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	Provedor de dados ArchiMer (2) Scientia Agricola (1) Electron. J. Biotechnol. (1) Autor Augustin, Jean-marie (2) Alleoni,Luís Reynaldo Ferracciú (1) Araujo,Eloá Moura (1) Barbosa,Rommel Melgaço (1) Boim,Alexys Giorgia Friol (1) Boucher, J.m. (1) Boucher, Jean-marc (1) Fablet, R (1) Fablet, Ronan (1) Karoui, I (1) Karoui, Imen (1) Maione,Camila (1) Santos-Araujo,Sabrina Novaes dos (1) Siqueira,Frank (1) Tonini,Gustavo (1) Mais... Palavra-chave Feature selection (4) Active regions (2) Angular backscattering (2) Level sets (2) Segmentation (2) Sonar images (2) Texture (2) Genetics (1) Heavy metals (1) ICP–OES (1) MMP (1) Parallel computing (1) Pattern detection (1) Performance (1) Traceability (1) Tropical soils (1) Mais... Tipo do documento Text (2) Info:eu-repo/semantics/article (1) Journal article (1) Ano 2022 (1) 2013 (1) 2009 (1) 2008 (1) País France (2) Brazil (1) Chile (1) Idioma Inglês (4)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 4 Primeira ... 1 ... Última Determining the geographical origin of lettuce with data mining applied to micronutrients and soil properties Scientia Agricola Maione,Camila; Araujo,Eloá Moura; Santos-Araujo,Sabrina Novaes dos; Boim,Alexys Giorgia Friol; Barbosa,Rommel Melgaço; Alleoni,Luís Reynaldo Ferracciú. ABSTRACT Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the... Tipo: Info:eu-repo/semantics/article Palavras-chave: ICP–OES; Traceability; Tropical soils; Heavy metals; Feature selection. Ano: 2022 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000100901 Evaluation of computer methods for biomarker discovery on computational grids Electron. J. Biotechnol. Tonini,Gustavo; Siqueira,Frank. Background: Discovering biomarkers is a fundamental step to understand and deal with genetic diseases. Methods using classic Computer Science algorithms have been adapted in order to support processing large biological data sets, aiming to find useful information to understand causing conditions of diseases such as cancer. Results: This paper describes some promising biomarker discovery methods based on several grid architectures. Each technique has some features that make it more suitable for a particular grid architecture. This matching depends on the parallelizing capabilities of the method and the resource availability in each processing/storage node. Conclusion: The study described in this paper analyzed the performance of biomarker discovery methods... Tipo: Journal article Palavras-chave: Feature selection; Genetics; Parallel computing; Pattern detection; Performance. Ano: 2013 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582013000500013 Region based variational approach for the segmentation textured sonar images ArchiMer Karoui, Imen; Fablet, Ronan; Boucher, Jean-marc; Augustin, Jean-marie. We propose a new region-based segmentation of textured sonar images with respect to seafloor types. We characterize sea-floor types by a set of empirical distributions estimated on texture responses to a set of different filters and we introduce a novel similarity measure between sonar textures in this attribute space. Our similarity measure is defined as a weighted sum of Kullback-Leibler divergences between texture features. The texture similarity measure weight setting is twofold: first we weight each filter, according to its discrimination power, the computation of these weights are issued from the margin maximization criterion, Second, we add an additional weighting, evaluated as an angular distance between the incidence angles of the compared texture... Tipo: Text Palavras-chave: Level sets; Active regions; Segmentation; Angular backscattering; Feature selection; Sonar images; Texture. Ano: 2008 URL: http://archimer.ifremer.fr/doc/2008/publication-6120.pdf Seabed segmentation using optimized statistics of sonar textures ArchiMer Karoui, I; Fablet, R; Boucher, J.m.; Augustin, Jean-marie. We propose and compare two supervised algorithms of the segmentation of textured sonar images with respect to seafloor types. We characterize sea-floors by a set of empirical distributions estimated on texture responses for a wide set of different filters with various parameterizations and we introduce a novel similarity measure between sonar textures in this feature space. Our similarity measure is defined as a weighted sum of Kullback-Leibler divergences between texture features. The weight setting is twofold. First each filter is weighted according to its discrimination power: the computation of these weights are issued from a margin maximization criterion. Second, an additional weight, evaluated as an angular distance between the incidence angles of... Tipo: Text Palavras-chave: Level sets; Active regions; MMP; Segmentation; Angular backscattering; Feature selection; Sonar images; Texture. Ano: 2009 URL: http://archimer.ifremer.fr/doc/2009/publication-6101.pdf Registros recuperados: 4 Primeira ... 1 ... Última

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