884 resultados para T-Kernel
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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No Brasil os acidentes por picada de escorpião são considerados um problema para saúde pública, uma vez que este agravo pode levar ao óbito caso não haja um tratamento adequado. Assim, no município de Presidente Prudente/SP o órgão responsável pelo combate aos escorpiões é o Centro de Controle de Zoonoses (CCZ), que tem por finalidade notificar a aparição de escorpiões no município, promovendo portanto, as devidas informações à população com finalidade de evitar acidentes. Desse modo, com o uso dos Sistemas de Informações Geográficas (SIGs) aplicados à técnicas de estatística espacial, foi possível elaborar mapas de distribuição das notificações de escorpiões referente aos anos de 2012 e 2013, por meio dos métodos da geocodificação de endereços; estimador de intensidade de Kernel; índice de Moran função (LISA); quantidade de notificações por setor censitário e interpolação por vizinho mais próximo. Como resultado desta análise, o trabalho fornece informações ao CCZ sobre a distribuição de tal fenômeno para que fosse possível um controle direcionado os locais mais infestados, economizando tempo e recursos financeiros, visando o bem estar da população
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Pós-graduação em Biometria - IBB
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The nutrition of the orchards is the major factor of productivity, being necessary to know the proper doses of fertilizers and their influence on fruit quality attributes for industrialization. This study evaluated the effects of different doses of nitrogen and potassium on the productivity of guava trees and also on the values of pH, soluble solids (SS), titratable acidity (TA) and pulp/kernel ratio of guavas. The experiment was conducted at Vista Alegre do Alto, SP in an irrigated 'Paluma'guava orchard, 7 years old, managed with pruning during three consecutive cycles of production. The soil of the area was dystrophic Ultisol. The experimental design was the randomized blocks, in factorial, with four nitrogen doses (0, 0.5, 1 and 2 kg of N plant(-1)) and four of potassium (0, 0.55, 1.1 and 2.2 kg of K2O plant(-1)), with three replications. Nitrogen fertilization increased productivity and the pH of the fruit, being explained by the quadratic polynomial regression models; reduced linearly the pulp/kernel ratio and do not influenced the SS and TA values. On the other hand, potassium fertilization and N x K interaction had no significant effects on productivity and the other characteristics evaluated.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.
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Objective: to identify patterns in the spatial and temporal distribution of cases of dengue fever occurring in the city of Cruzeiro, state of Sao Paulo (SP).Methods: an ecological and exploratory study was undertaken using spatial analysis tools and data from dengue cases obtained on the SinanNet. The analysis was carried out by area, using the IBGE census sector as a unit. The months of March to June 2006 and 2011 were assessed, revealing progress of the disease. TerraView 3.3.1 was used to calculate the Global Moran's I, month to month, and the Kernel estimator.Results: in the year 2006, 691 cases of dengue fever (rate of 864.2 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (TM = 0.28; p = 0.01; I-M = 0.20; p = 0.01) with higher densities in the central, north, northeast and south regions. In the year 2011, 654 cases of dengue fever (rate of 886.8 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (I, = 0.28; p = 0.01; I-M = 0.16; p = 0.05) with densities in the same regions as 2006. The Global Moran's I is a global measure of spatial autocorrelation, which indicates the degree of spatial association in the set of information from the product in relation to the average. The I varies between -1 and +1 and can be attributed to a level of significance (p-value). The positive value points to a positive or direct spatial autocorrelation.Conclusion: we were able to identify patterns in the spatial and temporal distribution of dengue cases occurring in the city of Cruzeiro, SP, and locate the census sectors where the outbreak began and how it evolved.