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| Registros recuperados: 38 | |
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| Pereira,DF; do Vale,MM; Zevolli,BR; Salgado,DD. |
| Layer mortality due to heat stress is an important economic loss for the producer. The aim of this study was to determine the mortality pattern of layers reared in the region of Bastos, SP, Brazil, according to external environment and bird age. Data mining technique were used based on monthly mortality records of hens in production, 135 poultry houses, from January 2004 to August 2008. The external environment was characterized according maximum and minimum temperatures, obtained monthly at the meteorological station CATI in the city of Tupã, SP, Brazil. Mortality was classified as normal (£ 1.2%) or high (> 1.2%), considering the mortality limits mentioned in literature. Data mining technique produced a decision tree with nine levels and 23 leaves,... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Data mining; Layer production; Mortality; Thermal comfort. |
| Ano: 2010 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2010000400008 |
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| Chagas,César da Silva; Carvalho Júnior,Waldir de; Pinheiro,Helena Saraiva Koenow; Xavier,Pedro Armentano Mudado; Bhering,Silvio Barge; Pereira,Nilson Rendeiro; Calderano Filho,Braz. |
| ABSTRACT: Planning sustainable use of land resources requires reliable information about spatial distribution of soil physical and chemical properties related to environmental processes and ecosystemic functions. In this context, cation exchange capacity (CEC) is a fundamental soil quality indicator; however, it takes money and time to obtain this data. Although many studies have been conducted to spatially quantify soil properties on various scales and in different environments, not much is known about interactions between soil properties and environmental covariates in the Brazilian semiarid region. The goal of this study was to evaluate the efficiency of random forest and cokriging models applied to predict CEC in the Brazilian semiarid region. The... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Data mining; Geostatistics; Landsat 5; Legacy data; Soil survey. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100311 |
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| Campos,Alcinei Ribeiro; Giasson,Elvio; Costa,José Janderson Ferreira; Machado,Israel Rosa; Silva,Elisângela Benedet da; Bonfatti,Benito Roberto. |
| ABSTRACT A large number of predictor variables can be used in digital soil mapping; however, the presence of irrelevant covariables may compromise the prediction of soil types. Thus, algorithms can be applied to select the most relevant predictors. This study aimed to compare three covariable selection systems (two filter algorithms and one wrapper algorithm) and assess their impacts on the predictive model. The study area was the Lajeado River Watershed in the state of Rio Grande do Sul, Brazil. We used forty predictor covariables, derived from a digital elevation model with 30 m resolution, in which the three selection models were applied and separated into subsets. These subsets were used to assess performance by applying four prediction algorithms. The... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Data mining; Geomorphometric variables; Soil prediction. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100315 |
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| Celik,Senol; Eyduran,Ecevit; Karadas,Koksal; Tariq,Mohammad Masood. |
| ABSTRACT The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth) and testicular (testicular length, scrotal length, and scrotal circumference) measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1) and with interaction (MARS_2) terms. The superiority... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: ANN; Artificial intelligence; Data mining; Decision tree; MARS algorithm. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017001100863 |
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| Orhan,Hikmet; Eyduran,Ecevit; Tatliyer,Adile; Saygici,Hasan. |
| ABSTRACT This study was conducted on 2049 eggs, collected from commercial white layer hybrids, with the purpose of predicting egg weight (EW) from egg quality characteristics such as shell weight (SW), albumen weight (AW), and yolk weight (YW). In the prediction of EW, ridge regression (RR), multiple linear regression (MLR), and regression tree analysis (RTM) methods were used. Predictive performance of RR and MLR methods was evaluated using the determination coefficient (R2) and variance inflation factor (VIF). R2 (%) coefficients for RR and MLR methods were found as 93.15% and 93.4% without multicollinearity problems due to very low VIF values, varying from 1 to 2, respectively. Being a visual, non-parametric analysis technique, regression tree method... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Chaid algorithm; Data mining; Decision tree; Multiple regression. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982016000700380 |
