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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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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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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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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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