1. Prediction of restrained shrinkage crack width of slag mortar composites using data mining techniques

ABSTRACT The purpose of this study is to develop data mining models to predict restrained shrinkage crack widths of slag mortar cementitious composites. A database published by BILIR et al. [1] was used to develop these models. As a modelling tool R environment was used to apply these data mining (DM) techniques. Several algorithms were tested and analyzed u

Matéria (Rio J.). Publicado em: 25/11/2019

2. High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

ABSTRACT. The genetic differentiation of grapevine rootstock varieties was inferred by the Artificial Neural Network approach based on the Self-Organizing Map algorithm. A combination of RAPD and SSR molecular markers, yielding polymorphic informative loci, was used to determine the genetic characterization among the rootstock varieties 420-A, Schwarzmann, I

Acta Sci., Agron.. Publicado em: 21/10/2019

3. Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest

Resumo A modelagem do recrutamento em florestais tropicais é importante para estudos de sustentabilidade do manejo florestal, por dar subsídio adequado à recuperação do estoque de madeira. O objetivo do trabalho foi estimar o recrutamento após a colheita de madeira, empregando um modelo de rede neural artificial (RNA). A área de estudo está localizad

Ciênc. Florest.. Publicado em: 30/09/2019

4. PREDICTION OF EFFICIENCY IN COLOMBIAN HIGHER EDUCATION INSTITUTIONS WITH DATA ENVELOPMENT ANALYSIS AND NEURAL NETWORKS

ABSTRACT This paper shows the results of a research of the application of data envelopment analysis (DEA) together with artificial neural networks (ANN) of higher education institutions in Colombia during the years 2011-2013, for the purpose of evaluating the technical efficiency of Colombian higher education institutions and subsequently carry out predictio

Pesqui. Oper.. Publicado em: 23/09/2019

5. Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari

ABSTRACT The objective of this study was to obtain regression equations and artificial neural networks (ANNs) for prediction and prognosis of the yield of Pinus caribaea var. caribaea Barrett & Golfari. The data used for modeling comes from measuring the variables diameter at breast height (DBH) and total height (Ht) in 550 temporary plots and 14 circular pe

Floresta Ambient.. Publicado em: 12/09/2019

6. ARTIFICIAL NEURAL NETWORKS APPLIED IN FOREST BIOMETRICS AND MODELING: STATE OF THE ART (JANUARY/2007 TO JULY/2018)

ABSTRACT Artificial Intelligence has been an important support tool in different spheres of activity, enabling knowledge aggregation, process optimization and the application of methodologies capable of solving complex real problems. Despite focusing on a wide range of successful metrics, the Artificial Neural Network (ANN) approach, a technique similar to t

CERNE. Publicado em: 09/09/2019

7. Simulation and Prediction for a Satellite Temperature Sensors Based on Artificial Neural Network

ABSTRACT: Spacecrafts in space environment are exposed to several kinds of thermal sources such as radiation, albedo and emitted IR from the earth. The thermal control subsystem in spacecraft is used to keep all parts operating within allowable temperature ranges. A failure in one or many temperature sensors could lead to abnormal operation. Consequently, a

J. Aerosp. Technol. Manag.. Publicado em: 26/08/2019

8. PEDOFUNCTIONS APPLIED TO THE LEAST LIMITING WATER RANGE TO ESTIMATE SOIL WATER CONTENT AT SPECIFIC POTENTIALS

ABSTRACT The least limiting water range (LLWR) is a soil physical quality indicator that receives much attention. It has been criticized and put to the test regarding mathematical models that compose it since they describe the behavior of soil physical attributes in a simplified way. This study aimed to assess the efficiency of some pedofunctions proposed in

Eng. Agríc.. Publicado em: 12/08/2019

9. Mapeamento dos aspectos hidrogeoquímicos de águas subterrâneas a partir de estatística multivariada e redes neurais artificiais

RESUMO O objetivo central deste trabalho foi realizar o mapeamento dos aspectos hidrogeoquímicos de águas subterrâneas usando a estatística multivariada e redes neurais artificiais como subsídio para identificação de padrões espaciais. Para tal, foi executado um estudo de caso em aquíferos no município de Lençóis, Bahia, na região da Chapada Dia

Eng. Sanit. Ambient.. Publicado em: 05/08/2019

10. Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed

ABSTRACT. The present study aimed to apply artificial neural networks to predict the breeding values of body weight in 6-month age of Kermani sheep. For this purpose, records of 867 lambs including lamb sex, dam age, birth weight, weaning weight, age at 3-month (3 months old), age at 6-month (6 months old) and body weight at 3 months of age were used. Firstl

Acta Sci., Anim. Sci.. Publicado em: 04/07/2019

11. Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

Resumo: O objetivo deste trabalho foi comparar métodos para obtenção do índice de sítio para povoamentos de eucalipto (Eucalyptus spp.), bem como avaliar seus impactos na estabilidade desse índice em bases de dados com e sem a presença de “outliers”. Foram testados três métodos, com uso de regressão linear, regressão quantílica e rede neural

Pesq. agropec. bras.. Publicado em: 27/05/2019

12. Color reduction of raw sugar syrup using hydrogen peroxide

Resumo Uma solução comercial de H2O2 (35%, v/v) foi avaliada como agente clarificante em xarope de açúcar tipo VHP (alta polarização), em arranjo experimental de redes neurais artificiais (RNA). Quinze corridas experimentais foram conduzidas e as amostras foram coletadas em intervalos de tempo de 0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 75 e 90 min. Os en

Braz. J. Food Technol.. Publicado em: 20/05/2019