Prediction Of Random Variables
Mostrando 1-12 de 16 artigos, teses e dissertações.
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1. Predictive model for difficult laryngoscopy using machine learning: retrospective cohort study
Abstract Background Both predictions and predictors of difficult laryngoscopy are controversial. Machine learning is an excellent alternative method for predicting difficult laryngoscopy. This study aimed to develop and validate practical predictive models for difficult laryngoscopy through machine learning. Methods Variables for the prediction of difficul
Brazilian Journal of Anesthesiology. Publicado em: 2022
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2. Digital mapping of soil attributes using machine learning
RESUMO O mapeamento de atributos químicos do solo em larga escala pode acarretar em ganhos no planejamento de uso e ocupação do mesmo. Existem diferentes técnicas disponíveis para tal fim, cujos desempenhos devem ser testados para diferentes situações de paisagem. Objetivou-se neste trabalho espacializar atributos químicos do solo, comparando oito m�
Rev. Ciênc. Agron.. Publicado em: 04/11/2019
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3. Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction
ABSTRACT: Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated
Sci. agric. (Piracicaba, Braz.). Publicado em: 2019-07
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4. Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment
ABSTRACT: Different uses of soil legacy data such as training dataset as well as the selection of soil environmental covariables could drive the accuracy of machine learning techniques. Thus, this study evaluated the ability of the Random Forest algorithm to predict soil classes from different training datasets and extrapolate such information to a similar a
Sci. agric. (Piracicaba, Braz.). Publicado em: 2019-05
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5. Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach
ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two differ
Sci. agric. (Piracicaba, Braz.). Publicado em: 2018-08
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6. Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands
ABSTRACT Brazil has many rural properties with unmanaged eucalyptus stands. These plantations are heterogeneous, presenting different tree sizes, advanced ages, and large wood volumes that can be quantified using forest inventories. The prediction error of dendrometric variables, mainly in highly heterogeneous areas, can be associated with inadequate forest
Floresta Ambient.. Publicado em: 30/07/2018
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7. Avaliação estatística do erro de modelos de resistência para elementos lineares de concreto armado da ABNT NBR 6118:2007 / Statistical evaluation of resistance modelling error for linear elements of concrete structures of ABNT NBR 6118:2007
In a design of structures, it should be considered the intrinsic uncertainties to the present variables in the structure and in the structural concept, such as intensity and action distribution, mechanic properties of the material, geometric parameters of the structure and structural analysis and calculus models. Thus, the structural safety can only be measu
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 03/08/2012
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8. Prediction of non-carcass components in cattle
This study was conducted to develop equations to predict chemical composition of head, limbs, hide and blood in cattle. A database containing 335 animals from 10 trials, with 221 Nellore, 38 Nellore-Simmental and 76 Nellore-Angus (96 steers, 118 heifers and 121 bulls) animals was used. Models were constructed to estimate water, ether extract (EE), crude prot
R. Bras. Zootec.. Publicado em: 2012-08
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9. Melhor predição linear não viciada (Blup) de valores genéticos no melhoramento de Pinus.
1997
Boletim de Pesquisa Florestal. Publicado em: 2011
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10. Meta-analyses of experimental data in the animal sciences
In certain areas of animal research, such as nutrition, quantitative summarizations of literature data are periodically needed. In such instances, statistical methods dealing with the analysis of summary data (generally from the literature) must be used. These methods are known as meta-analyses. The implementation of a meta-analysis is done in several phases
Revista Brasileira de Zootecnia. Publicado em: 2007-07
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11. Evaluation of the nutritional status and of the soil fertility for the plantain crop (Musa AAB subgroup plantain cv. Hárton). / Avaliação do estado nutricional e da fertilidade do solo na cultura do plátano (Musa AAB subgrupo Plátano cv. Hárton).
The aim of this work is to suggest an adequate methodology for the evaluation of the nutritional status and of the fertility of soil for plantain grown in the South of Lake Maracaibo, Venezuela. Initially the area to sample was split into similar soil series. Four high yielding plantations were selected within a soil series of intermediate texture. Afterward
Publicado em: 2003
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12. Comparison of two prospective rate-setting models: the DRG and PIR models.
The article compares two statistical prospective hospital reimbursement models: the diagnosis-related group (DRG) model and the prospective individualized reimbursement (PIR) model. Both models are applied to the same variables from the same data set, a random sample of 10,000 hospital discharges in Maryland in 1983. For comparative purposes, the two statist