Neural Network
Mostrando 13-24 de 875 artigos, teses e dissertações.
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13. Discrimination of pores and cracks in iron ore pellets using deep learning neural networks
Abstract The discrimination between pores and cracks is an important step in the microstructural analysis of iron ore pellets. While the porosity is fundamental during the reduction process in blast furnaces, cracks are strongly detrimental to the mechanical strength. The usual image processing tools cannot automatically discriminate between these two types
REM, Int. Eng. J.. Publicado em: 2020-06
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14. EVALUATION OF MECHANICAL AND FLAME RETARDANT PROPERTIES OF MEDIUM DENSITY FIBERBOARD USING ARTIFICIAL NEURAL NETWORK
ABSTRACT The present study presents the application of artificial neural network (ANN) to predict the modulus of rupture (MOR) and mass loss (ML) of the fire retarded fiberboard. Hence, the effect of adding the fire retardants including boric acid, borax and ammonium sulfate was evaluated on MOR and ML of fiberboard manufactured at different press temperatur
CERNE. Publicado em: 2020-06
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15. Enurese noturna: uma condição comórbida,
ABSTRACT The present study presents the application of artificial neural network (ANN) to predict the modulus of rupture (MOR) and mass loss (ML) of the fire retarded fiberboard. Hence, the effect of adding the fire retardants including boric acid, borax and ammonium sulfate was evaluated on MOR and ML of fiberboard manufactured at different press temperatur
J. Pediatr. (Rio J.). Publicado em: 2020-06
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16. Artificial neural network for determining the hedonic score of texture of and distinguishing different grades of ham sausages
Abstract The preference of consumers of ham sausages is mainly determined on its texture. A method of determining hedonic score of texture and distinguishing different grades of ham sausages based on artificial neural network was established in this study. The topological texture of the artificial neural network was developed on the basis of analyzing the ha
Food Sci. Technol. Publicado em: 2020-03
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17. Kinetics of Lumefantrine Thermal Decomposition Employing Isoconversional Models and Artificial Neural Network
Thermal analysis can be used to determine shelf-life and kinetic parameters in pharmaceutical systems. This work investigates the kinetic of lumefantrine thermal decomposition, an antimalarial, using non-isothermal and isothermal experimental data. The non-isothermal conditions are analyzed applying Vyazovkin method, while isothermal conditions employ models
J. Braz. Chem. Soc.. Publicado em: 2020-03
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18. Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network
Abstract We present a general investigation of a Long-Period Grating (LPG) for transverse strain measurement. The transverse strain sensing characteristics, for instance, the load intensity and azimuthal angle, are analyzed with the data set generated by the LPG sensor and probed by artificial neural network (ANN). Furthermore, we evaluate and compare the pr
J. Microw. Optoelectron. Electromagn. Appl.. Publicado em: 2020-03
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19. 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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20. ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF PHYSIOLOGICAL AND PRODUCTIVE VARIABLES OF BROILERS
ABSTRACT Due to a number of factors involving the thermal environment of a broiler cutting installation and its interaction with the physiological and productive responses of birds, artificial intelligence has been shown to be an interesting methodology to assist in the decision-making process. For this reason, the main aim of this work was to develop an art
Eng. Agríc.. Publicado em: 2020-02
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21. A NEW COMPUTATIONAL FLUID DYNAMICS STUDY OF A LIQUID-LIQUID HYDROCYCLONE IN THE TWO PHASE CASE FOR SEPARATION OF OIL DROPLETS AND WATER
Abstract A liquid-liquid hydrocyclone, so-called de-oiling equipment, was investigated for separation of oil droplets and wastewater in an Iranian petroleum unit. To generate the geometry and the mesh of the hydrocyclone Gambit software was used. Afterwards, by taking advantage of the Euler-Lagrangian principle, the governing equations covering the motion of
Braz. J. Chem. Eng.. Publicado em: 13/01/2020
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22. Artificial neural network for determining the hedonic score of texture of and distinguishing different grades of ham sausages
Abstract The preference of consumers of ham sausages is mainly determined on its texture. A method of determining hedonic score of texture and distinguishing different grades of ham sausages based on artificial neural network was established in this study. The topological texture of the artificial neural network was developed on the basis of analyzing the ha
Food Sci. Technol. Publicado em: 13/12/2019
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23. Letter-name knowledge: Predicting reading and writing diffi culties
Resumo Este estudo avaliou a precisão de classificação do risco de dificuldade de leitura e escrita, a partir do conhecimento do nome das letras. Foram aplicadas duas versões da tarefa de reconhecimento das letras, a primeira com todas as 26 letras do alfabeto, e a outra com apenas 15. A tarefa foi aplicada a 213 crianças brasileiras, matriculadas no �
Estud. psicol. (Campinas). Publicado em: 02/12/2019
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24. 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