23 resultados para Decision tree


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Prostate cancer is a serious public health problem accounting for up to 30% of clinical tumors in men. The diagnosis of this disease is made with clinical, laboratorial and radiological exams, which may indicate the need for transrectal biopsy. Prostate biopsies are discerningly evaluated by pathologists in an attempt to determine the most appropriate conduct. This paper presents a set of techniques for identifying and quantifying regions of interest in prostatic images. Analyses were performed using multi-scale lacunarity and distinct classification methods: decision tree, support vector machine and polynomial classifier. The performance evaluation measures were based on area under the receiver operating characteristic curve (AUC). The most appropriate region for distinguishing the different tissues (normal, hyperplastic and neoplasic) was defined: the corresponding lacunarity values and a rule's model were obtained considering combinations commonly explored by specialists in clinical practice. The best discriminative values (AUC) were 0.906, 0.891 and 0.859 between neoplasic versus normal, neoplasic versus hyperplastic and hyperplastic versus normal groups, respectively. The proposed protocol offers the advantage of making the findings comprehensible to pathologists. (C) 2014 Elsevier Ltd. All rights reserved.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Hazard analysis and critical control points (HACCP) is one of the main tools currently used to ensure safety, quality and integrity of foods. So, the aim of this study was to develop and implement the HACCP program in the processing of pasteurized grade A milk Checklists were used to assess on the level of the pre requisites programs and on the sanitary classification of the dairy industry and the results were used as references for the development of the HACCP system. A "decision tree" protocol was used for the identification of the critical control points (CCP). No physical or chemical CCP were identified, whereas pasteurization and packaging were considered biological CCP For these CCP, the limits for prevention, monitoring needs, corrective actions, critical limits and verification procedures were established. The pre requisites program was essential for the establishment of the system. The implementation of the HACCP for the processing of grade A pasteurized milk was efficient to control the biological hazards and enabled the product to comply with the legislation specifications and achieve safety.

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The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.

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Pós-graduação em Zootecnia - FCAV

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A new species of Aparasphenodon is described from patches of arboreal restinga within the Atlantic Forest Biome, in a region known as Baixo Sul in southern Bahia, northeastern Brazil. Aparasphenodon arapapa sp. nov. is promptly diagnosed from other Aparasphenodon mainly by having small size (male snout-vent length 57.4-58.1 mm), loreal region flattened and wide, and canthus rostralis rounded and poorly elevated. The wide and flattened snout resembles that found in Triprion and Diaglena, and possibly is a parallelism (homoplasy) related to the phragmotic behavior of casque-headed tree frogs to their microhabitat usage. The decision to allocate the new species in the genus Aparasphenodon is discussed in detail, as the single morphological synapomorphy of the genus, the presence of a prenasal bone, is insufficient to morphologically relate the new species to Aparasphenodon, Triprion, or Diaglena.

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The objective of the present study was to develop a sequential sampling plan for the decision-making process to control Tenuipalpus heveae Baker (Acari: Tenuipalpidae), an important pest of the rubber tree crop. The experimental area was represented by 1,000 plants of the RRIM 600 clone divided in 100 plots with 10 plants each. Leaves were collected and the number of mites determined under laboratory conditions. The sequential sampling plan was developed in accordance with the Sequential Test Likelihood Ratio. The value 0.10 was pre-established for α and β representing type I and type II errors, respectively. The level of control adopted was six mites per 12 cm2. The operating characteristic curve and the curve of maximum expected sample were determined. Two lines were generated: the upper one, when the condition for chemical control is recommended (S1 = 23.3080 + 2.1972); and the lower, when chemical control is not recommended (S0 = -23.3080 + 2.1972). Sample size for the decision-making process to control T. heveae requires 6 to 18 plants. © 2013 Sociedade Entomológica do Brasil.