Redes neuronales artificiales en el pronóstico de la producción de leche bovina

Los pronósticos facilitan la toma de decisiones en granjas productoras de leche y contribuyen a mejorar la cadena productiva de este alimento. En la literatura se identificó que las redes neuronales artificiales poseen un ajuste aceptable al pronóstico de las producciones de leche. Sin embargo, en l...

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Autores:: Perdigón Llanes, Rudibel
González Benítez, Neilys

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Redes neuronales artificiales en el pronóstico de la producción de leche bovina
dc.title.translated.eng.fl_str_mv	Artificial neural networks in bovine milk production forecasting
title	Redes neuronales artificiales en el pronóstico de la producción de leche bovina
spellingShingle	Redes neuronales artificiales en el pronóstico de la producción de leche bovina Inteligencia artificial Modelos de pronóstico Ganadería Toma de decisiones Artificial intelligence Forecasting models Livestock Decision making
title_short	Redes neuronales artificiales en el pronóstico de la producción de leche bovina
title_full	Redes neuronales artificiales en el pronóstico de la producción de leche bovina
title_fullStr	Redes neuronales artificiales en el pronóstico de la producción de leche bovina
title_full_unstemmed	Redes neuronales artificiales en el pronóstico de la producción de leche bovina
title_sort	Redes neuronales artificiales en el pronóstico de la producción de leche bovina
dc.creator.fl_str_mv	Perdigón Llanes, Rudibel González Benítez, Neilys
dc.contributor.author.none.fl_str_mv	Perdigón Llanes, Rudibel González Benítez, Neilys
dc.contributor.orcid.spa.fl_str_mv	Perdigón Llanes, Rudibel [0000-0001-7288-6224] González Benítez, Neilys [0000-0001-8691-445X]
dc.subject.spa.fl_str_mv	Inteligencia artificial Modelos de pronóstico Ganadería Toma de decisiones
topic	Inteligencia artificial Modelos de pronóstico Ganadería Toma de decisiones Artificial intelligence Forecasting models Livestock Decision making
dc.subject.keywords.eng.fl_str_mv	Artificial intelligence Forecasting models Livestock Decision making
description	Los pronósticos facilitan la toma de decisiones en granjas productoras de leche y contribuyen a mejorar la cadena productiva de este alimento. En la literatura se identificó que las redes neuronales artificiales poseen un ajuste aceptable al pronóstico de las producciones de leche. Sin embargo, en las fuentes bibliográficas consultadas no se evidenció un consenso sobre el tipo de red neuronal artificial con mejores rendimientos en esta actividad. Esta investigación tiene como objetivo identificar la red neuronal artificial con mayores índices de desempeño en el pronóstico de la producción de leche bovina. Se realizó una revisión de la literatura relacionada con los pronósticos de las producciones de leche mediante el uso de redes neuronales artificiales. Los resultados obtenidos en la literatura analizada evidenciaron que las redes no lineales autorregresivas con variables exógenas y las redes convolucionales poseen los mejores rendimientos en el pronóstico de la producción de leche bovina.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-11-23
dc.date.accessioned.none.fl_str_mv	2024-09-13T20:54:33Z
dc.date.available.none.fl_str_mv	2024-09-13T20:54:33Z
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dc.type.local.spa.fl_str_mv	Artículo
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dc.identifier.issn.spa.fl_str_mv	ISSN: 1657-2831 e-ISSN: 2539-2115
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/20.500.12749/26524
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Autónoma de Bucaramanga UNAB
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dc.identifier.doi.none.fl_str_mv	https://doi.org/10.29375/25392115.4209
identifier_str_mv	ISSN: 1657-2831 e-ISSN: 2539-2115 instname:Universidad Autónoma de Bucaramanga UNAB repourl:https://repository.unab.edu.co
url	http://hdl.handle.net/20.500.12749/26524 https://doi.org/10.29375/25392115.4209
