Examinando por Materia "factual database"

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    Emotion Recognition from EEG and Facial Expressions: A Multimodal Approach
    (Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Chaparro V.; Gomez A.; Salgado A.; Quintero O.L.; Lopez N.; Villa L.F.; Chaparro V.; Gomez A.; Salgado A.; Quintero O.L.; Lopez N.; Villa L.F.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    The understanding of a psychological phenomena such as emotion is of paramount importance for psychologists, since it allows to recognize a pathology and to prescribe a due treatment for a patient. While approaching this problem, mathematicians and computational science engineers have proposed different unimodal techniques for emotion recognition from voice, electroencephalography, facial expression, and physiological data. It is also well known that identifying emotions is a multimodal process. The main goal in this work is to train a computer to do so. In this paper we will present our first approach to a multimodal emotion recognition via data fusion of Electroencephalography and facial expressions. The selected strategy was a feature-level fusion of both Electroencephalography and facial microexpressions, and the classification schemes used were a neural network model and a random forest classifier. Experimental set up was out with the balanced multimodal database MAHNOB-HCI. Results are promising compared to results from other authors with a 97% of accuracy. The feature-level fusion approach used in this work improves our unimodal techniques up to 12% per emotion. Therefore, we may conclude that our simple but effective approach improves the overall results of accuracy. © 2018 IEEE.
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    mantisGRID: a grid platform for DICOM medical images management in Colombia and Latin America.
    (SPRINGER, 2011-04-01) Garcia Ruiz M; García, A.; Ruiz Ibañez C; Gutierrez Mazo JM; Ramirez Giraldo JC; Pelaez Echavarria A; Valencia Diaz E; Pelaez Restrepo G; Montoya Munera EN; Garcia Loaiza B; Gomez Gonzalez S; Garcia Ruiz M; García, A.; Ruiz Ibañez C; Gutierrez Mazo JM; Ramirez Giraldo JC; Pelaez Echavarria A; Valencia Diaz E; Pelaez Restrepo G; Montoya Munera EN; Garcia Loaiza B; Gomez Gonzalez S; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    This paper presents the mantisGRID project, an interinstitutional initiative from Colombian medical and academic centers aiming to provide medical grid services for Colombia and Latin America. The mantisGRID is a GRID platform, based on open source grid infrastructure that provides the necessary services to access and exchange medical images and associated information following digital imaging and communications in medicine (DICOM) and health level 7 standards. The paper focuses first on the data abstraction architecture, which is achieved via Open Grid Services Architecture Data Access and Integration (OGSA-DAI) services and supported by the Globus Toolkit. The grid currently uses a 30-Mb bandwidth of the Colombian High Technology Academic Network, RENATA, connected to Internet 2. It also includes a discussion on the relational database created to handle the DICOM objects that were represented using Extensible Markup Language Schema documents, as well as other features implemented such as data security, user authentication, and patient confidentiality. Grid performance was tested using the three current operative nodes and the results demonstrated comparable query times between the mantisGRID (OGSA-DAI) and Distributed mySQL databases, especially for a large number of records.
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    Ítem
    mantisGRID: a grid platform for DICOM medical images management in Colombia and Latin America.
    (SPRINGER, 2011-04-01) Garcia Ruiz M; García, A.; Ruiz Ibañez C; Gutierrez Mazo JM; Ramirez Giraldo JC; Pelaez Echavarria A; Valencia Diaz E; Pelaez Restrepo G; Montoya Munera EN; Garcia Loaiza B; Gomez Gonzalez S; Universidad EAFIT. Departamento de Ingeniería Mecánica; Bioingeniería GIB (CES – EAFIT)
    This paper presents the mantisGRID project, an interinstitutional initiative from Colombian medical and academic centers aiming to provide medical grid services for Colombia and Latin America. The mantisGRID is a GRID platform, based on open source grid infrastructure that provides the necessary services to access and exchange medical images and associated information following digital imaging and communications in medicine (DICOM) and health level 7 standards. The paper focuses first on the data abstraction architecture, which is achieved via Open Grid Services Architecture Data Access and Integration (OGSA-DAI) services and supported by the Globus Toolkit. The grid currently uses a 30-Mb bandwidth of the Colombian High Technology Academic Network, RENATA, connected to Internet 2. It also includes a discussion on the relational database created to handle the DICOM objects that were represented using Extensible Markup Language Schema documents, as well as other features implemented such as data security, user authentication, and patient confidentiality. Grid performance was tested using the three current operative nodes and the results demonstrated comparable query times between the mantisGRID (OGSA-DAI) and Distributed mySQL databases, especially for a large number of records.
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    Ítem
    Recognition and regionalization of emotions in the arousal-valence plane
    (Institute of Electrical and Electronics Engineers Inc., 2015-01-01) Bustamante, P.A.; Lopez Celani, N.M.; Perez, M.E.; Quintero Montoya, O.L.; Bustamante, P.A.; Lopez Celani, N.M.; Perez, M.E.; Quintero Montoya, O.L.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    The emotion recognition systems have become important for the diversity of its applications. Several methodologies have been proposed based on how emotions are reflected in biological systems, such as facial expressions, the activity of the nervous system or the prosody of voice. The detection of emotions by voice processing is an approach that involves a noninvasive procedure that produces results with an acceptable rate of detection. In this work an algorithm for features extraction was developed, that efficiently classify different emotional states. Thus, emotions that have not been trained can be associated with a trained emotion both belonging to the same region of the valence-arousal plane.