<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992021000200024</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Comparación y selección de técnicas de inteligencia artificial para pronosticar las producciones de leche bovina]]></article-title>
<article-title xml:lang="en"><![CDATA[Comparison and selection of artificial intelligence techniques for forecasting bovine milk productions]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Perdigón Llanes]]></surname>
<given-names><![CDATA[Rudibel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Benítez]]></surname>
<given-names><![CDATA[Neilys]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Empresa Comercializadora &#8220;Frutas Selectas&#8221;  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Centro Meteorológico Provincial CITMA  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>15</volume>
<numero>2</numero>
<fpage>24</fpage>
<lpage>43</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992021000200024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992021000200024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992021000200024&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Los pronósticos constituyen una herramienta efectiva para la toma de decisiones, principalmente en el sector de la industria láctea porque contribuyen a mejorar la gestión del rebaño lechero, ahorrar energía en las granjas y optimizar las inversiones de capital a largo plazo. La aplicación de técnicas de inteligencia artificial para pronosticar las producciones de leche es un tema de interés para la comunidad científica. Sin embargo, definir una técnica o modelo para pronosticar estas producciones con un rendimiento eficiente en diferentes ambientes es una actividad desafiante y compleja, porque ninguno es preciso en todos los escenarios. En esta investigación se compararon las técnicas de inteligencia artificial utilizadas en la literatura para pronosticar las producciones de leche bovina y se seleccionó mediante la aplicación del Proceso de Análisis Jerárquico la técnica con mejor ajuste a estos pronósticos. Se utilizaron como métodos científicos el analítico sintético, la encuesta y el método experimental. Los resultados obtenidos permitieron identificar a las técnicas de inteligencia artificial basadas en Redes Neuronales Artificiales como las de mejor ajuste al pronóstico de las producciones de leche bovina, superior a los Árboles de Decisión y a las Máquinas de Soporte Vectorial. Se determinó que los criterios de selección más relevantes en el ámbito de las producciones lecheras son la capacidad de estas técnicas para manejar datos que presentan incertidumbre y su habilidad para obtener resultados precisos de manera óptima. El análisis realizado apoya la toma de decisiones en organizaciones productoras de leche.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Forecasting is an effective decision-making tool, especially in the dairy industry, because it helps to improve dairy herd management, save farm energy and optimize long-term capital investments. The application of artificial intelligence techniques to forecasting milk productions is a topic of interest for the scientific community. However, defining a technique or model to forecast these productions with an absolute performance at a global level is a challenging and complex activity, because none is accurate in all scenarios. In this research, artificial intelligence techniques used in the literature to forecast bovine milk productions were compared and the technique with the best adjustment to these forecasts was selected through the application of the Analytic Hierarchy Process. The synthetic analysis, the survey and experimental method were used as scientific methods. The results obtained allowed identifying artificial intelligence techniques based on Artificial Neural Networks as the best fit for forecasting bovine milk production, superior to Decision Trees and Support Vector Machines. It was determined that the most relevant selection criteria in the dairy production sector are the capacity of these techniques to handle data that present uncertainty and their ability to obtain precise results in an optimal way. The analysis carried out supports decision making in milk producing organizations.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[análisis multi-criterio]]></kwd>
<kwd lng="es"><![CDATA[proceso de análisis jerárquico]]></kwd>
<kwd lng="es"><![CDATA[pronósticos]]></kwd>
<kwd lng="es"><![CDATA[toma de decisiones]]></kwd>
<kwd lng="en"><![CDATA[multi-criteria analysis]]></kwd>
<kwd lng="en"><![CDATA[analytic hierarchy process]]></kwd>
<kwd lng="en"><![CDATA[forecasting]]></kwd>
<kwd lng="en"><![CDATA[decision making]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Da Rosa]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Goldschmidt]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Kunst]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Deon]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Da Costa]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<source><![CDATA[Towards combining data prediction and internet of things to manage milk production on dairy cows]]></source>
<year>2020</year>
<page-range>169</page-range><publisher-name><![CDATA[Computers and Electronics in Agriculture]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dongre,]]></surname>
<given-names><![CDATA[V.B]]></given-names>
</name>
<name>
<surname><![CDATA[Gandhi]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Ruhil]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle]]></article-title>
<source><![CDATA[Livestock Science]]></source>
<year>2012</year>
<volume>147</volume>
<numero>1-3</numero>
<issue>1-3</issue>
<page-range>192-7</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Eyduran]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Yilmaz]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Tariq]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Kaygisiz]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimation of 305-d milk yield using regression tree method in Brown Swiss Cattle.]]></article-title>
<source><![CDATA[JAPS Journal of Animal and Plant Sciences]]></source>
<year>2013</year>
<volume>23</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>731-5</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Estrada]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
