ferramenta de busca de tecnologias educacionais, p... more Este trabalho apresenta a implementacao de uma ferramenta de busca de tecnologias educacionais, pre-qualificadas pelo Ministerio da Educacao (MEC). O processo de pre-qualificacao de tecnologias educacionais, no qual se baseia este trabalho, tem como objetivo a producao de um documento chamado Guia de Tecnologias Educacionais, composto por tecnologias aprovadas para uso em escolas publicas brasileiras. A interpretacao computacional do guia, operacionalizando-o atraves de uma ferramenta de busca web, permite recuperar tecnologias conforme consultas especificas dos usuarios e este e o objetivo geral do presente trabalho. Alem disto, propoe-se com esta ferramenta que a disponibilizacao das tecnologias pre-qualificadas seja realizada de forma mais rapida, uma vez que logo apos o processo avaliativo, os dados sobre as tecnologias ja estarao disponiveis para consultas, sem que seja necessario esperar que o processo de editoracao e diagramacao do documento com as tecnologias aprovadas seja ...
The COVID-19 pandemic has been pressuring the whole society and overloading hospital systems. Mac... more The COVID-19 pandemic has been pressuring the whole society and overloading hospital systems. Machine learning models designed to predict hospitalizations, for example, can contribute to better targeting hospital resources. However, as the excess of information, often irrelevant or redundant, can impair the performance of predictive models, we propose in this work a hybrid approach to attribute selection. This method aims to find an optimal attribute subset through a genetic algorithm, which considers the results of a classification model in its evaluation function to improve the hospitalization need prediction of COVID-19 patients. We evaluated this approach in a database of more than 200 thousand COVID-19 patients from the State Health Secretariat of Rio Grande do Sul. We provided an increase of 18% in the classification precision for patients with hospitalization necessities. In a real-time application, this would also mean greater precision in targeting resources, as well as, consequently and mainly, improved service to the infected population.
Educational Management Systems store a large amount of data from interaction of not only students... more Educational Management Systems store a large amount of data from interaction of not only students and professors but also of students and the educational environment. Analyze and find patterns manually from a huge amount of data is hard, so Educational Data Mining (EDM) is widely used. This work presents a model that can predict the student’s risk of dropout using data from the first three semesters attended by Computer Science Undergraduate students (N=1516) from Federal University of Pelotas. This work uses the CRISP-DM methodology e data from Cobalto Management System. The results are shown for three algorithms and for the RandomForest algorithm a precision of 95.12% and a Recall of 91.41% is presented indicating that it is possible to use a prediction model using only the data from the first three semesters of the course.
Communications in computer and information science, 2016
This research presents a method to describe Learning Objects as Semantic Web compatible Ontologie... more This research presents a method to describe Learning Objects as Semantic Web compatible Ontologies. The proposed method divides the Ontologies among three layers. The first is composed by the knowledge domain, the second by the LOs and their relations, and the third is responsible for knowledge inference and reasoning. As study case, it is presented the Ontologies of LOM and OBAA metadata standards as part of the Layer One. The layer two is composed by the description of sample Learning Objects based on the properties and restrictions defines by the layer one ontologies. The layer three describes the knowledge inference axioms, which we defined as Application Profiles. Our current results can be resumed as a contribution to Ontology Engineering for Semantic Web applied to Digital Education.
Background: Low back pain (LBP) is the leading cause of years lived with disability worldwide. Pu... more Background: Low back pain (LBP) is the leading cause of years lived with disability worldwide. Public safety workers are highly exposed to physically demanding activities and inappropriate postures, which can increase the LBP experience in this population. Smartphone app-based self-managed interventions may be an alternative for chronic non-specific LBP (CNSLBP) treatment. This study aims to develop and test the effectiveness of an 8-week smartphone app-based self-managed exercise program plus health education, compared to a health education program alone, on neuromuscular and perceptual outcomes in police officers and firefighters with CNSLBP. Methods: This is a parallel, two-armed, blinded evaluator randomized clinical trial. Police officers and firefighters (from public safety institutions in the Rio Grande do Sul state, Brazil) will be randomly assigned to m-health self-managed exercise program (twice a week) plus health education or health education alone. Self-management exercise program components are mobility and core resistance exercises, available on the app. Follow-ups will be conducted post-treatment (8 weeks) and 16 weeks after randomization. The primary outcomes will be pain intensity and disability post-treatment (8 weeks). Secondary outcomes will be biopsychosocial factors related to CNSLBP. Discussion: Our hypothesis is of superiority for the effects of a smartphone app-based self-managed exercise program on primary and secondary outcomes, compared to the effects of only the health education intervention in police officers and firefighters with CNSLBP. Trial registration: The study was prospectively registered at ClinicalTrials.gov (NCT05481996. Registered on August 01, 2022).
