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	Provedor de dados Rev. Bras. Ciênc. Solo (3) ArchiMer (2) REA (2) Agronomy (1) AgEcon (1) BABT (1) BJMBR (1) Scientia Agricola (1) Genet. Mol. Biol. (1) Rev. Bras. Ciênc. Avic. (1) AGRIAMBI (1) Mais... Autor Albuquerque,Jackson Adriano (1) Alves,Marcelo Eduardo (1) Barroso,Emílio Velloso (1) Barrón, Cristina (1) Bjorklund, Kr (1) Bolzan,AC (1) Borges, Alberto V. (1) Bortolini,Diego (1) Braga,Antônio de Pádua (1) Brandão Júnior,Augusto Ferreira (1) Bruning,Jhosefe (1) Cagliari,Joice (1) Calderano Filho,Braz (1) Calderano,Sebastião Barreiros (1) Cambraia,Mario Sergio (1) Campos,Alcinei Ribeiro (1) Carbonera,Roberto (1) Carvalho Júnior,Waldir de (1) Chagas,César da Silva (1) Coblinski,João Augusto (1) Mais... Palavra-chave Artificial neural networks (15) Pedotransfer functions (2) Artificial incubation (1) Available water (1) Avena sativa (1) Bioinformatics (1) C14 (1) C21 (1) Classification (1) Clustering (1) Coastal ecosystems (1) Computer Science and Engineering (1) Computer Vision (1) Demand analysis (1) Demand and Price Analysis (1) Digital image processing (1) Digital mapping (1) Domain (1) Elasticity (1) Feature extraction (1) Mais... Tipo do documento Info:eu-repo/semantics/article (11) Text (2) Conference Paper or Presentation (1) Info:eu-repo/semantics/other (1) Ano 2021 (1) 2019 (1) 2018 (4) 2016 (1) 2014 (1) 2011 (1) 2009 (1) 2008 (1) 2007 (1) 2005 (2) 2004 (1) Mais... País Brazil (12) France (2) United States (1) Idioma Inglês (15)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 15 Primeira ... 1 ... Última Analysing Wine Demand With Artificial Neural Networks AgEcon Gerolimetto, M.; Mauracher, Christine; Procidano, I.. In this paper we analyse wine demand in Italy using microdata. Instead of estimating a traditional parametric model (like AIDS) we employed artificial neural networks (ANN) and evaluate the elasticities using two different methods, specific for the non parametric framework. We compared the performances of the two methods to estimate elasticities and put in evidence the relevance of some demographic variables together with the usual economic ones, explaining the consumer's behaviour. Tipo: Conference Paper or Presentation Palavras-chave: Artificial neural networks; Demand analysis; Wine; Elasticity; Demand and Price Analysis; C14; C21; Q11; Q13. Ano: 2005 URL: http://purl.umn.edu/24753 Artificial intelligence in seeding density optimization and yield simulation for oat AGRIAMBI Dornelles,Eldair F.; Kraisig,Adriana R.; Silva,José A. G. da; Sawicki,Sandro; Roos-Frantz,Fabricia; Carbonera,Roberto. ABSTRACT Artificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m-2) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were... Tipo: Info:eu-repo/semantics/article Palavras-chave: Avena sativa; Artificial neural networks; Genetic algorithms; Innovation. Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000300183 Artificial neural network analysis of factors controlling ecosystem metabolism in coastal systems ArchiMer Rochelle-newall, Emma. J.; Winter, Christian; Barrón, Cristina; Borges, Alberto V.; Duarte, Carlos M.; Elliott, Mike; Frankignoulle, Michel; Gazeau, Frederic; Middelburg, Jack J.; Pizay, Marie-dominique; Gattuso, Jean-pierre. Knowing the metabolic balance of an ecosystem is of utmost importance in determining whether the system is a net source or net sink of carbon dioxide to the atmosphere. However, obtaining these estimates often demands significant amounts of time and manpower. Here we present a simplified way to obtain an estimation of ecosystem metabolism. We used artificial neural networks (ANNs) to develop a mathematical model of the gross primary production to community respiration ratio (GPP:CR) based on input variables derived from three widely contrasting European coastal ecosystems (Scheldt Estuary, Randers Fjord, and Bay of Palma). Although very large gradients of nutrient concentration, light penetration, and organic-matter concentration exist across the sites,... Tipo: Text Palavras-chave: Artificial neural networks; Coastal ecosystems; Metabolic balance; Primary production; Respiration. Ano: 2007 URL: https://archimer.ifremer.fr/doc/00247/35857/34378.pdf Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹ Rev. Bras. Ciênc. Solo Calderano Filho,Braz; Polivanov,Helena; Chagas,César da Silva; Carvalho Júnior,Waldir de; Barroso,Emílio Velloso; Guerra,Antônio José Teixeira; Calderano,Sebastião Barreiros. Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS... Tipo: Info:eu-repo/semantics/other Palavras-chave: Artificial neural networks; Terrain attributes; Digital mapping. Ano: 2014 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832014000600003 Clustering and artificial neural networks: classification of variable lengths of Helminth antigens in set of domains Genet. Mol. Biol. Rodrigues,Thiago de Souza; Pacífico,Lucila Grossi Gonçalves; Teixeira,Santuza Maria Ribeiro; Oliveira,Sérgio Costa; Braga,Antônio de Pádua. A new scheme for representing proteins of different lengths in number of amino acids that can be presented to a fixed number of inputs Artificial Neural Networks (ANNs) speel-out classification is described. K-Means's clustering of the new vectors with subsequent classification was then possible with the dimension reduction technique Principal Component Analysis applied previously. The new representation scheme was applied to a set of 112 antigens sequences from several parasitic helminths, selected in the National Center for Biotechnology Information and classified into fourth different groups. This bioinformatic tool permitted the establishment of a good correlation with domains that are already well characterized, regardless of the differences between... Tipo: Info:eu-repo/semantics/article Palavras-chave: Bioinformatics; Artificial neural networks; Clustering; Helminth antigen; Domain. Ano: 2004 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572004000400032 Digital soil class mapping in Brazil: a systematic review Scientia Agricola Coelho,Fabrício Fernandes; Giasson,Elvio; Campos,Alcinei Ribeiro; Tiecher,Tales; Costa,José Janderson Ferreira; Coblinski,João Augusto. ABSTRACT: In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for... Tipo: Info:eu-repo/semantics/article Palavras-chave: Pedology; Mapping unit density; Artificial neural networks; Soil-forming factors; Overall accuracy. Ano: 2021 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000501401 Egg hatchability prediction by multiple linear regression and artificial neural networks Rev. Bras. Ciênc. Avic. Bolzan,AC; Machado,RAF; Piaia,JCZ. An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction. Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial incubation; Artificial neural networks; Hatchability; Multiple linear regression. Ano: 2008 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2008000200004 Estimation of the Retention and Availability of Water in Soils of the State of Santa Catarina Rev. Bras. Ciênc. Solo Bortolini,Diego; Albuquerque,Jackson Adriano. ABSTRACT: Soil water retention and availability are important properties for agricultural production, which can be measured directly or estimated by pedotransfer functions. Some studies on this topic were carried out in Santa Catarina, Brazil. To improve the estimates, it is necessary to evaluate other properties, to analyze more soil types, as well as to use other analysis techniques such as artificial neural networks and regression trees. Thus, the objective of the study was to estimate the field capacity (FC), permanent wilting point (PWP), and available water (AW) in soils of Santa Catarina (SC), through multiple linear regressions (MLR), artificial neural networks (ANN), and regression trees (RT), more efficiently than the current pedotransfer... Tipo: Info:eu-repo/semantics/article Palavras-chave: Pedotransfer functions; Water retention curve; Artificial neural networks; Regression trees; Multiple linear regressions. Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100424 Late Pleistocene-Holocene radiolarian paleotemperatures in the Norwegian Sea based on artificial neural networks ArchiMer Cortese, G; Dolven, Jk; Bjorklund, Kr; Malmgren, Ba. Artificial Neural Networks (ANN) were trained by using an extensive radiolarian census dataset from the Nordic (Greenland, Norwegian, and Iceland) Seas. The regressions between observed and predicted Summer Sea Temperature (SST) indicate that lower error margins and better correlation coefficients are obtained for 100 m (SST100) compared to 10 m (SST10) water depth, and by using a subset of species instead of all species. The trained ANNs were subsequently applied to radiolarian data from two Norwegian Sea cores, HM 79-4 and MD95-2011, for reconstructions of SSTs through the last 15,000 years. The reconstructed SST is quite high during the Bolling-Allerod, when it reaches values only found later during the warmest phase of the Holocene. The climatic... Tipo: Text Palavras-chave: Artificial neural networks; Radiolarians; Nordic seas; Late Pleistocene; Holocene. Ano: 2005 URL: https://archimer.ifremer.fr/doc/00229/34074/32535.pdf Method for automatic detection of wheezing in lung sounds BJMBR Riella,R.J.; Nohama,P.; Maia,J.M.. The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed... Tipo: Info:eu-repo/semantics/article Palavras-chave: Wheezes; Lung sounds; Spectrogram; Digital image processing; Artificial neural networks. Ano: 2009 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009000700013 PEDOFUNCTIONS APPLIED TO THE LEAST LIMITING WATER RANGE TO ESTIMATE SOIL WATER CONTENT AT SPECIFIC POTENTIALS REA Tavanti,Renan F. R.; Freddi,Onã da S.; Tavanti,Tauan R.; Rigotti,Adriel; Magalhães,Wellington de A.. 