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	Provedor de dados Electron. J. Biotechnol. (2) CIGR Journal (1) Colegio de Postgraduados (1) Autor Amini-Pozveh, Samaneh (1) Chen,Fudi (1) Ge,Huihua (1) Ghanbarian, Davoud (1) Ghasemi-Varnamkhasti, Mahdi (1) Ghosh, Satyabrata (1) Hou,Shixia (1) Izadi, Zahra (1) Li,Hao (1) Mireei, Seyed Ahmad (1) Mishra, Puneet (1) Rivera Castillo, Enrique (1) Xu,Zhihan (1) Yang,Dazuo (1) Zhang,Guangya (1) Mais... Palavra-chave Support vector machine (4) Amino acid composition (1) Artificial neural network (1) Computer aided classification (1) Estadística (1) Evapotranspiración (1) Evapotranspiration (1) Fed-batch fermentation (1) Food and olive oil Olive oil industry (1) Forecasting (1) General regression neural network (1) Iturin A (1) Maestría (1) Máquina de soporte vectorial (1) Neural network (1) Optimum temperature (1) Predicción (1) Red neuronal (1) Spectroscopy (1) Uniform design (1) Mais... Tipo do documento Journal article (2) Info:eu-repo/semantics/article (1) Ano 2018 (1) 2015 (1) 2012 (2) País Chile (2) China (1) Mexico (1) Idioma Inglês (3) Espanhol (1)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 4 Primeira ... 1 ... Última Utilizing visible and near infrared spectroscopy based on multi-class support vector machines classification to characterize olive oil adulteration CIGR Journal Ghasemi-Varnamkhasti, Mahdi; Amini-Pozveh, Samaneh; Mireei, Seyed Ahmad; Mishra, Puneet; Ghosh, Satyabrata; Ghanbarian, Davoud; Izadi, Zahra. Rapid and non-destructive adulteration detection is of particular importance to oil industries. This paper presents an application of visible and near-infrared spectroscopy (VNIR) for detection of adulteration levels in olive oil. Sunflower oil was used as an adulterant to olive oil and adulteration samples with different levels ranging from 0 to 40% were prepared and used for the experiments. The spectra were first considered in the range of 500-900 nm and then smoothened and normalized to reduce the light scattering effects. Principal component analysis (PCA) was performed on the spectra to have a primary data visualization and feature extraction. The extracted PCA scores were used to calculate the Mahalanobis distances of the adulterated samples from... Tipo: Info:eu-repo/semantics/article Palavras-chave: Food and olive oil Olive oil industry; Support vector machine; Computer aided classification; Spectroscopy. Ano: 2018 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/4681 Máquinas de soporte vectorial en el análisis de series de tiempo. Colegio de Postgraduados Rivera Castillo, Enrique. La evapotranspiración de referencia (ETo) es un proceso no lineal empleado para determinar la cantidad de agua utilizada en los programas de irrigación. El nivel de precisión de esta variable a partir de datos históricos, ha sido siempre fundamental. En este trabajo, se presenta una aplicación de las Máquinas de Soporte Vectorial (SVMs) para la predicción de ETo y se compara su capacidad predictiva con otras dos metodologías de predicción: Redes Neuronales Artificiales de Multicapa (MLP) y modelos Autoregresivos Integrados de Promedio Móvil (ARIMA). Se propone un algoritmo heurístico de refinamiento para la implementación de las SVM resultando en una predicción mucho mejor que la obtenida con los otros dos métodos. La capacidad de predicción fue evaluada... Palavras-chave: Evapotranspiración; Red neuronal; Predicción; Máquina de soporte vectorial; Evapotranspiration; Neural network; Forecasting; Support vector machine; Estadística; Maestría. Ano: 2012 URL: http://hdl.handle.net/10521/1693 User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine Electron. J. Biotechnol. Chen,Fudi; Li,Hao; Xu,Zhihan; Hou,Shixia; Yang,Dazuo. Background In the field of microbial fermentation technology, how to optimize the fermentation conditions is of great crucial for practical applications. Here, we use artificial neural networks (ANNs) and support vector machine (SVM) to offer a series of effective optimization methods for the production of iturin A. The concentration levels of asparagine (Asn), glutamic acid (Glu) and proline (Pro) (mg/L) were set as independent variables, while the iturin A titer (U/mL) was set as dependent variable. General regression neural network (GRNN), multilayer feed-forward neural networks (MLFNs) and the SVM were developed. Comparisons were made among different ANNs and the SVM. Results The GRNN has the lowest RMS error (457.88) and the shortest training time (1... Tipo: Journal article Palavras-chave: Artificial neural network; Fed-batch fermentation; General regression neural network; Iturin A; Support vector machine. Ano: 2015 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000400003 Prediction of xylanase optimal temperature by support vector regression Electron. J. Biotechnol. Zhang,Guangya; Ge,Huihua. Background: Support vector machine (SVM), a novel powerful machine learning technology, was used to develop the non-linear quantitative structure-property relationship (QSPR) model of the G/11 xylanase based on the amino acid composition. The uniform design (UD) method was applied to optimize the running parameters of SVM for the first time. Results: Results showed that the predicted optimum temperature of leave-one-out (LOO) cross-validation fitted the experimental optimum temperature very well, when the running parameter C, ξ, and γ was 50, 0.001 and 1.5, respectively. The average root-mean-square errors (RMSE) of the LOO cross-validation were 9.53ºC, while the RMSE of the back propagation neural network (BPNN), was 11.55ºC. The... Tipo: Journal article Palavras-chave: Amino acid composition; Optimum temperature; Support vector machine; Uniform design; Xylanase. Ano: 2012 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582012000100007 Registros recuperados: 4 Primeira ... 1 ... Última

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