A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal

Perai, A. H.; Moghaddam, Hassan Nassiri; Asadpour, Saeid; Bahrampour, Jamil; Mansoori, Gh

doi:10.3382/ps.2010-00639

Cited by 39 publications

(36 citation statements)

References 12 publications

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“…The values for the m.s.e. are into the interval presented by Perai et al (2010) Table 3. However, they used a specific protein feedstuff.…”

Section: Resultsmentioning

confidence: 99%

“…Another group with seven data lines (three FM and four POM) were used as validation set of the ANN. Perai et al (2010) used 34 raw lines and only one feedstuff (meat and bone meal-MBM). Although Ahmadi et al (2008) and Perai et al (2010) used a small dataset, their prediction was accurate.…”

Section: Resultsmentioning

confidence: 99%

“…Perai et al (2010) used 34 raw lines and only one feedstuff (meat and bone meal-MBM). Although Ahmadi et al (2008) and Perai et al (2010) used a small dataset, their prediction was accurate. This most likely occurred because they used specific feedstuffs.…”

Section: Resultsmentioning

confidence: 99%

“…The goodness of fit of the model and the accuracy of the predicted AMEn were evaluated using the training and testing data. The measures used in this process were as follows: Coefficient of determination (R 2 ), m.s.e., mean absolute deviation (MAD), mean absolute percentage error (MAPE) and bias, as defined in Bolzan et al (2008) and Perai et al (2010).…”

Section: Model Developmentmentioning

confidence: 99%

“…Okut et al (2011) predicted body mass index using a regularised neural network. ANN has also been used to predict ME (Ahmadi et al, 2007 andPerai et al, 2010). The multilayer perceptron neural network (MLP) has been successfully applied to predict the true metabolisable energy (TMEn) values of meat and bone meal samples (Perai et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Mariano

Paixão

Lima

et al. 2013

Animal

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Several researchers have developed prediction equations to estimate the metabolisable energy (ME) of energetic and protein concentrate feedstuffs used in diets for broilers. The ME is estimated by considering CP, ether extract, ash and fibre contents. However, the results obtained using traditional regression analysis methods have been inconsistent and new techniques can be used to obtain better estimate of the feedstuffs' energy value. The objective of this paper was to implement a multilayer perceptron network to estimate the nitrogen-corrected metabolisable energy (AMEn) values of the energetic and protein concentrate feeds, generally used by the poultry feed industry. The concentrate feeds were from plant origin. The dataset contains 568 experimental results, all from Brazil. This dataset was separated into two parts: one part with 454 data, which was used to train, and the other one with 114 data, which was used to evaluate the accuracy of each implemented network. The accuracy of the models was evaluated on the basis of their values of mean squared error, R 2 , mean absolute deviation, mean absolute percentage error and bias. The 7-5-3-1 model presented the highest accuracy of prediction. It was developed an Excel R AMEn calculator by using the best model, which provides a rapid and efficient way to predict the AMEn values of concentrate feedstuffs for broilers.Keywords: avian production, broilers, metabolisable energy, multilayer perceptron ImplicationsIt is difficult and expensive to estimate the nitrogencorrected metabolisable energy (AMEn) values of the feedstuffs used for broilers. The results of this study demonstrate that the implementation of multilayer perceptron networks (MLP) in a meta-analysis is suitable to estimate these energy values. Furthermore, a calculator was created on the basis of the results of the MLP, which allows an efficient way of predicting the AMEn values.

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“…The values for the m.s.e. are into the interval presented by Perai et al (2010) Table 3. However, they used a specific protein feedstuff.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Mariano

Paixão

Lima

et al. 2013

Animal

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Artificial neural networks applied to the classification of hair samples according to pigment and sex using non‐invasive analytical techniques

et al. 2020

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In this study, we investigated the possibility of using an artificial neural network (ANN) to classify human hair samples according to pigment (original or bleached hair) and sex (female or male) from numerical data obtained by wavelength dispersive X-ray fluorescence (WDXRF) and by laser-induced breakdown spectroscopy (LIBS). The results were promising, showing that the developed ANNs are able to classify the pigment and donor sex of hair samples with 100% and 89.5% accuracy, respectively, in the test set using WDXRF data. For the LIBS data in the test set, 100% of the pigment classifications were correct, and 78.9% of the donor sex classifications were correct.

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Neural network committee to predict the AMEn of poultry feedstuffs

Mariano

Lima

Alvarenga

et al. 2014

Neural Comput & Applic

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A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal

Cited by 39 publications

References 12 publications

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Artificial neural networks applied to the classification of hair samples according to pigment and sex using non‐invasive analytical techniques

Neural network committee to predict the AMEn of poultry feedstuffs

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