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dc.relation.references.none.fl_str_mv	Akilli, A., & Atil, H. (2020). Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield. Turkish Journal of Agricultural Engineering Research, 354–367. https://doi.org/10.46592/turkager.2020.v01i02.011 Atil, H., & Akilli, A. (2016). Comparison of artificial neural network and K-means for clustering dairy cattle. International Journal of Sustainable Agricultural Management and Informatics, 2, 40. https://doi.org/10.1504/IJSAMI.2016.077266 Banerjee, G., Sarkar, U., Das, S., Das, S., & Ghosh, I. (2018). Artificial Intelligence in Agriculture: A Literature Survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1–6. Bhosale, M. D., & Singh, T. P. (2015). Comparative study of feed-forward neuro-computing with multiple linear regression model for milk yield prediction in dairy cattle. Current Science, 108(12), 2257–2261. Bhosale, M. D., & Singh, T. P. (2017). Development of Lifetime Milk Yield Equation Using Artificial Neural Network in Holstein Friesian Cross Breddairy Cattle and Comparison with Multiple Linear Regression Model. Current Science, 113(05), 951. https://doi.org/10.18520/cs/v113/i05/951-955 Boniecki, P., Lipiński, M., Koszela, K., & Przybył, J. (2013). Neural prediction of cows’ milk yield according to environment temperature. African Journal of Biotechnology, 12(29). Chaturvedi, S., Gupta, A., Yadav, R., & Sharma, A. K. (2013). Life time milk amount prediction in dairy cows using artificial neural networks. International Journal of Recent Research and Review, 5, 1–6. Cockburn, M. (2020). Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms. Animals, 10(9). https://doi.org/10.3390/ani10091690 da Silva, I. N., Hernane Spatti, D., Andrade Flauzino, R., Liboni, L. H. B., & dos Reis Alves, S. F. (2017). Artificial Neural Network Architectures and Training Processes. In I. N. da Silva, D. Hernane Spatti, R. Andrade Flauzino, L. H. B. Liboni, & S. F. dos Reis Alves (Eds.), Artificial Neural Networks (pp. 21–28). Springer International Publishing. https://doi.org/10.1007/978-3-319-43162-8_2 Dongre, V., Gandhi, R. S., Singh, A., & Ruhil, A. P. (2012). Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle. Livestock Science, 147(1), 192–197. https://doi.org/10.1016/j.livsci.2012.04.002 Dongre, V., Kokate, L. S., Salunke, V., Durge, S., & Patil, V. (2017). Artificial Intelligence for Prediction of Standard Lactation Milk yield in Deoni Cattle. International Journal of Livestock Research, 1. https://doi.org/10.5455/ijlr.20170806105856 Flores-Calero, M., Leppe, B., Pilla, M., Gualsaqui, M., Zabala-Blanco, D., & Albuja, A. (2021). Multiclasificación de arritmias cardíacas usando una red neuronal y la tarjeta MyRio-1900. Inteligencia Artificial Revista Iberoamericana de Inteligencia Artificial, 24, 129–146. https://doi.org/10.4114/intartif.vol24iss67pp129-146 Gandhi, N., & Armstrong, L. J. (2016). A review of the application of data mining techniques for decision making in agriculture. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 1–6. https://doi.org/10.1109/IC3I.2016.7917925 Gandhi, R. S., Monalisa, V. B., Ruhil, A. P., Singh, A., & Sachdeva, G. K. (2012). Prediction of first lactation 305-day milk yield based on weekly test day records using artificial neural networks in Sahiwal Cattle. Indian Journal of Dairy Science, 65(3), 229–233. Gandhi, R. S., Raja, T., Ruhil, A. P., & Kumar, A. (2010). Artificial Neural Network versus Multiple Regression Analysis for Prediction of Lifetime Milk Production in Sahiwal Cattle. Journal of Applied Animal Research, 38, 233–237. https://doi.org/10.1080/09712119.2010.10539517 Gorgulu, O. (2012). Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. South African Journal of Animal Science, 42(3). https://doi.org/10.4314/sajas.v42i3.10 Gorgulu, O. (2018). Prediction of 305 days milk yield from early records in dairy cattle using on Fuzzy Inference System. The Journal of Animal & Plant Sciences, 28(4), 996–1001. Grzesiak, W., Zaborski, D., Szatkowska, I., & Królaczyk, K. (2021). Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model. Animal Bioscience, 34(4), 770–782. https://doi.org/10.5713/ajas.19.0939 Gupta, A., Salau, A. O., Chaturvedi, P., & Akinola, S. A. (2019). Artificial Neural Networks: Its Techniques and Applications to Forecasting. 2019 International Conference on Automation, Computational and Technology Management (ICACTM), 320–324. https://doi.org/10.1109/ICACTM.2019.8776701 Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1–12. https://doi.org/10.1016/j.aiia.2019.05.004 Kamilaris, A., & Prenafeta Boldú, F. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156, 1–11. https://doi.org/10.1017/S0021859618000436 Kumar, H., & Hooda, B. (2014). Prediction of milk production using artificial neural network. Current Advances in Agricultural Sciences, 6(2), 173. https://doi.org/10.5958/2394-4471.2014.00013.6 Lame, G. (2019). Systematic Literature Reviews: An Introduction. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), 1633–1642. https://doi.org/10.1017/dsi.2019.169 Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8). https://doi.org/10.3390/s18082674 Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2019). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194. https://doi.org/10.1177/0312896219877678 Liseune, A., Salamone, M., van den Poel, D., Ranst, B., & Hostens, M. (2020). Leveraging latent representations for milk yield prediction and interpolation using deep learning. Computers and Electronics in Agriculture, 175, 105600. https://doi.org/10.1016/j.compag.2020.105600 Liseune, A., Salamone, M., van den Poel, D., van Ranst, B., & Hostens, M. (2021). Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Computers and Electronics in Agriculture, 180, 105904. https://doi.org/10.1016/j.compag.2020.105904 Liu, Y., Ma, X., Shu, L., Hancke, G. P., & Abu-Mahfouz, A. M. (2021). From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Transactions on Industrial Informatics, 17(6), 4322–4334. https://doi.org/10.1109/TII.2020.3003910 Machado Dallago, G., Figueiredo, D. M. de, Andrade, P. C. de R., Santos, R. A. dos, Lacroix, R., Santschi, D. E., & Lefebvre, D. M. (2019). Predicting first test day milk yield of dairy heifers. Computers and Electronics in Agriculture, 166, 105032. https://doi.org/10.1016/j.compag.2019.105032 Manoj, M., Gandhi, R. S., Raja, T., Ruhil, A. P., Singh, A., & Gupta, A. K. (2014). Comparison of artificial neural network and multiple linear regression for prediction of first lactation milk yield using early body weights in Sahiwal cattle. Indian Journal of Animal Sciences, 84, 427–430. Murphy, M. D., O’Mahony, M. J., Shalloo, L., French, P., & Upton, J. (2014). Comparison of modelling techniques for milk-production forecasting. Journal of Dairy Science, 97(6), 3352–3363. https://doi.org/10.3168/jds.2013-7451 Nayak, J., Vakula, K., Dinesh, P., Naik, B., & Pelusi, D. (2020). Intelligent food processing: Journey from artificial neural network to deep learning. Computer Science Review, 38, 100297. https://doi.org/10.1016/j.cosrev.2020.100297 Nguyen, Q. T., Fouchereau, R., Frénod, E., Gerard, C., & Sincholle, V. (2020). Comparison of forecast models of production of dairy cows combining animal and diet parameters. Computers and Electronics in Agriculture, 170, 105258. https://doi.org/10.1016/j.compag.2020.105258 Njubi, D., Wakhungu, J., & Badamana, M. S. (2011). Prediction of second parity milk yield of Kenyan Holstein-Friesian dairy cows on first parity information using neural network system and multiple linear regression methods. Livestock Research for Rural Development, 23. Njubi, D., Wakhungu, J. W., & Badamana, M. S. (2010). Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein-Friesian dairy cows. Tropical Animal Health and Production, 42(4), 639–644. https://doi.org/10.1007/s11250-009-9468-7 Oyegbile, B., Akinsola, O., Obioma, O., Atanda, A., Paul, B., Oladipo, M., & Abba, Z. (2018). Neural Network and Regression Based Model for Cows’ Milk Yield Prediction in Different Climatic Gradients. Annual Research & Review in Biology, 28, 1–9. https://doi.org/10.9734/ARRB/2018/41947 Panigrahi, S., Karali, Y., & Behera, Dr. H. (2013). Time Series Forecasting using Evolutionary Neural Network. International Journal of Computer Applications, 75, 13–17. https://doi.org/10.5120/13146-0553 Peña-Rueda, Y., Benitez, D., Ray, J. v, & Fernández-Romay, Y. (2018). Determinant factors of livestock production in a rural community in the southwest of Holguín, Cuba. Literature-Film Quarterly, 52. Perdigón Llanes, R., & González Benítez, N. (2020). Una revisión bibliográfica sobre modelos para predecir las producciones de leche. Revista Ingeniería Agrícola, 10(4). Perdigón Llanes, R., & González Benítez, N. (2021). Comparación y selección de técnicas de inteligencia artificial para pronosticar las producciones de leche bovina. 15. Pimpa, A., Eiamkanitchat, N., Phatsara, C., & Moonmanee, T. (2019). Decision Support System for Dairy Cattle Management Using Computational Intelligence Technique. Proceedings of the 2019 7th International Conference on Computer and Communications Management, 181–185. https://doi.org/10.1145/3348445.3348449 Radwan, H., el Qaliouby, H., & Elfadl, E. A. (2020). Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models. Journal of Advanced Veterinary and Animal Research, 7(3), 429–435. https://doi.org/10.5455/javar.2020.g438 Ruelle, E., Delaby, L., & Shalloo, L. (2019). Linkage between predictive transmitting ability of a genetic index, potential milk production, and a dynamic model. Journal of Dairy Science, 102. https://doi.org/10.3168/jds.2018-15197 Saefullah, A., Hendri, M., Lindawati, S., Badaruddin, M., & Hutahaean, J. (2020). Analysis of Deep Learning Cyclical order for Prediction of Fresh Milk Production in Sumatera. Journal of Physics: Conference Series, 1566, 12087. https://doi.org/10.1088/1742-6596/1566/1/012087 Sharma, S. K., & Kumar, E. (2014). Anticipating milk yield using artificial neural network. Int. Journal of Applied Sciences and Engineering Research, 3, 690–695. https://doi.org/10.6088/ijaser.030300013 Sharma, S., Sharma, S., & Athaiya, A. (2020). Activation functions in neural networks. International Journal of Engineering Applied Sciences and Technology, 04(12), 310–316. https://doi.org/10.33564/IJEAST.2020.v04i12.054 Singh, N. P., Usman, S., Maurya, V., Dutt, T., Bhatt, N., & Kumar, A. (2020). Comparative analysis of artificial neural network algorithms for prediction of FL305DMY in Murrah buffalo. International Journal of Livestock Research, 1. https://doi.org/10.5455/ijlr.20200704062936 Slob, N., Catal, C., & Kassahun, A. (2020). Application of Machine Learning to Improve Dairy Farm Management: A Systematic Literature Review. Preventive Veterinary Medicine, 187, 105237. https://doi.org/10.1016/j.prevetmed.2020.105237 Sugiono, S., Soenoko, R., & Andriani, D. (2016). Analysis the relationship of physiological, environmental, and cow milk productivity using AI. https://doi.org/10.1109/ICODSE.2016.7936165 Sugiono, S., Soenoko, R., & Riawati, L. (2017). Investigating the Impact of Physiological Aspect on Cow Milk Production Using Artificial Intelligence. International Review of Mechanical Engineering (I.RE.M.E.), 11, 7. Torres-Inga, C. S., López-Crespo, G., Guevara-Viera, R., Narváez-Terán, J., Serpa-Garcia, V. G., Guzmán-Espinoza, C. K., Guevara-Viera, G., & de Juana, Á. J. (2019). Eficiencia técnica en granjas lecheras de la Sierra Andina mediante modelación con redes neuronales. Revista de Producción Animal, 31(1), 11–17. Usman, M., Singh, N. P., Dutt, T., Tiwari, R., & Kumar, A. (2020). Comparative study of artificial neural network algorithms performance for prediction of FL305DMY in crossbred cattle. Journal of Entomology and Zoology Studies, 8, 516–520. Winkowski, C. (2019). Classification of forecasting methods in production engineering. Engineering Management in Production and Services, 11(4), 23–33. Yan, W. J., Chen, X., Akcan, O., Lim, J., & Yang, D. (2015). Big data analytics for empowering milk yield prediction in dairy supply chains. 