<name>
<surname><![CDATA[Febles]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estudio y selección de las técnicas de Inteligencia Artificial para el diagnóstico de enfermedades]]></article-title>
<source><![CDATA[Revista de Ciencias Médicas de Pinar del Río]]></source>
<year>2018</year>
<volume>22</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>534-44</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Leiva]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Faggioni]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Álvarez]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estudio comparado de las técnicas de Inteligencia Artificial para el diagnóstico de enfermedades en la ganadería.]]></article-title>
<source><![CDATA[Revista de Sistemas, Cibernética e Informática]]></source>
<year>2018</year>
<volume>15</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>17-20</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gorgulu]]></surname>
<given-names><![CDATA[O]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prediction of 305 days milk yield from early records in dairy cattle using on Fuzzy Inference System.]]></article-title>
<source><![CDATA[The Journal of Animal &amp; Plant Sciences]]></source>
<year>2018</year>
<volume>28</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>996-1001</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gunnar]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Different methods to forecast milk delivery to dairy: a comparison for forecasting.]]></article-title>
<source><![CDATA[International Journal of Agricultural Management]]></source>
<year>2015</year>
<volume>4</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>132-40</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jensen]]></surname>
<given-names><![CDATA[D. B]]></given-names>
</name>
<name>
<surname><![CDATA[Van Der Voort]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Dynamic forecasting of individual cow milk yield in automatic milking systems]]></article-title>
<source><![CDATA[J. Dairy Sci]]></source>
<year>2018</year>
<volume>101</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>1-12</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kaygisiz]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Sezgin]]></surname>
<given-names><![CDATA[F. H]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Forecasting goat milk production in Turkey using Artificial Neural Networks and Box-Jenkins models]]></article-title>
<source><![CDATA[Animal Review]]></source>
<year>2017</year>
<volume>4</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>45-52</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kli&#347;]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Piwczy&#324;ski]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Sawa]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Sitkowska]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prediction of Lactational Milk Yield of Cows Based on Data Recorded by AMS during the Periparturient Period]]></article-title>
<source><![CDATA[Animals]]></source>
<year>2021</year>
<volume>11</volume>
<numero>2</numero>
<issue>2</issue>
</nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liakos]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Busato]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Moshou]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Pearson]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Bochtis]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<source><![CDATA[Machine Learning in Agriculture: A Review]]></source>
<year>2018</year>
<publisher-name><![CDATA[Sensors]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liseunea]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Salamone]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Van Den Poel]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Van Ranst]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Hostens]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Leveraging latent representations for milk yield prediction and interpolation using deep learning.]]></article-title>
<source><![CDATA[Computers and Electronics in Agriculture]]></source>
<year>2020</year>
<volume>175</volume>
<numero>105600</numero>
<issue>105600</issue>
</nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Machado]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Figueiredoa]]></surname>
<given-names><![CDATA[D. M]]></given-names>
</name>
<name>
<surname><![CDATA[Resende]]></surname>
<given-names><![CDATA[P. C]]></given-names>
</name>
<name>
<surname><![CDATA[Dos Santosa]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Lacroixc]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Santschic]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Lefebvre]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<source><![CDATA[Predicting first test day milk yield of dairy heifers.]]></source>
<year>2019</year>
<volume>166</volume>
<page-range>1-8</page-range><publisher-name><![CDATA[Computers and Electronics in Agriculture]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mahmoudi]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Mi]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Liao]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Feylizadeh]]></surname>
<given-names><![CDATA[M. F]]></given-names>
</name>
<name>
<surname><![CDATA[Turskis]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Grey Best-Worst Method for Multiple Experts Multiple Criteria Decision Making Under Uncertainty]]></article-title>
<source><![CDATA[Informatica]]></source>
<year>2020</year>
<volume>31</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>331-57</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mendoza]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Solano]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Palencia]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Garcia]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Aplicación del proceso de jerarquía analítica (AHP) para la toma de decisión con juicios de expertos.]]></article-title>
<source><![CDATA[Ingeniare. Revista chilena de ingeniería]]></source>
<year>2019</year>
