Mineração de dados: aplicações, ferramentas, tipos de aprendizado e outros subtemas

Carvalho, Déborah Ribeiro; Dallagassa, Marcelo Rosano

doi:10.5380/atoz.v3i2.41340

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The use of machine learning algorithms in the medical field has provided important results. This type of learning belongs to Artificial Intelligence because it is an automation process that generalizes the experience expressed in a set of data (Carvalho and Dallagassa, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms

et al. 2020

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-2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in the world, but its mutagenic and carcinogenic potential is still controversial. We simulated environmental exposure to 2,4-D, with the objective of evaluating the genotoxic effect of acute and chronic exposure to 2,4-D in rodents. We also evaluated the performance of machine learning algorithms in detecting differences in exposure groups through recognition performed from genotoxic characteristics. In the acute phase, 88 Swiss mice were used, distributed in five groups and exposed to nebulizations at different time intervals (24, 48, 72 and 192 hr). In the chronic phase, 88 Wistar rats were used, distributed in two groups (inhaled and oral) and exposed for six months. Femoral bone marrow cells were collected for a micronucleus test and comet assay. Data were evaluated by pattern recognition algorithms. In acute exposure, medium and high concentrations induced DNA damage in the comet assay, but these concentrations did not increase micronucleated cells. In the chronic exposure, there was an increase in micronuclei and DNA damage in the comet assay in all exposed groups regardless of the exposure route. The data showed a robust pattern of distinction between exposed and nonexposed groups to 2,4-D. Our data showed that both acute inhalation exposure and chronic oral and inhalation exposure to 2,4-D can cause genotoxic effects regardless of concentration. Machine learning showed a clear distinction between the control groups and those exposed to 2,4-D, and the effects of exposure are not concentration-dependent.

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Section: Introductionmentioning

confidence: 99%

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms

et al. 2020

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“…Entretanto, muitas vezes as informações são perdidas por não se conseguir extrair conhecimentos úteis desses dados (Bramer, 2007;Keim & Ward, 2002). Neste sentido, a área de data mining, utilizando uma combinação de conhecimentos nas áreas de estatística, banco de dados e inteligência artificial, permite analisar grandes volumes de dados e extrair conhecimentos relevantes sobre um fenômeno (Bramer, 2007;Carvalho & Dallagassa, 2014). Ainda, o avanço tecnológico também propiciou o surgimento dos telefones móveis.…”

Section: Introductionunclassified

Visualização de dados para extração de conhecimento: um estudo de caso

Matsuba¹,

Mattedi

2021

AtoZ

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Introdução: O rápido crescimento no volume de dados coletados nos últimos anos está tornando o processo de análise e extração de conhecimento cada vez mais complexo. Organizações têm tido dificuldades em agregar essas grandes quantidades de dados em análises úteis para dar suporte a suas decisões. Objetivo: Avaliar a implantação de ferramentas de data mining visual em uma startup de seguros (insurtech) para smartphones. Método: Foram desenvolvidos painéis dinâmicos usando o software Tableau. Os dados de alimentação do sistema foram divididos dois grupos (“Medidas” e “Dimensões”) para cada tema de análise escolhido. Também foi feita uma pesquisa de usabilidade junto aos usuários dos painéis. Resultados: A agregação de diversas subtelas e informações em um mesmo painel foi importante para os usuários visualizarem novos padrões. Conclusão: A introdução das novas ferramentas de descoberta de conhecimento fez com que os usuários passassem a apresentar um conhecimento mais profundo sobre o tema e fazerem melhores análises sobre os padrões.

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“…Moreover, it is constituted by selection, process, transformation, DM, and assessment stages (FAYYAD, 1996), involving a series of areas related in a multidisciplinary manner (CARVALHO & DALLAGASSA, 2014).…”

Section: Introductionmentioning

confidence: 99%

Data Mining for the Assessment of Management Areas in Precision Agriculture

et al. 2017

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Precision Agriculture (PA) uses technologies with the aim of increasing productivity and reducing the environmental impact by means of site-specific application of agricultural inputs. In order to make it economically feasible, it is essential to improve the current methodologies as well as proposing new ones, in which data regarding productivity, soil, and compound indicators are used to determine Management Areas (MAs). These units are heterogeneous areas within the same region. With these methodologies, data mining (DM) techniques and algorithms may be used. In order to integrate DM techniques to PA, the aim of this study was to associate MAs created for soy productivity using the Fuzzy C-means algorithm by SDUM software over a 9.9-ha plot as the reference method. It was in opposition to the grouping of 2, 3, and 4 clusters obtained by the Kmeans classification algorithms, with and without the Principal Component Analysis (PCA), and the EM algorithm using chemical and physical data of the soil samples collected in the same area during the same period. The EM algorithm with PCA modeling had a superior performance than Kmeans based on hit rates. It is noteworthy that the greater the number of analyzed MAs, the lower the percentage of hits, in agreement with the result shown by SDUM, which shows that two MAs compose the best configuration for this studied area.

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Mineração de dados: aplicações, ferramentas, tipos de aprendizado e outros subtemas

Cited by 3 publications

References 3 publications

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms

Visualização de dados para extração de conhecimento: um estudo de caso

Data Mining for the Assessment of Management Areas in Precision Agriculture

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