2015 IEEE International Conference on Big Data (Big Data), 2132–2137. https://doi.org/10.1109/BigData.2015.7363997 Zhang, F., Murphy, M. D., Shalloo, L., Ruelle, E., & Upton, J. (2016). An automatic model configuration and optimization system for milk production forecasting. Computers and Electronics in Agriculture, 128, 100–111. https://doi.org/10.1016/j.compag.2016.08.016 Zhang, F., Upton, J., Shalloo, L., & Murphy, M. D. (2019). Effect of parity weighting on milk production forecast models. Computers and Electronics in Agriculture, 157, 589–603. https://doi.org/10.1016/j.compag.2018.12.051 Zhang, F., Upton, J., Shalloo, L., Shine, P., & Murphy, M. D. (2020). Effect of introducing weather parameters on the accuracy of milk production forecast models. Information Processing in Agriculture, 7(1), 120–138. https://doi.org/10.1016/j.inpa.2019.04.004 Zhang, W., Yang, K., Yu, N., Cheng, T., & Liu, J. (2020). Daily milk yield prediction of dairy cows based on the GA-LSTM algorithm. 2020 15th IEEE International Conference on Signal Processing (ICSP), 1, 664–668. https://doi.org/10.1109/ICSP48669.2020.9320926
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spelling	Perdigón Llanes, Rudibelee4515a1-a61d-4ac9-9690-6a01cb257ff6González Benítez, Neilysf73e924e-701d-46ae-ab52-dcc27cc88426Perdigón Llanes, Rudibel [0000-0001-7288-6224]González Benítez, Neilys [0000-0001-8691-445X]2024-09-13T20:54:33Z2024-09-13T20:54:33Z2021-11-23ISSN: 1657-2831e-ISSN: 2539-2115http://hdl.handle.net/20.500.12749/26524instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/25392115.4209Los pronósticos facilitan la toma de decisiones en granjas productoras de leche y contribuyen a mejorar la cadena productiva de este alimento. En la literatura se identificó que las redes neuronales artificiales poseen un ajuste aceptable al pronóstico de las producciones de leche. Sin embargo, en las fuentes bibliográficas consultadas no se evidenció un consenso sobre el tipo de red neuronal artificial con mejores rendimientos en esta actividad. Esta investigación tiene como objetivo identificar la red neuronal artificial con mayores índices de desempeño en el pronóstico de la producción de leche bovina. Se realizó una revisión de la literatura relacionada con los pronósticos de las producciones de leche mediante el uso de redes neuronales artificiales. Los resultados obtenidos en la literatura analizada evidenciaron que las redes no lineales autorregresivas con variables exógenas y las redes convolucionales poseen los mejores rendimientos en el pronóstico de la producción de leche bovina.Forecasting facilitates decision-making on dairy farms and contributes to improving the milk production chain. According to the literature, artificial neural networks have an acceptable adjustment to milk production forecasting. However, in the consulted bibliographic sources, there was no consensus on the type of artificial neural network with the best performance in this activity. This research is aimed at identifying the artificial neural network with the highest performance levels in bovine milk production forecasting. A literature review related to milk production forecasting using artificial neural networks was carried out. The results from the sources examined revealed that nonlinear autoregressive networks with exogenous variables and convolutional neural networks perform best in forecasting bovine milk production.application/pdfspaUniversidad Autónoma de Bucaramanga UNABhttps://revistas.unab.edu.co/index.php/rcc/article/view/4209/3609https://revistas.unab.edu.co/index.php/rcc/issue/view/282Akilli, A., & Atil, H. (2020). Evaluation of Normalization Techniques on Neural Networks for the Prediction of 305-Day Milk Yield. Turkish Journal of Agricultural Engineering Research, 354–367. https://doi.org/10.46592/turkager.2020.v01i02.011Atil, H., & Akilli, A. (2016). Comparison of artificial neural network and K-means for clustering dairy cattle. International Journal of Sustainable Agricultural Management and Informatics, 2, 40. https://doi.org/10.1504/IJSAMI.2016.077266Banerjee, G., Sarkar, U., Das, S., Das, S., & Ghosh, I. (2018). Artificial Intelligence in Agriculture: A Literature Survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1–6.Bhosale, M. D., & Singh, T. P. (2015). Comparative study of feed-forward neuro-computing