Comparison of Artificial Neural Network and Regression Models for Prediction of Body Weight in Raini Cashmere Goat
محورهای موضوعی : Camel
م. خورشیدی-جلالی
1
(Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran)
م.ر. محمدآبادی
2
(Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran)
ع. اسمعیلی‌زاده
3
(Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran)
ا. برازنده
4
(Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran)
ُ.ا. بابنکو
5
(Department of Animal Science, Bila Tserkva National Agrarian University, Bila Tserkva, Ukraine)
کلید واژه: Artificial Neural Networks, body measurements, linear models, Raini goat,
چکیده مقاله :
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 R2 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.
شبکه­های عصبی مصنوعی الگوریتم­های آموزشی و مدل­های ریاضی هستند که توانایی تقلید از مغز انسان در پردازش اطلاعات را دارند و می­توانند داده­های پیچیده و غیر خطی را مورد استفاده قرار دهند. هدف این پژوهش مقایسه شبکه عصبی مصنوعی و مدل­های رگرسیونی برای پیش‌بینی وزن بدن در بز کرکی راینی بود. داده­های 1389 بز برای وزن بدن، ارتفاع جدوگاه، طول بدن و قفسه سینه مورد استفاده قرار گرفت. مدل­های رگرسیونی مختلف با تمام فاکتورهای ثابت برای بیشتر حالت­های ممکن و با درجه­های مختلف محاسبه شدند و دو شبکه عصبی مصنوعی با لایه­های مخفی متفاوت، توابع آموزش و توابع انتقال گوناگون استفاده شدند. در نهایت، مدل پرسپترون چند لایه با یک لایه مخفی به همراه نرون­ها انتخاب و استفاده شد. همبستگی بین وزن بدن و اندازه‌گیری­هایش نشان داد که می­توان از اندازه­های بدن برای پیش‌بینی وزن بدن استفاده کرد و هرچه اندازه­های بیشتری استفاده شوند پیش‌بینی دقیق­تری انجام خواهد شد. براساس پارامترهای R2و MSE، بهترین معادله رگرسیون فیت شده برای پیش‌بینی وزن بدن با استفاده از اندازه‌گیری­های ابعاد بدن انتخاب شد. در حالیکه هر سه اندازه در مدل اثر معنی‌داری داشتند (0001/0P<)، ارتفاع جدوگاه بالاترین ضریب را داشت (65/0)، بنابراین می­تواند بیشترین اثر را در پیش‌بینی داشته باشد. مقایسه دو مدل نشان داد که هر دو مدل می­توانند به خوبی وزن بدن را، نزدیک به وزن واقعی آن پیش‌بینی کنند، اما توانایی شبکه عصبی مصنوعی بالاتر است (R2 برای شبکه عصبی مصنوعی 86/0 و برای مدل­های رگرسیونی 76/0) و به ورن واقعی بدن نزدیک­تر می­باشد. با این وجود، اگر اندازه­های مرتبط بیشتری رکورد‌برداری شوند می­توان نتایج مطلوب­تری را با شبکه عصبی مصنوعی به دست آورد. بنابراین، از شبکه عصبی مصنوعی می­توان به جای روش­های سنتی مرسوم برای پیش‌بینی وزن واقعی بدن با استفاده از اندازه­های بدن استفاده کرد.
Afolayan R.A., Adeyinka I.A. and Lakpini C.A.M. (2006). The estimation of live weight from body measurements in Yankasa sheep. Czech J. Anim. Sci. 51, 343-348.
Akkol S., Akilli A. and Cemal I. (2017). Comparison of artificial neural network and multiple linear regression for prediction of live weight in hair goats. Yyu J. Agric. Sci. 27, 21-29.
Alade N.K., Raji A.O. and Atiku M.A. (2008). Determination of appropriate model for the estimation of body weight in goats. J. Agric. Biol. Sci. 3, 52-57.
Askari N., Baghizadeh A. and Mohamadabadi M.R. (2008). Analysis of the genetic structure of iranian indigenous Raeni cashmere goat populations using microsatellite markers. Indian J. Biotechnol. 2, 142-145.