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 the literature and artificial neural networks on the accuracy in predicting soil water retention at potentials equivalent to field capacity (θFC) and permanent wilting point (θPWP). In other words, to apply the best models to LLWR of two soil types (Oxisol and Ultisol) and verify changes in their structure. The results indicated that pedofunctions using sand, silt, clay, bulk density, and soil organic... Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil physics; Soil physical quality indicator; Available water; Pedotransfer functions; Artificial neural networks. Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000400444 Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks Agronomy Ganganagowdar, Narendra Veranagouda; Siddaramappa, Hareesha Katiganere.  A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels.  Tipo: Info:eu-repo/semantics/article Palavras-chave: Computer Science and Engineering; Computer Vision; Image Processing; Soft Computing White Wholes (WW) grade cashew kernel images; Feature extraction; Artificial neural networks; Classification. Ano: 2016 URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861 Remaining phosphorus estimated by pedotransfer function Rev. Bras. Ciênc. Solo Cagliari,Joice; Veronez,Maurício Roberto; Alves,Marcelo Eduardo. Although the determination of remaining phosphorus (Prem) is simple, accurate values could also be estimated with a pedotransfer function (PTF) aiming at the additional use of soil analysis data and/or Prem replacement by an even simpler determination. The purpose of this paper was to develop a pedotransfer function to estimate Prem values of soils of the State of São Paulo based on properties with easier or routine laboratory determination. A pedotransfer function was developed by artificial neural networks (ANN) from a database of Prem values, pH values measured in 1 mol L-1 NaF solution (pH NaF) and soil chemical and physical properties of samples collected during soil classification activities carried out in the State of São Paulo by the Agronomic... Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural networks; Modeling; Multiple regression analysis. Ano: 2011 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832011000100019 Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features BABT Cambraia,Mario Sergio; Brandão Júnior,Augusto Ferreira; Rosa,Luiz Henrique Leite. ABSTRACT This work presents the methodology, development and testing of an autonomous system, based on Artificial Neural Networks (ANN), for the reduction of technical losses in reticulated underground systems through the optimal control of the capacitor banks (CBs) present in the grid. The proposed methodology includes Smart Grid features, including practical solutions for current transformers positioning in underground networks, collecting field measurements for the Distribution Operation Centre (DOC) and real-time control of field equipment (capacitors banks). The steps of the proposed methodology and the main aspects of the development of the system are also described, as well as the tests performed to prove the results and validate the system. Tipo: Info:eu-repo/semantics/article Palavras-chave: Smart grids; Artificial neural networks; Underground reticulated networks; Technical losses. Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200205 THE USE OF ARTIFICIAL INTELLIGENCE FOR ESTIMATING SOIL RESISTANCE TO PENETRATION REA Pereira,Tonismar dos S.; Robaina,Adroaldo D.; Peiter,Marcia X.; Torres,Rogerio R.; Bruning,Jhosefe. ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were evaluated. The models generated from the ANNs were implemented through the multilayer perceptron with backpropagation algorithm of Matlab software, varying the number of neurons in the input and intermediate layers. For the procedure from SVM, the RapidMiner software was used, varying input variables, the kernel function and the coefficients of these... Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil compaction; Machine learning; Support vector machines; Artificial neural networks. Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100142 Registros recuperados: 15 Primeira ... 1 ... Última

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