Learning Algorithms
Mostrando 1-12 de 159 artigos, teses e dissertações.
-
1. 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
-
2. Deep learning model-assisted detection of kidney stones on computed tomography
ABSTRACT Introduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and
International braz j urol. Publicado em: 2022
-
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
-
4. 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
-
5. 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
-
6. 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
-
7. 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
-
8. 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
-
9. Land-use influence on the soil hydrology: An approach in upper Grande River basin, Southeast Brazil
RESUMO A Bacia do Alto Grande (ARG) é responsável pela drenagem de vários rios no sudeste do Brasil, sendo uma região hidrológica de grande importância para o Sistema Elétrico Brasileiro. Portanto, estudos sobre a disponibilidade de água nesta região são indispensáveis para uma melhor tomada de decisão na gestão dos recursos hídricos. O objetiv
Ciênc. agrotec.. Publicado em: 09/12/2019
-
10. Meta-análise sobre Conhecimento para Ensinar Divisão de Frações
Resumo O objetivo deste artigo é identificar as principais contribuições de estudos sobre conhecimento docente relativo ao ensino e à aprendizagem da divisão de frações e analisar como tais resultados contribuem para responder a pergunta: que conjunto de conhecimentos um professor precisa para ensinar e fazer aprender divisão de frações? Realizamos
Bolema. Publicado em: 02/12/2019
-
11. Abordagem das Dificuldades de Ensino e Aprendizagem do Domínio da Estatística na Graduação em Psicologia: um olhar através do contrato didático
Resumo O ensino de conteúdos conceituais, competências e habilidades relacionados à análise quantitativa de dados nos cursos de graduação em Psicologia tem se mostrado problemático. Enquanto domínio da Matemática, a Estatística de fato se distancia dos saberes usualmente trabalhados nas demais disciplinas. O estudo em questão buscou investigar asp
Bolema. Publicado em: 02/12/2019
-
12. A NOVEL RAISIN SEGMENTATION ALGORITHM BASED ON DEEP LEARNING AND MORPHOLOGICAL ANALYSIS
ABSTRACT We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region
Eng. Agríc.. Publicado em: 04/11/2019