Askari N., Mohammadabadi M.R. and Baghizadeh A. (2010). Genetic diversity in four populations of Raeini cashmere goat using ISSR markers. Modern Genet. J. 5, 49-56.
Askari N., Mohammadabadi M.R. and Baghizadeh A. (2011). ISSR markers for assessing DNA polymorphism and genetic characterization of cattle, goat and sheep populations. Iranian J. Biotechnol. 9, 222-229.
Askari N., Mohammadabadi M.R., Beygi Nasiri M.T., Baghizadeh A. and Fayazi J. (2009). Study of genetic diversity of Raeini Cashmere goat based on microsatellite markers. J. Agric. Sci 18, 155-161.
Baghizadeh A., Bahaaddini M., Mohamadabadi M.R. and Askari N. (2009). Allelic variations in exon 2 of Caprine MHC class II DRB3 gene in Raeini Cashmere goat. Am. J. Agric. Environ. Sci. 6, 454-459.
Bahreini Behzadi M.R. and Aslaminejad A.A. (2010). A comparison of neural network and nonlinear regression prediction of sheep growth. J. Anim. Vet. Adv. 9, 2128-2131.
Barazandeh A., Moghbeli S.M., Hossein-Zadeh N.G. and Vatankhah M. (2012). Genetic evaluation of growth in Raini goat using random regression models. Livest. Sci. 145, 1-6.
Barazandeh A., Moghbeli S.M., Vatankhah M. and Mohammadabadi M.R. (2011). Estimating non genetic and genetic parameters of pre weaning growth traits in Raini Cashmere goat. Trop. Anim. Health Prod. 44, 811-817.
Birteeb P.T., Peters S.O., Yakubu A., Adeleke M.A. and Ozoje M.O. (2012). Multivariate characterisation of the phenotypic traits of Djallonke and Sahel sheep in Northern Ghana. Trop. Anim. Health Prod. 45, 267-274.
Brockmann G.A., Tsaih S.W., Neusch C., Churchill G.A. and Li R. (2009). Genetic factors contributing to obesity and body weight can act through mechanisms affecting muscle weight, fat weight, or both. Physiol. Genom. 36, 114-126.
Eyduran E., Topal M. and Sonmez A.Y. (2010). Use of factor scores in multiple regression analysis for estimation of body weight by several body measurements in brown trouts (Salmo trutta fario). Int. J. Agric. Biol. 12, 611-615.
Favaro L., Briefer E.F. and McElligott A.G. (2014). Artificial neural network approach for revealing individuality, group membership and age information in goat kid contact calls. Acta Acustica United Acustica. 100, 782-789
Fernández C., Soria E., Martín J.D. and Serrano A.J. (2006). Neural networks for animal science applications: Two case studies. Expert Syst. Appl. 31, 444-450.
Gandhi R.S., Raja T.V., Ruhil A.P. and Kumar A. (2009). Evolving prediction equations for lifetime milk production using artificial neural network methodology in Sahiwal cattle. Pp. 7-9 in Proc. 37th Dairy Ind. Conf. Goa., Panaji, India.
Ghazanfari S., Nobari K. and Tahmoorespur M. (2011). Prediction of egg production using artificial neural network. Iran. J. Anim. Sci. 1, 11-16.
Grzesiak W., Lacroixr R., Wojcik J. and Blaszczyk P. (2003). A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Canadian J. Anim. Sci. 83, 307-310.
Hassan K.J., Lopez-Benavidest M.G. and Samarasinghe S. (2009). Use of neural networks to detect minor and major pathogens that cause bovine mastitis. J. Dairy Sci. 92, 1493-1499.
Hassani M.N., Asadi Fozi M., Esmaelizadeh A.K. and MohammadAbadi M.R. (2010). A genetic analysis of growth traits in Raieni cashmere goat using multivariate animal model. Iranian J. Anim. Sci. 41, 323-329.
