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A happiness degree predictor using the conceptual data structure for deep learning architectures

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A happiness degree predictor using the conceptual data structure for deep learning architectures

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Perez-Benito, FJ.; Villacampa-Fernandez, P.; Conejero, JA.; Garcia-Gomez, JM.; Navarro-Pardo, E. (2019). A happiness degree predictor using the conceptual data structure for deep learning architectures. Computer Methods and Programs in Biomedicine. 168:59-68. https://doi.org/10.1016/j.cmpb.2017.11.004

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/151042

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Title: A happiness degree predictor using the conceptual data structure for deep learning architectures
Author: Perez-Benito, Francisco Javier Villacampa-Fernandez, Patricia Conejero, J. Alberto Garcia-Gomez, Juan M NAVARRO-PARDO, ESPERANZA
UPV Unit: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada
Universitat Politècnica de València. Instituto Universitario de Matemática Pura y Aplicada - Institut Universitari de Matemàtica Pura i Aplicada
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Abstract:
[EN] Background and Objective: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the ...[+]
Subjects: Deep learning , Data-structure driven deep neural network (D-SDNN) , Happiness,Happiness-Degree Predictor (H-DP)
Copyrigths: Reserva de todos los derechos
Source:
Computer Methods and Programs in Biomedicine. (issn: 0169-2607 )
DOI: 10.1016/j.cmpb.2017.11.004
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.cmpb.2017.11.004
Type: Artículo

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