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| Pereira,Paulo Rodrigo Ramos Xavier; Barcellos,Júlio Otávio Jardim; Federizzi,Luiz Carlos; Lampert,Vinícius do Nascimento; Canozzi,Maria Eugênia Andrighetto; Marques,Pedro Rocha. |
| The objectives of this research were to analyse data on the international market of frozen boneless beef and to classify its participants into groups according to their trade relationships, identifying the main factors that influence the preference of a country to beef from a determined supplier country. International beef trade is composed of two markets: in one of them, the relationships between supplier and client depend on the lowest price, and Brazil is found in favorable conditions; and the other, the relationships are preferably based on the sanitary quality of the herd and traceability systems recognized by the purchaser, to which Brazilian participation is low. |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Bovine spongiform encephalopaty (BSE); Cluster analysis; Data mining; Foot and mouth disease; International trade beef. |
| Ano: 2011 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982011000100028 |
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| Maia,Ana Paula de Assis; Oliveira,Stanley Robson de Medeiros; Moura,Daniella Jorge de; Sarubbi,Juliana; Vercellino,Rimena do Amaral; Medeiros,Brenda Batista Lemos; Griska,Paulo Roberto. |
| Thermal comfort is of great importance in preserving body temperature homeostasis during thermal stress conditions. Although the thermal comfort of horses has been widely studied, there is no report of its relationship with surface temperature (T S). This study aimed to assess the potential of data mining techniques as a tool to associate surface temperature with thermal comfort of horses. T S was obtained using infrared thermography image processing. Physiological and environmental variables were used to define the predicted class, which classified thermal comfort as "comfort" and "discomfort". The variables of armpit, croup, breast and groin T S of horses and the predicted classes were then subjected to a machine learning process. All variables in the... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Feature selection methods; Data mining; Surface temperature; Infrared thermography; Thermoregulation. |
| Ano: 2013 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162013000600001 |
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| Arruda,Gustavo Pais de; Demattê,José A. M.; Chagas,César da Silva; Fiorio,Peterson Ricardo; Souza,Arnaldo Barros e; Fongaro,Caio Troula. |
| ABSTRACT Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soil-landscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Map extrapolation; Pedological survey; Landscape attributes; Pedological classes; Data mining. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300266 |
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| Farhate,Camila Viana Vieira; Souza,Zigomar Menezes de; Oliveira,Stanley Robson de Medeiros; Carvalho,João Luís Nunes; Scala Júnior,Newton La; Santos,Ana Paula Guimarães. |
| ABSTRACT: The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil CO2 emission; Data mining; Variable selection; Soil temperature; Soil organic matter. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000300216 |
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| SCHIKOWSKI,ANA B.; CORTE,ANA P.D.; RUZA,MARIELI S.; SANQUETTA,CARLOS R.; MONTAÑO,RAZER A.N.R.. |
| 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 for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified.... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Artificial intelligence; Data mining; Random forest; ANN. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703389 |
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| Dota,Mara Andrea; Cugnasca,Carlos Eduardo; Barbosa,Domingos Sávio. |
| Agriculture, roads, animal farms and other land uses may modify the water quality from rivers, dams and other surface freshwaters. In the control of the ecological process and for environmental management, it is necessary to quickly and accurately identify surface water contamination (in areas such as rivers and dams) with contaminated runoff waters coming, for example, from cultivation and urban areas. This paper presents a comparative analysis of different classification algorithms applied to the data collected from a sample of soil-contaminated water aiming to identify if the water quality classification proposed in this research agrees with reality. The sample was part of a laboratory experiment, which began with a sample of treated water added with... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Environmentalcontrol; Runoff; Wireless sensor networks; Machine learning; Data mining. |
| Ano: 2015 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782015000200267 |
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| Registros recuperados: 38 | |
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