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## Comparison of Artificial Neural Network and Regression Models for Prediction of Body Weight in Raini Cashmere Goat | ||

Iranian Journal of Applied Animal Science | ||

مقاله 10، دوره 9، شماره 3، آذر 2019، صفحه 453-461
اصل مقاله (594.6 K)
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نوع مقاله: Research Articles | ||

نویسندگان | ||

M. Khorshidi-Jalali^{1}؛ M.R. Mohammadabadi ^{} ^{1}؛ A. Esmailizadeh^{1}؛ A. Barazandeh^{2}؛ O.I. Babenko^{3}
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^{1}Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran | ||

^{2}Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran | ||

^{3}Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine | ||

چکیده | ||

The artificial neural networks (ANN) are the learning algorithms and mathematical models, which mimic the information processing ability of human brain and can be used to non linear and complex data. The aim of this study was to compare artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. The data of 1389 goats for body weight, height at withers (HAW), body length (BL) and chest girth (CG) were used. Different regression models with all fixed factors were calculated for the most possible states and with different degrees and two artificial neural networks with different hidden layers, learning functions and transform functions were used. Finally, Multilayer perceptron model with one hidden layer along with neurons was selected and used. Correlation between body weight and its measurements showed that it is possible to use body measurements for prediction of body weight though prediction of body weight can be improved when more measurements are used. Based on R^{2} andmean square error (MSE) parameters, the best fitted regression equation for prediction of body weight using body measurements was selected. While all three measurements had a significant effect in the model (P<0.0001), height at wither had the highest correlation coefficient (0.65), hence may have the greatest effect on prediction. Comparing two models indicated that both models can predict body weight well and near to actual body weight, but the capability of artificial neural network model is higher (R2=0.86 for ANN and 0.76 for multiple regression analysis (MRA)) and closer to actual body weight. However, if more related measurements are recorded, ANN can give the desirable results. Therefore, it is possible to apply artificial neural networks, instead of customary procedures for prediction of actual body weight using body measurements. | ||

کلیدواژهها | ||

Artificial Neural Networks؛ body measurements؛ linear models؛ Raini goat | ||

اصل مقاله | ||

Goat farming is practiced worldwide, with goat products having a favorable image. The number of goats has increased globally, even in countries with high and intermediate incomes, despite major changes in agriculture due to industrial mergers, globalization, and technological advances in developed countries (Shamsalddini
Measuring live body weight exactly in village situations is so hard, because in villages and mountains where these animals are kept there is no transferable weighing balance and skilled technicians. These conditions are real for nomads of Kerman province who keep the Raini Cashmere goat breed. Hence, in these situations, the weight of the animals is predicted via regressing body weight on different body measurements as CG, BL, HAW and so on. These can be measured without hesitation. Therefore, in this study, the data of 1389 Raini Cashmere goat which were recorded during 2010-2011 were obtained from the Breeding Station of Raini goat in Baft city (middle of Kerman Province, Iran) (n=701), from rural flocks in Kerman province (n=619) and from Livestock research center of Shahid Bahonar University of Kerman, Iran (n=69) (Table 1).
CV: coefficient of variation.
The obtained data were edited firstly and the outliers and the illogical data were removed from the dataset. The microsoft excel and neuro solution (http://www.neurosolutions.com) software were used to normalize standardize the data. In order to achieve the best model of body weight prediction using mentioned phenotypic traits the multiple linear regression models were applied and comparison of R² and MSE was conducted in R environment (https://cran.r-project.org). In this study, different regression models with all fixed factors were calculated for the most possible states and with different degrees. Then, the data that were analyzed and investigated in the previous steps using regression models were transferred to neuro solution software and then the neural network was designed. In the present study, two artificial neural networks; multilayer perceptron and generalized feed forward with different hidden layers, learning functions, and transform functions were used. And then, the best network was selected. Finally, multilayer perceptron model with one hidden layer along with neurons was selected and used. Figure 1 shows input layers, neurons, and output layer included the variables for producing the network response. MSE and R
Correlation between body weight and its measurements showed that it is possible to use body measurements for prediction of body weight (Table 2). The highest and the lowest correlations were between body weight and HAW (0.75) and body length (0.45), respectively. Among the body measurements, the highest and the lowest correlations were obtained between HAW and chest girth (0.60) and between body length and HAW (0.35), respectively. In the other investigations, the same results have been reported by other researchers (Afolayan
Comparison of models using R BW= -40.74 + 0.23 BL + 0.75 HAW + 0.41 CG The MSE and R² for this equation were 47.20 and 0.67, respectively. These observations indicate that body weight of Raini Cashmere goat can be predicted with relatively high accuracy HAW, BL and CG. All three measurements had a significant effect in the model (P<0.0001). The HAW had the most coefficient (0.65 and 0.75, unstandardized and standardized coefficients respectively), hence may have the greatest effect on prediction. Figure 2 shows correlation between actual and predicted weight using the best multiple regression model. As can be seen, this relationship is along a line representing the ability of this model for prediction of body weight. Figures 3 and 4 suggest that changes of variables HAW, BL and CG are very similar to variations related to actual measured body weight and indicate the accuracy and precision of prediction by multiple linear regression models and also the necessity of these three variables in the model. In the other investigations, the same results have been reported by other researchers (Rani
To prevent over-fitting of the artificial neural network, 70% of the data were used as training set, 15% as testing set and 15% as the validating set.
The neural network models were trained using the training data sets to predict the body weight and a maximum goal of 99% accuracy was set to be achieved in 2000 epochs (cycles).
Then, prediction of body weight using different training functions was performed. Correlation coefficient (R
RMSE: root mean square error.
Raja
As has shown in Figure 8, both artificial neural network model and multiple regression model can predict body weight well and near to actual body weight, but capability of artificial neural network model in comparison of multiple regression model is higher and closer to actual body weight. Results showed that R
Roush
In the other study, Bahreini Behzadi and Aslaminejad (2010) used 6 nonlinear regression forms of von Bertalanffy, Gompertz, Logistic (with 3 and 4 parameters), Brody and Richards and artificial neural network to predict Baluchi sheep growth and concluded that artificial neural network generates a slightly better descriptive sheep growth curve in comparison with nonlinear models and makes the most accurate prediction. They proposed that artificial neural network is a valuable tool for prediction of lamb body weight. Neural network models also have been used for detecting mastitis (Hassan
Our results demonstrated that for prediction of body weight in Raini Cashmere goat artificial neural networks are better and more accurate than multiple regression models due to the higher R
We would like to thank the Vice Chancellor for Research and Technology of Shahid Bahonar University of Kerman for financial support to perform this research. | ||

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