عوامل مؤثر بر ماندگاری گاوهای شیری هلشتاین: یک رویکرد داده کاوی

Reference

Bergstra, J., & Yoshua, B. (2012). Random search for hyper-parameter optimization. The Journal of Machine Learning Research,  13, 281-305.

Botchkarev, A. (2018). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: properties and typology. Interdiscipl. J. Inf. Knowl. Manag, 14, 45-79. https://arxiv.org/abs/1809.03006.

Carvalho, T., Soares, F., Vita, R., Francisco, R., Basto, J., & G. Soares Alcalá, S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.

Ransom, C. J., Kitchen, N. R., Camberato, J. J., Carter, P. R., Ferguson, R. B., Fernández, F. G., ... & Shanahan, J. F. (2019). Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations. Computers and electronics in agriculture, 164, 104872. https://doi.org/10.1016/j.compag.2019.104872.

Torshizi, M. E., Mashhadi, M. H., & Farhangfar, H. (2019). Different aspects of lactation persistency in dairy cows. The Indian Journal of Animal Sciences, 89(6), 607-614.

Fadul-Pacheco, L., Delgado, H., & Cabrera, V. E. (2021). Exploring machine learning algorithms for early prediction of clinical mastitis. International Dairy Journal, 119, 105051.

Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85. https://doi.org/10.1007/978-0-387-84858-7.

Horn, M., Steinwidder, A., Gasteiner, J., Podstatzky, L., Haiger, A., & Zollitsch, W. (2013). Suitability of different dairy cow types for an Alpine organic and low-input milk production system. Livestock Science, 153(1-3), 135-146.

Hu, H., Mu, T., Ma, Y., Wang, X., & Ma, Y. (2021). Analysis of longevity traits in Holstein cattle: A review. Frontiers in genetics, 12, 695543.

Imbayarwo‐Chikosi, V. E., Ducrocq, V., Banga, C. B., Halimani, T. E., Van Wyk, J. B., Maiwashe, A., & Dzama, K. (2017). Estimation of genetic parameters for functional longevity in the South African Holstein cattle using a piecewise Weibull proportional hazards model. Journal of Animal Breeding and Genetics, 134(5), 364-372.

Kuhn, M. (2019). The Caret Package. Available online:  http://topepo.github.io/caret/index.html 

Lantz, B. (2015). Machine learning with R (Vol. 452). Birmingham: Packt publishing. ISBN 978-1-78216-214-8.

Mammadova, N., & Keskin, I. (2013). Application of the support vector machine to predict subclinical mastitis in dairy cattle. The Scientific World Journal, 2013(1), 603897. http://dx.doi.org/10.1155/2013/603897.

Pietersma, D., Lacroix, R., Lefebvre, D., & Wade, K. M. (2003). Performance analysis for machine-learning experiments using small data sets. Computers and electronics in agriculture, 38(1), 1-17.

Robert, B., White, B. J., Renter, D. G., & Larson, R. L. (2009). Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle. Computers and electronics in agriculture, 67(1-2), 80-84.

Rostellato, R., Lora, I., Promp, J., Cassandro, M., Ducrocq, V., & Cozzi, G. (2022). Factors affecting true and functional productive lifespan in Italian Holstein-Friesian cows. Italian Journal of Animal Science, 21(1), 1268-1276.

Schuster, J. C., Barkema, H. W., De Vries, A., Kelton, D. F., & Orsel, K. (2020). Invited review: Academic and applied approach to evaluating longevity in dairy cows. Journal of Dairy Science, 103(12), 11008-11024.

Shahinfar, S., Khansefid, M., Haile-Mariam, M., & Pryce, J. E. (2021). Machine learning approaches for the prediction of lameness in dairy cows. Animal, 15(11), 100391.

Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88.

Tamura, T., Okubo, Y., Deguchi, Y., Koshikawa, S., Takahashi, M., Chida, Y., & Okada, K. (2019). Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers. Animal Science Journal, 90(4), 589-596.

Pelt, M. V., Jong, G. D., & Veerkamp, R. F. (2017). Improving the genetic evaluation for longevity in the Netherlands. Interbull bulletin 51: 33-37, Tallinn, Estonia, August 25-28.

Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., & Data, M. (2005, June). Practical machine learning tools and techniques. In Data mining (Vol. 2, No. 4, pp. 403-413). Amsterdam, The Netherlands: Elsevier.