Learning Of Machine
Mostrando 1-12 de 240 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. Rapid Recognizing the Producing Area of a Tobacco Leaf Using Near-Infrared Technology and a Multi-Layer Extreme Learning Machine Algorithm
A novel recognition method was put forward to identify the producing areas of the flue-cured tobacco leaves rapidly and non-destructively by using a near-infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. In contrast to traditional linear discriminant analysis (LDA) and extreme learning machine (ELM) algorithms, the ac
Journal of the Brazilian Chemical Society. Publicado em: 2022
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3. 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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4. 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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5. CLOROFILA EXTRAÍDA DE RESÍDUO INDUSTRIAL DA ERVA-MATE (Ilex paraguaiensis) UMA POSSIBILIDADE DE ECONOMIA CIRCULAR
Chlorophyll is the most abundant green pigment on the planet, it is unstable and decomposes naturally. Mate-herb is a traditional native plant in the southern region of South America, and its tea is part of the local culture and extractive agriculture. The mate-herb industry generates as a by-product a resinous material rich in chlorophyll whose use is propo
Química Nova. Publicado em: 2022
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6. Cascade Modeling of the Measuring System Used to Assess S-Parameters of Anchor Rods on Power Transmission Lines Guyed Towers
Abstract The structural condition of the cable-stayed towers anchorage on power transmission lines requires constant monitoring. Maintenance routines must be able to identify faulty anchor rods and substitute them to avoid tower collapses and power delivery interruptions. Modern statistical diagnostic systems based on machine learning requires the generation
Journal of Microwaves, Optoelectronics and Electromagnetic Applications. Publicado em: 2022
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7. Impact assessment of emergency care units on hospitalizations for respiratory system diseases in Brazil
Resumo As Unidades de Pronto Atendimento 24h (UPAs) compõem a Política de Atenção a Urgências e Emergências (PNAU) implementada pelo Governo Federal. São componentes pré-hospitalares fixos do SUS, cujo objetivo é o atendimento resolutivo de urgência a pacientes que sofrem quadros clínicos agudos, e o primeiro atendimento em casos cirúrgicos. Desd
Ciência & Saúde Coletiva. Publicado em: 2022
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8. A review of systems biology research of anxiety disorders
The development of “omic” technologies and deep phenotyping may facilitate a systems biology approach to understanding anxiety disorders. Systems biology approaches incorporate data from multiple modalities (e.g., genomic, neuroimaging) with functional analyses (e.g., animal and tissue culture models) and mathematical modeling (e.g., machine learning) to
Braz. J. Psychiatry. Publicado em: 2021-08
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9. Applicability of computer vision in seed identification: deep learning, random forest, and support vector machine classification algorithms
ABSTRACT The use of computer image analysis can assist the extraction of morphological information from seeds, potentially serving as a resource for solving taxonomic problems that require extensive training by specialists whose primary method of examination is visual identification. We propose to test the ability of deep learning, SVM and random forest algo
Acta Bot. Bras.. Publicado em: 2021-03
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10. A note on real estate appraisal in Brazil
Abstract Brazilian banks commonly use linear regression to appraise real estate: they regress price on features like area, location, etc, and use the resulting model to estimate the market value of the target property. But Brazilian banks do not test the predictive performance of those models, which for all we know are no better than random guesses. That int
Rev. Bras. Econ.. Publicado em: 2021-03
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11. Prediction of impacts on liver enzymes from the exposure of low-dose medical radiations through artificial intelligence algorithms
SUMMARY OBJECTIVES: This study aimed to develop artificial intelligence and machine learning-based models to predict alterations in liver enzymes from the exposure of low annual average effective doses in radiology and nuclear medicine personnel of Institute of Nuclear Medicine and Oncology Hospital. METHODS: Ninety workers from the Radiology and Nuclear M
Rev. Assoc. Med. Bras.. Publicado em: 2021-02
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12. Automated Framework for Developing Predictive Machine Learning Models for Data-Driven Drug Discovery
The increasing availability of extensive collections of chemical compounds associated with experimental data provides an opportunity to build predictive quantitative structure-activity relationship (QSAR) models using machine learning (ML) algorithms. These models can promote data-driven decisions and have the potential to speed up the drug discovery process
J. Braz. Chem. Soc.. Publicado em: 2021-01