Haykin S.S. (2001). Neural Networks: A Comprehensive Foundation. Tsinghua University Press, Beijing, China.
Iqbal M., Javed K. and Ahmad N. (2013). Prediction of body weight through body measurements in Beetal goats. Pakistan J. Anim. Sci. 65, 458-461.
Kaygisiz F. and Sezgin F.H. (2017). Forecasting goat milk production in Turkey using Artificial Neural Networks and Box-Jenkins models. Anim. Rev. 4, 45-52.
Moghadaszadeh M., Mohammadabadi M.R. and Esmailizadeh A.K. (2015). Association of exon 2 of BMP15 gene with the litter size in the Raini Cashmere goat. Genet. 3rd Millennium. 13, 4062-4067.
Mohammad M.T., Rafeeq M., Bajwa M.A., Awan M.A., Abbas F., Waheed A., Bukhari F.A. and Akhtar P. (2012). Prediction of body weight from body measurements using regression tree (RT) method for Indigenous sheep breeds in Balochistan. Pakistan J. Anim. Plant Sci. 22, 20-24.
Mohammadabadi M.R. (2012). Relationships of IGFBP3 gene polymorphism with cashmere traits in Raini Cashmere goat. Modern Genet. J. 7, 115-120.
Molaei Moghbeli S., Barazandeh A., Vatankhah M. and Mohammadabadi M.R. (2013). Genetics and non genetics parameters of body weight for post-weaning traits in Raini Cashmere goats. Trop. Anim. Health Prod. 45, 1519-1524.
Musa A.M., Idam N.Z. and Elamin K.M. (2012). Heart girth reflect live body weight in Sudanese Shogur sheep under field conditions. World’s Vet. J. 2, 54-56.
Pour Hamidi S., Mohammadabadi M.R., Asadi Foozi M. and Nezamabadipour H. (2017). Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J. Livest. Sci. Technol. 5, 53-61.
Raja T.V., Ruhil A.P. and Gandhi R.S. (2012). Comparison of connectionist and multiple regression approaches for prediction of body weight of goats. Neural Comp. Appl. 21, 119-124.
Rani A., Raghavan K.C. and Mercey K.A. (2010). Prediction of body weight of Malabari goats from body measurements under field conditions. J. Vet. Anim. Sci. 41, 21-27.
Roush W.B., Dozier W.A. and Branton S.L. (2006). Comparison of gompertz and neural network models of broiler growth. Poult. Sci. 85, 794-797.
Ruhil A.P., Raja T.V. and Gandhi R.S. (2013). Preliminary study on prediction of body weight morphometric of goats through ANN models. J. Indian Soc. Agric. Stat. 67, 51-58.
Shahinfar S., Mehrabani-Yeganeh H., Lucas C., Kalhor A., Kazemian M. and Weigel K.A. (2012). Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Comput. Math. Methods Med. 4, 1-9.
Shamsalddini S., Mohammadabadi M.R. and Esmailizadeh A.K. (2016). Polymorphism of the prolactin gene and its effect on fiber traits in goat. Russian J. Genet. 52, 405-408.
Sharma A.K., Sharma R.K. and Kasana H.S. (2006). Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows. Neural Comp. Appl. 15, 359-365.
Takma Ç., Atıl H. and Aksakal V. (2012). Comparison of multiple linear regression and artificial neural network models goodness of fit to lactation milk yields. Kafkas Univ. Vet. Fak. Derg. 18, 941-944.
Thiruvenkadan A.K. (2005). Determination of best fitted regression model for estimation of body weight in Kanni Adu kids under farmer’s management system. Livest. Res. Rural Devel. 17, 76-78.
Tohidi Nezhad F., Mohamadabadi M.R., Esmailizadeh A.K. and Najmi N. (2015). Comparison of different levels of Rheb gene expression in different tissues of Raini Cashmir goat. J. Agric. Biotechnol. 6, 35-50.
Yang X.Z., Wade K.M. and Lacroix R. (2000). Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks. Canadian J. Anim. Sci. 80, 415-426.
