Knn
Mostrando 1-12 de 94 artigos, teses e dissertações.
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1. Identifying olive oil fraud and adulteration using machine learning algorithms
As olive oil (OO) is more expensive than other vegetable oils, it is usually adulterated by blending it with more economic edible oils such as cottonseed oil (CSO), canola oil (CO), and soybean oil (SO). This research aimed to determine the fatty acid compositions obtained as a result of blending different proportions of CSO, CO and SO with OO using a gas ch
Química Nova. Publicado em: 2022
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2. CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS
RESUMO - A análise de imagem associada com modelos de aprendizado de máquina pode ser uma excelente ferramenta de classificação para genótipos de fava, além de ser um sistema de baixo custo. A produção de feijão-fava é realizada por agricultores familiares, principalmente, nas regiões Nordeste e Sul do país, apresentando importância econômica e
Revista Caatinga. Publicado em: 2022
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3. X-ray Scattering and Chemometrics as Tools to Assist in the Identification of Gunshot Residues by Wavelength Dispersive X-ray Fluorescence Spectrometry
Wavelength dispersion X-ray fluorescence spectrometry (WDXRF) is a non-destructive technique and therefore attractive for gunshot residues (GSR) analysis. It is well known for determination of inorganic constituents of samples. However, X-ray scattering region spectral data is not commonly used, although it may provide information about organic constituents
J. Braz. Chem. Soc.. Publicado em: 2020-12
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4. K-NEAREST NEIGHBORS METHOD FOR PREDICTION OF FUEL CONSUMPTION IN TRACTOR-CHISEL PLOW SYSTEMS
ABSTRACT Most important farm operations require a significant amount of energy, and this consumes a major portion of the farm's budget. Consequently, analyzing the fuel consumption of agricultural machinery for farm operations of different sizes makes it possible to predict fuel consumption to set an appropriate budget for energy. The main purpose of this st
Eng. Agríc.. Publicado em: 09/12/2019
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5. Modeling of stem form and volume through machine learning
Abstract Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function f
An. Acad. Bras. Ciênc.. Publicado em: 18/10/2018
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6. Quantitative MRI data in Multiple Sclerosis patients: a pattern recognition study
Abstract Introduction Multiple Sclerosis (MS) is a neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Quantitative Magnetic Resonance Imaging (qMRI) enables a detailed characterization of brain tissue, but generates a large number of numerical results. In this study, we elucidated the main qMRI techniques a
Res. Biomed. Eng.. Publicado em: 28/05/2018
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7. Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation
ABSTRACT Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Rangi
An. Acad. Bras. Ciênc.. Publicado em: 2018-03
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8. Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees
Abstract Introduction Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduce
Res. Biomed. Eng.. Publicado em: 17/08/2017
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9. Wrappers Feature Selection in Alzheimer’s Biomarkers Using kNN and SMOTE Oversampling
RESUMO Biomarcadores são medidas biológicas que ajudam a rastrear e compreender a progressão fisiopatológica de várias doenças. A combinação de diferentes modalidades de biomarcadores muitas vezes permite uma classificação de diagnóstico preciso. Na doença de Alzheimer (DA), os biomarcadores são indispensáveis para identificar indivíduos cogni
TEMA (São Carlos). Publicado em: 2017-04
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10. A fully automatic method for recognizing hand configurations of Brazilian sign language
Abstract Introduction Sign language is a collection of gestures, postures, movements, and facial expressions used by deaf people. The Brazilian sign language is Libras. The use of Libras has been increased among the deaf communities, but is still not disseminated outside this community. Sign language recognition is a field of research, which intends to help
Res. Biomed. Eng.. Publicado em: 2017-03
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11. Influence of Yttrium Dopant on the Structure and Electrical Conductivity of Potassium Sodium Niobate Thin Films
KNN thin films with diverse yttrium concentration (mol % = 0, 0.1, 0.3, 0.5, 0.7 and 0.9) were fabricated using sol-gel spin coating technique. Doped KNN revealed that Y3+ was successfully doped into the ABO3 perovskite lattice without changing the phase formation of KNN. The thickness of the deposited layer of KNN produced with increasing dopant concentrati
Mat. Res.. Publicado em: 24/10/2016
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12. CLASSIFICAÇÃO DE ÁGUAS MINERAIS BASEADA EM IMAGENS DIGITAIS OBTIDAS POR SMARTPHONES
This work describes a new procedure for classification of mineral waters based on digital images acquired by smartphones. Commercial waters from eight mineral springs plus distilled water and tap water were combined with eriochrome T black or murexide and transferred to a cuvette, which was positioned into a light controlled chamber. RGB (Red, Blue and Green
Quím. Nova. Publicado em: 2016-08