<?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>0123-4226</journal-id>
<journal-title><![CDATA[Revista U.D.C.A Actualidad & Divulgación Científica]]></journal-title>
<abbrev-journal-title><![CDATA[rev.udcaactual.divulg.cient.]]></abbrev-journal-title>
<issn>0123-4226</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Aplicadas y Ambientales]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-42262022000200020</article-id>
<article-id pub-id-type="doi">10.31910/rudca.v25.n2.2022.2334</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aplicaciones de aprendizaje automático para el análisis industrial de la provisión azucarera en Matanzas, Cuba]]></article-title>
<article-title xml:lang="en"><![CDATA[Machine learning application to industrial analysis of the sugar provision in Matanzas, Cuba]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-López]]></surname>
<given-names><![CDATA[Yasmany]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Sáez]]></surname>
<given-names><![CDATA[Lourdes Yamen]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cabrera-Hernández]]></surname>
<given-names><![CDATA[Juan Alfredo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto de Investigaciones de la Caña de Azúcar  ]]></institution>
<addr-line><![CDATA[Matanzas ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Matanzas Departamento de Química e Ingeniería Química ]]></institution>
<addr-line><![CDATA[Matanzas ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Matanzas Observatorio Ambiental COSTATENAS ]]></institution>
<addr-line><![CDATA[Matanzas ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>25</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-42262022000200020&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-42262022000200020&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-42262022000200020&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El análisis de los servicios ecosistémicos puede aportar conocimientos importantes sobre cómo se procesan y se obtienen los bienes del sistema agroindustrial azucarero. Para este trabajo, se recopilaron 346 datos del procesamiento industrial de la caña de azúcar en tres zafras, en la agroindustria del municipio Calimete, Provincia Matanzas (Cuba), con el objetivo de emplear algoritmos de aprendizaje automáticos, para predicciones relacionadas a datos biofísicos y económicos. Se analizaron siete predictores y mediante best subset selection, se identificó la combinación de rendimiento potencial en caña y pérdidas industriales totales, para predecir el servicio de provisión azucarera, mediante la regresión lineal múltiple. Se ajustó, también, un segundo modelo, que predice el efecto económico de las pérdidas industriales. En ambos modelos, se logró explicar por encima del 70 % de la variabilidad observada, en las variables dependientes, con un test F significativo (p-value: &lt; 0,05), además de cumplirse con las condiciones de diagnóstico y validación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The analysis of ecosystem services can provide important insights into how goods are processed and obtained from the sugar agro-industrial system. For this work, 346 data were collected on the industrial processing of sugarcane in three harvest, in the agroindustry of the Calimete municipality, Matanzas Province (Cuba), with the objective to use the machine learning algorithm, to predict both, biophysical and economic data. Seven predictors were analyzed and by best subset selection, it was identified both the potential yield in sugarcane and the total industrial losses combination to predict the sugar provision service, by multiple linear regression. In addition, it was adjusted a second model to predict the economic effect of the industrial losses. In both models were able to explain over 70 % of the variability observed, in the dependent variables, with a significant F test (p-value: &lt;0.05), also the diagnostic and validation conditions were met.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Industria]]></kwd>
<kwd lng="es"><![CDATA[Predicción]]></kwd>
<kwd lng="es"><![CDATA[Modelo]]></kwd>
<kwd lng="es"><![CDATA[Provisión]]></kwd>
<kwd lng="es"><![CDATA[Azúcar]]></kwd>
<kwd lng="en"><![CDATA[Industry: Prediction]]></kwd>
<kwd lng="en"><![CDATA[Model]]></kwd>
<kwd lng="en"><![CDATA[Provision]]></kwd>
<kwd lng="en"><![CDATA[Sugar]]></kwd>
</kwd-group>
</article-meta>
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