with multiple linear regression model for milk yield prediction in dairy cattle. Current Science, 108(12), 2257–2261.Bhosale, M. D., & Singh, T. P. (2017). Development of Lifetime Milk Yield Equation Using Artificial Neural Network in Holstein Friesian Cross Breddairy Cattle and Comparison with Multiple Linear Regression Model. Current Science, 113(05), 951. https://doi.org/10.18520/cs/v113/i05/951-955Boniecki, P., Lipiński, M., Koszela, K., & Przybył, J. (2013). Neural prediction of cows’ milk yield according to environment temperature. African Journal of Biotechnology, 12(29).Chaturvedi, S., Gupta, A., Yadav, R., & Sharma, A. K. (2013). Life time milk amount prediction in dairy cows using artificial neural networks. International Journal of Recent Research and Review, 5, 1–6.Cockburn, M. (2020). Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms. Animals, 10(9). https://doi.org/10.3390/ani10091690da Silva, I. N., Hernane Spatti, D., Andrade Flauzino, R., Liboni, L. H. B., & dos Reis Alves, S. F. (2017). Artificial Neural Network Architectures and Training Processes. In I. N. da Silva, D. Hernane Spatti, R. Andrade Flauzino, L. H. B. Liboni, & S. F. dos Reis Alves (Eds.), Artificial Neural Networks (pp. 21–28). Springer International Publishing. https://doi.org/10.1007/978-3-319-43162-8_2Dongre, V., Gandhi, R. S., Singh, A., & Ruhil, A. P. (2012). Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle. Livestock Science, 147(1), 192–197. https://doi.org/10.1016/j.livsci.2012.04.002Dongre, V., Kokate, L. S., Salunke, V., Durge, S., & Patil, V. (2017). Artificial Intelligence for Prediction of Standard Lactation Milk yield in Deoni Cattle. International Journal of Livestock Research, 1. https://doi.org/10.5455/ijlr.20170806105856Flores-Calero, M., Leppe, B., Pilla, M., Gualsaqui, M., Zabala-Blanco, D., & Albuja, A. (2021). Multiclasificación de arritmias cardíacas usando una red neuronal y la tarjeta MyRio-1900. Inteligencia Artificial Revista Iberoamericana de Inteligencia Artificial, 24, 129–146. https://doi.org/10.4114/intartif.vol24iss67pp129-146Gandhi, N., & Armstrong, L. J. (2016). 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Daily milk yield prediction of dairy cows based on the GA-LSTM algorithm. 2020 15th IEEE International Conference on Signal Processing (ICSP), 1, 664–668. https://doi.org/10.1109/ICSP48669.2020.9320926Vol. 23 Núm. 1 (2022): Revista Colombiana de Computación (Enero-Junio); 20-33Inteligencia artificialModelos de pronósticoGanaderíaToma de decisionesArtificial intelligenceForecasting modelsLivestockDecision makingRedes neuronales artificiales en el pronóstico de la producción de leche bovinaArtificial neural networks in bovine milk production forecastinginfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/access_right/c_abf2ORIGINALArtículo.pdfArtículo.pdfArtículoapplication/pdf419508https://repository.unab.edu.co/bitstream/20.500.12749/26524/1/Art%c3%adculo.pdf07d4dd13dfcaa1a75fdc6a0eb16ec09dMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8347https://repository.unab.edu.co/bitstream/20.500.12749/26524/2/license.txt855f7d18ea80f5df821f7004dff2f316MD52open accessTHUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg9677https://repository.unab.edu.co/bitstream/20.500.12749/26524/3/Art%c3%adculo.pdf.jpg5fe8e0d5b70595079608246cf65b1749MD53open access20.500.12749/26524oai:repository.unab.edu.co:20.500.12749/265242024-09-13 22:01:32.572open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGEgUmV2aXN0YSBDb2xvbWJpYW5hIGRlIENvbXB1dGFjacOzbiBlcyBmaW5hbmNpYWRhIHBvciBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgQnVjYXJhbWFuZ2EuIEVzdGEgUmV2aXN0YSBubyBjb2JyYSB0YXNhIGRlIHN1bWlzacOzbiB5IHB1YmxpY2FjacOzbiBkZSBhcnTDrWN1bG9zLiBQcm92ZWUgYWNjZXNvIGxpYnJlIGlubWVkaWF0byBhIHN1IGNvbnRlbmlkbyBiYWpvIGVsIHByaW5jaXBpbyBkZSBxdWUgaGFjZXIgZGlzcG9uaWJsZSBncmF0dWl0YW1lbnRlIGludmVzdGlnYWNpw7NuIGFsIHDDumJsaWNvIGFwb3lhIGEgdW4gbWF5b3IgaW50ZXJjYW1iaW8gZGUgY29ub2NpbWllbnRvIGdsb2JhbC4=

Redes neuronales artificiales en el pronóstico de la producción de leche bovina

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