Machine Learning
Mostrando 13-24 de 313 artigos, teses e dissertações.
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13. 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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14. 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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15. 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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16. 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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17. 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
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18. Geostatistics or machine learning for mapping soil attributes and agricultural practices
ABSTRACT Applying the upcoming technologies in agriculture has been a major economic, environmental and social challenge for scientists and farmers. In order to overcome such challenge, this study evaluated the advantages and limitations of using geostatistics and machine learning for soil mapping in agricultural practices and soil surveys. The study occurre
Rev. Ceres. Publicado em: 2020-08
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19. 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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20. 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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21. Santos HG, Nascimento CF, Izbicki R, Duarte YAO, Chiavegatto Filho ADP. Machine learning para análises preditivas em saúde: exemplo de aplicação para predizer óbito em idosos de São Paulo, Brasil. Cad Saúde Pública 2019; 35(7):e00050818.
Cad. Saúde Pública. Publicado em: 20/01/2020
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22. Machine-learning approaches in psychotherapy: a promising tool for advancing the understanding of the psychotherapeutic process
Braz. J. Psychiatry. Publicado em: 09/12/2019
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23. 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
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24. Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype
Abstract Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of variou
Braz. arch. biol. technol.. Publicado em: 25/11/2019