<volume>27</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>348-60</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Montalván-Estrada]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Aguilera-Corrales]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Veitia-Rodríguez]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Brígido-Flores]]></surname>
<given-names><![CDATA[O]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Análisis multicriterio para la gestión integrada de aguas residuales industriales]]></article-title>
<source><![CDATA[Ingeniería Industrial]]></source>
<year>2017</year>
<volume>38</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>56-67</page-range></nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Murphy]]></surname>
<given-names><![CDATA[M. D]]></given-names>
</name>
<name>
<surname><![CDATA[O&#8217;mahony]]></surname>
<given-names><![CDATA[M. J]]></given-names>
</name>
<name>
<surname><![CDATA[Shalloo]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[French]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Upton]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comparison of modeling techniques for milk-production forecasting.]]></article-title>
<source><![CDATA[J. Dairy Sci]]></source>
<year>2014</year>
<volume>97</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>3352-63</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Notte]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Pedemonte]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Cancela]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Chilibroste]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
</person-group>
<source><![CDATA[Resource allocation in pastoral dairy production systems: Evaluating exact and genetic algorithms approaches.]]></source>
<year>2016</year>
<volume>148</volume>
<page-range>114-23</page-range><publisher-name><![CDATA[Agricultural Systems]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nguyen]]></surname>
<given-names><![CDATA[Q. T]]></given-names>
</name>
<name>
<surname><![CDATA[Fouchereau]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Frénod]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Gerard]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Sincholle]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
</person-group>
<source><![CDATA[Comparison of forecast models of production of dairy cows combining animal and diet parameters.]]></source>
<year>2020</year>
<volume>170</volume>
<publisher-name><![CDATA[Computers and Electronics in Agriculture]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ocampo]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Tamayo]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Castaño]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Gestión del Riesgo en la Implementación de Sistemas Fotovoltaicos en Proyectos de Extracción de Oro en Colombia a partir del Proceso de Análisis Jerárquico (AHP).]]></article-title>
<source><![CDATA[Información Tecnológica]]></source>
<year>2019</year>
<volume>30</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>127-36</page-range></nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Oscullo]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Haro]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Pronóstico de la Demanda Diaria del Sistema Nacional Interconectado Utilizando Redes Neuronales]]></article-title>
<source><![CDATA[Revista Politécnica]]></source>
<year>2016</year>
<volume>38</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>77-82</page-range></nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ossadnik]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
<name>
<surname><![CDATA[Schinke]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[KASPAR]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<source><![CDATA[Group Aggregation Techniques for Analytic Hierarchy Process and Analytic Network Process: A Comparative Analysis.]]></source>
<year>2016</year>
<page-range>421-57</page-range><publisher-name><![CDATA[Group Decision and Negotiation]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rozman]]></surname>
<given-names><![CDATA[&#268;]]></given-names>
</name>
<name>
<surname><![CDATA[Grgi&#263;]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Maksimovi&#263;]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[&#262;ejvanovi&#263;]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Pu&#353;ka]]></surname>
<given-names><![CDATA[A. I]]></given-names>
</name>
<name>
<surname><![CDATA[&#352;aki&#263; Bobi&#263;]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multiple-criteria approach of evaluation of milk farm models in Bosnia and Herzegovina]]></article-title>
<source><![CDATA[Mljekarstvo]]></source>
<year>2016</year>
<volume>66</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>206-14</page-range></nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Piwczy&#324;ski]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Sitkowska]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Kolenda]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Brzozowski]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Aerts]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Schork]]></surname>
<given-names><![CDATA[P. M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees.]]></article-title>
<source><![CDATA[Animal Science Journal]]></source>
<year>2020</year>
<volume>91</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Perdigón]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Una revisión bibliográfica sobre modelos para predecir las producciones de leche]]></article-title>
<source><![CDATA[Revista Ingeniería Agrícola]]></source>
<year>2020</year>
<volume>10</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>69-77</page-range></nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Perdigón]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Viltres]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Orellana]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Models for predicting perishable products demands in food trading companies.]]></article-title>
<source><![CDATA[Revista Cubana de Ciencias Informáticas]]></source>
<year>2020</year>
<volume>14</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>110-35</page-range></nlm-citation>
</ref>
<ref id="B27">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Saaty]]></surname>
<given-names><![CDATA[T. L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[How to make a decision: The analytic hierarchy process.]]></article-title>