Mining Chat Discussions (9781605660103): Stanley Loh Daniel Licthnow, Thyago Borges Tiago Primo: ... more Mining Chat Discussions (9781605660103): Stanley Loh Daniel Licthnow, Thyago Borges Tiago Primo: Book Chapters.
This research presents a method to describe Learning Objects as Semantic Web compatible Ontologie... more This research presents a method to describe Learning Objects as Semantic Web compatible Ontologies. The proposed method divides the Ontologies among three layers. The first is composed of the knowledge domain, the second by the Learning Objects (LOs) and their relations, and the third is responsible for knowledge inference and reasoning. As study case, we present the Ontologies of Learning Object Metadata (LOM) and Brazilian Metadata for Learning Objects (OBAA) metadata standards as part of the Layer One. The Layer Two composed by the description of sample Learning Objects based on the properties and restrictions defined by the Layer One ontologies. Layer Three describes the knowledge inference axioms, which we defined as Application Profiles. Our current results can summarize a contribution to Ontology Engineering for Semantic Web applied to Digital Education.
Este trabalho apresenta a implementacao de uma ferramenta de busca de tecnologias educacionais, p... more Este trabalho apresenta a implementacao de uma ferramenta de busca de tecnologias educacionais, pre-qualificadas pelo Ministerio da Educacao (MEC). O processo de pre-qualificacao de tecnologias educacionais, no qual se baseia este trabalho, tem como objetivo a producao de um documento chamado Guia de Tecnologias Educacionais, composto por tecnologias aprovadas para uso em escolas publicas brasileiras. A interpretacao computacional do guia, operacionalizando-o atraves de uma ferramenta de busca web, permite recuperar tecnologias conforme consultas especificas dos usuarios e este e o objetivo geral do presente trabalho. Alem disto, propoe-se com esta ferramenta que a disponibilizacao das tecnologias pre-qualificadas seja realizada de forma mais rapida, uma vez que logo apos o processo avaliativo, os dados sobre as tecnologias ja estarao disponiveis para consultas, sem que seja necessario esperar que o processo de editoracao e diagramacao do documento com as tecnologias aprovadas seja ...
The COVID-19 pandemic has been pressuring the whole society and overloading hospital systems. Mac... more The COVID-19 pandemic has been pressuring the whole society and overloading hospital systems. Machine learning models designed to predict hospitalizations, for example, can contribute to better targeting hospital resources. However, as the excess of information, often irrelevant or redundant, can impair the performance of predictive models, we propose in this work a hybrid approach to attribute selection. This method aims to find an optimal attribute subset through a genetic algorithm, which considers the results of a classification model in its evaluation function to improve the hospitalization need prediction of COVID-19 patients. We evaluated this approach in a database of more than 200 thousand COVID-19 patients from the State Health Secretariat of Rio Grande do Sul. We provided an increase of 18% in the classification precision for patients with hospitalization necessities. In a real-time application, this would also mean greater precision in targeting resources, as well as, consequently and mainly, improved service to the infected population.
Educational Management Systems store a large amount of data from interaction of not only students... more Educational Management Systems store a large amount of data from interaction of not only students and professors but also of students and the educational environment. Analyze and find patterns manually from a huge amount of data is hard, so Educational Data Mining (EDM) is widely used. This work presents a model that can predict the student’s risk of dropout using data from the first three semesters attended by Computer Science Undergraduate students (N=1516) from Federal University of Pelotas. This work uses the CRISP-DM methodology e data from Cobalto Management System. The results are shown for three algorithms and for the RandomForest algorithm a precision of 95.12% and a Recall of 91.41% is presented indicating that it is possible to use a prediction model using only the data from the first three semesters of the course.
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