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	Provedor de dados Acta biol.Colomb. (1) REA (1) Autor Bruning,Jhosefe (1) DÍAZ,GLORIA (1) HERNÁNDEZ-TAMAMES,JUAN ANTONIO (1) MALPICA,NORBERTO (1) MOLINA,VICENTE (1) Peiter,Marcia X. (1) Pereira,Tonismar dos S. (1) ROMERO,EDUARDO (1) Robaina,Adroaldo D. (1) Torres,Rogerio R. (1) Mais... Palavra-chave Support vector machines (2) Artificial neural networks (1) DARTEL (1) Machine learning (1) Neurodegenerative disease (1) Pattern classification (1) SPM (1) Soil compaction (1) Structural MRI (1) VBM (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Ano 2018 (1) 2010 (1) País Brazil (1) Colombia (1) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última AUTOMATIC CLASSIFICATION OF STRUCTURAL MRI FOR DIAGNOSIS OF NEURODEGENERATIVE DISEASES Acta biol.Colomb. DÍAZ,GLORIA; ROMERO,EDUARDO; HERNÁNDEZ-TAMAMES,JUAN ANTONIO; MOLINA,VICENTE; MALPICA,NORBERTO. This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples. Tipo: Info:eu-repo/semantics/article Palavras-chave: Neurodegenerative disease; Structural MRI; Pattern classification; SPM; VBM; DARTEL; Support vector machines. Ano: 2010 URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-548X2010000300012 THE USE OF ARTIFICIAL INTELLIGENCE FOR ESTIMATING SOIL RESISTANCE TO PENETRATION REA Pereira,Tonismar dos S.; Robaina,Adroaldo D.; Peiter,Marcia X.; Torres,Rogerio R.; Bruning,Jhosefe. ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were evaluated. The models generated from the ANNs were implemented through the multilayer perceptron with backpropagation algorithm of Matlab software, varying the number of neurons in the input and intermediate layers. For the procedure from SVM, the RapidMiner software was used, varying input variables, the kernel function and the coefficients of these... Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil compaction; Machine learning; Support vector machines; Artificial neural networks. Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100142 Registros recuperados: 2 Primeira ... 1 ... Última

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