Machine Learning Techniques
Mostrando 1-12 de 73 artigos, teses e dissertações.
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1. Precision non-implantable neuromodulation therapies: a perspective for the depressed brain
Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magne
Braz. J. Psychiatry. Publicado em: 2020-08
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2. MULTILEVEL NONLINEAR MIXED-EFFECTS MODEL AND MACHINE LEARNING FOR PREDICTING THE VOLUME OF Eucalyptus SPP. TREES
ABSTRACT Volumetric equations is one of the main tools for quantifying forest stand production, and is the basis for sustainable management of forest plantations. This study aimed to assess the quality of the volumetric estimation of Eucalyptus spp. trees using a mixed-effects model, artificial neural network (ANN) and support-vector machine (SVM). The datab
CERNE. Publicado em: 2020-03
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3. Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques
Abstract Purpose Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in cre
RAUSP Manag. J.. Publicado em: 25/11/2019
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4. Perspectivas do uso de mineração de dados e aprendizado de máquina em saúde e segurança no trabalho
Resumo Introdução: a variedade, volume e velocidade de geração de dados (big data) possibilitam novas e mais complexas análises. Objetivo: discutir e apresentar técnicas de mineração de dados (data mining) e de aprendizado de máquina (machine learning) para auxiliar pesquisadores de Saúde e Segurança no Trabalho (SST) na escolha da técnica ad
Rev. bras. saúde ocup.. Publicado em: 04/11/2019
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5. 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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6. Preprocessing procedures and supervised classification applied to a database of systematic soil survey
ABSTRACT: Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches
Sci. agric. (Piracicaba, Braz.). Publicado em: 20/05/2019
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7. 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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8. An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
Abstract Structural Health Monitoring using raw dynamic measurements is the subject of several studies aimed at identifying structural modifications or, more specifically, focused on damage assessment. Traditional damage detection methods associate structural modal deviations to damage. Nevertheless, the process used to determine modal characteristics can in
Lat. Am. j. solids struct.. Publicado em: 14/03/2019
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9. Scenario reduction using machine learning techniques applied to conditional geostatistical simulation
Abstract One of the basic factors in mine operational optimization is knowledge regarding mineral deposit features, which allows to predict its behavior. This could be achieved by conditional geostatistical simulation, which allows to evaluate deposit variability (uncertainty band) and its impacts on project economics. However, a large number of realizations
REM, Int. Eng. J.. Publicado em: 2019-03
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10. Using UAV for automatic lithological classification of open pit mining front
Abstract Mine planning is dependent on the natural lithologic features and on the definition of their limits. The geological model is constantly updated during the life of the mine, based on all the information collected so far, plus the knowledge developed from the exploration stage up to the mine closure. As the mine progresses, the amount of available dat
REM, Int. Eng. J.. Publicado em: 2019-03
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11. USE OF COMPUTATIONAL TOOLS AS SUPPORT TO THE CROSS-MAPPING METHOD BETWEEN CLINICAL TERMINOLOGIES
RESUMO Objetivo: refletir sobre o uso de ferramentas computacionais no método de mapeamento cruzado entre terminologias clínicas. Método: estudo de reflexão. Resultados: o método de mapeamento cruzado consiste na obtenção de listagem de termos, por meio de extração e normalização; ligação entre os termos da listagem e os da base de referên
Texto contexto - enferm.. Publicado em: 14/02/2019
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12. Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon
ABSTRACT: Ultisols are the most common soil order in the Brazilian Amazon. The Legal Amazon (LA) has an area of 5 × 106 km2, with few accessible areas, which restricts studies of soils at a detailed level. The pedological properties can be estimated more efficiently using statistical procedures and machine learning techniques, tools which are capable of rec
Rev. Bras. Ciênc. Solo. Publicado em: 06/12/2018