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An artificial neural network model for prediction of quality characteristics of apples during convective dehydration Ciênc. Tecnol. Aliment.
Scala,Karina Di; Meschino,Gustavo; Vega-gálvez,Antonio; Lemus-mondaca,Roberto; Roura,Sara; Mascheroni,Rodolfo.
In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural networks; Quality attributes; Genetic algorithm; Process optimization; Dried apple.
Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000300004
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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
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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
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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
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Artificial neural networks modeling of kinetic curves of celeriac (Apium graveolens L.) in vacuum drying Ciênc. Tecnol. Aliment.
BEIGI,Mohsen; AHMADI,Iman.
Abstract The objective of this study was to predict celeriac drying curves using artificial neural networks (ANNs). The experimental data for vacuum drying kinetics of celeriac slices reported by other researcher in the previously published article was used. The air temperature, chamber pressure and time values were used as ANN inputs. To predict the moisture content, the multilayer feed forward back propagation neural network, as a well-known network, was used. The network with Levenberg-Marquardt learning algorithm, hyperbolic tangent sigmoid transfer function, and 3-6-9-1 topology provided the superior results.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial neural networks; Celeriac; Drying kinetics; Multilayer feed forward back propagation.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612019000500035
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Chemometric classification of several olive cultivars from Trás-os-Montes region (Northeast of Portugal) using artificial neural networks. IPB - Escola Superior Agrária
Peres, António M.; Baptista, Paula; Malheiro, R.; Dias, L.G.; Bento, Albino; Pereira, J.A..
This work aimed to use artificial neural networks for fruit classification according to olive cultivar, as a tool to guarantee varietal authenticity. So, 70 samples, each one containing, in general, 40 olives, belonging to the six most representative olive cultivars of Trás-os-Montes region (Cobrançosa, Cordovil, Madural, Negrinha de Freixo, Santulhana and Verdeal Transmontana) were collected in different groves and during four crop years. Five quantitative morphological parameters were evaluated for each fruit and endocarp, respectively. In total, ten biometrical parameters were used together with a multilayer perceptron artificial neural network allowing the implementation of a classification model. Its performance was compared with that obtained using...
Tipo: Article Palavras-chave: Olea europaea L.; Artificial neural networks; Linear discriminant analysis; Authenticity; Cultivars; Protected designation of origin.
Ano: 2011 URL: http://hdl.handle.net/10198/3125
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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
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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
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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
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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
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Modeling the water uptake by chicken carcasses during cooling by immersion Ciênc. Tecnol. Aliment.
Martins,Tiago Dias; Klassen,Túlio; Canevesi,Rafael Luan Sehn; Barella,Rodrigo Augusto; Cardozo Filho,Lucio; Silva,Edson Antonio da.
In this study, water uptake by poultry carcasses during cooling by water immersion was modeled using artificial neural networks. Data from twenty-five independent variables and the final mass of the carcass were collected in an industrial plant to train and validate the model. Different network structures with one hidden layer were tested, and the Downhill Simplex method was used to optimize the synaptic weights. In order to accelerate the optimization calculus, Principal Component Analysis (PCA) was used to preprocess the input data. The obtained results were: i) PCA reduced the number of input variables from twenty-five to ten; ii) the neural network structure 4-6-1 was the one with the best result; iii) PCA gave the following order of importance:...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Chillers; Artificial neural networks; Water retention.
Ano: 2011 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612011000300004
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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
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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
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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
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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
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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
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UV spectrophotometry method for the monitoring of galacto-oligosaccharides production IPB - Escola Superior Agrária
Dias, L.G.; Veloso, Ana C.A.; Correia, Daniela M.; Rocha, Orlando; Torres, Duarte; Rocha, Isabel; Rodrigues, Lígia R.; Peres, António M..
Monitoring the industrial production of galacto-oligosaccharides (GOS) requires a fast and accurate methodology able to quantify, in real time, the substrate level and the product yield. In this work, a simple, fast and inexpensive UV spectrophotometric method, together with partial least squares regression (PLS) and artificial neural networks (ANN), was applied to simultaneously estimate the products (GOS) and the substrate (lactose) concentrations in fermentation samples. The selected multiple models were trained and their prediction abilities evaluated by cross-validation and external validation being the results obtained compared with HPLC measurements. ANN models, generated from absorbance spectra data of the fermentation samples, gave, in general,...
Tipo: Article Palavras-chave: Fermentation processes; Galacto-oligosaccharides; UV spectrophotometer; Partial least squares regression; Artificial neural networks.
Ano: 2009 URL: http://hdl.handle.net/10198/1028
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