<source><![CDATA[European Journal of Operational Research]]></source>
<year>1990</year>
<volume>48</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>9-26</page-range></nlm-citation>
</ref>
<ref id="B28">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Saaty]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Ergu]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods.]]></article-title>
<source><![CDATA[International Journal of Information Technology &amp; Decision Making]]></source>
<year>2015</year>
<volume>14</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1171-87</page-range></nlm-citation>
</ref>
<ref id="B29">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Saha]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Bhattacharyya]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial insemination for milk production in India: A statistical insight.]]></article-title>
<source><![CDATA[Indian Journal of Animal Sciences]]></source>
<year>2020</year>
<volume>90</volume>
<numero>8</numero>
<issue>8</issue>
<page-range>1186-90</page-range></nlm-citation>
</ref>
<ref id="B30">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Slob]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Catal]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Kassahun]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<source><![CDATA[Application of Machine Learning to Improve Dairy Farm Management: A Systematic Literature Review]]></source>
<year>2021</year>
<volume>187</volume>
<page-range>105237</page-range><publisher-name><![CDATA[Preventive Veterinary Medicine]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B31">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sugiono]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Soenoko]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Riawati]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Investigating the Impact of Physiological Aspect on Cow Milk Production Using Artificial Intelligence.]]></article-title>
<source><![CDATA[International Review of Mechanical Engineering]]></source>
<year>2017</year>
<volume>11</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>30-6</page-range></nlm-citation>
</ref>
<ref id="B32">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Torres-Inga]]></surname>
<given-names><![CDATA[C. S]]></given-names>
</name>
<name>
<surname><![CDATA[López-Crespo]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Guevara-Viera]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Narváez-Terán]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Serpa- García]]></surname>
<given-names><![CDATA[V. G]]></given-names>
</name>
<name>
<surname><![CDATA[Guzmán-Espinoza]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Guevara-Viera]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Aguirre de Juana]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Eficiencia técnica en granjas lecheras de la Sierra Andina mediante modelación con redes neuronales.]]></article-title>
<source><![CDATA[Revista Producción Animal]]></source>
<year>2019</year>
<volume>31</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>10-5</page-range></nlm-citation>
</ref>
<ref id="B33">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wen]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Liao]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Zavadskas]]></surname>
<given-names><![CDATA[E. K]]></given-names>
</name>
</person-group>
<source><![CDATA[Macont: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis]]></source>
<year>2020</year>
<page-range>1-24</page-range><publisher-name><![CDATA[Informatica]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B34">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[W. J]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Akcan]]></surname>
<given-names><![CDATA[O]]></given-names>
</name>
<name>
<surname><![CDATA[Lim]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<source><![CDATA[Big Data Analytics for Empowering Milk Yield Prediction in Dairy Supply Chains.]]></source>
<year>2015</year>
<page-range>2132-7</page-range><publisher-name><![CDATA[International Conference on Big Data (Big Data)]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B35">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Murphy]]></surname>
<given-names><![CDATA[M. D]]></given-names>
</name>
<name>
<surname><![CDATA[Shalloo]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Ruelle]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Upton]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<source><![CDATA[An automatic model configuration and optimization system for milk production forecasting.]]></source>
<year>2016</year>
<page-range>100-11</page-range><publisher-name><![CDATA[Computers and Electronics in Agriculture]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B36">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Upton]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Shalloo]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Murphy]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<source><![CDATA[Effect of parity weighting on milk production forecast models.]]></source>
<year>2019</year>
<volume>157:</volume>
<page-range>589-603</page-range><publisher-name><![CDATA[Computers and Electronics in Agriculture]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B37">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Upton]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Shalloo]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Shine]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Murphy]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Effect of introducing weather parameters on the accuracy of milk production forecast models.]]></article-title>
<source><![CDATA[Information Processing in Agriculture]]></source>
<year>2020</year>
<volume>7</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>120-38</page-range></nlm-citation>
</ref>
<ref id="B38">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<source><![CDATA[Daily milk yield prediction of dairy cows based on the GA-LSTM algorithm.]]></source>
<year>2020</year>
<page-range>664-8</page-range><publisher-name><![CDATA[International Conference on Signal Processing (ICSP),]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
