<?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>2007-3607</journal-id>
<journal-title><![CDATA[PAAKAT: revista de tecnología y sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[PAAKAT: rev. tecnol. soc.]]></abbrev-journal-title>
<issn>2007-3607</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Guadalajara, Sistema de Universidad Virtual]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-36072025000100005</article-id>
<article-id pub-id-type="doi">10.32870/pk.a15n28.897</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Desarrollo de Software dedicado a la traducción de la Lengua Mexicana de Señas mediante Deep Learning y Machine Learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Software Development Dedicated to the Translation of the Mexican Sign Language through Deep Learning and Machine Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Caballero Hernández]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Muñoz Jiménez]]></surname>
<given-names><![CDATA[Vianney]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramos Corchado]]></surname>
<given-names><![CDATA[Marco Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma del Estado de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>15</volume>
<numero>28</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-36072025000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-36072025000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-36072025000100005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Existen diversas propuestas para abordar la interpretación de las lenguas de señas (LS) a lenguajes escritos y hablados, empleando desarrollos basados en dispositivos electrónicos y en técnicas de visión computacional. Entre los problemas más comunes para la interpretación de las LS se encuentra la falta de conjuntos de datos estandarizados para generar modelos de interpretación de LS y la ausencia de software multiplataforma. Esta investigación presenta el desarrollo de un software para equipos móviles y de sobremesa dedicado a la interpretación de la Lengua de Señas Mexicana (LSM). Adicionalmente, el software tiene la función de detección de objetos que se encuentren en espacios como casa-habitación y oficinas para su traducción a la LSM y a texto en español para facilitar la integración del vocabulario de la LSM, tanto como para personas sordomudas y sin discapacidad auditiva o del habla. Por otro lado, se ha desarrollado un conjunto de datos para la LSM con 42 clases, basado en distintos escenarios de 30 participantes e imágenes provenientes de internet para desarrollar una red neuronal convolucional (RNC) con el algoritmo YOLOv8 y un modelo de reconocimiento de Machine Learning (ML) mediante MediaPipe, el cual pude ser actualizado a través de un servicio en la nube. Los resultados para la RCN dedicada a la LSM muestran 90% de mAP50 y para el modelo ML un accuracy de 0.817. La RNC dedicada a la traducción de objetos al español consta de 79 clases provenientes del conjunto de datos COCO.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Several proposals are currently addressing the interpretation of sign language (SL) into written and spoken languages, using developments based on electronic devices and computer vision techniques. Among the most common problems for interpreting SL is the lack of standardized data sets to generate SL interpretation models and the absence of multiplatform software. This research presents software development for mobile and desktop devices dedicated to interpreting Mexican Sign Language (MSL). Additionally, the software can detect objects found in spaces such as homes and offices for their translation into MSL and Spanish text to help the integration of MSL vocabulary for both deaf-mute people and people without hearing or speech disabilities. On the other hand, a dataset for the MSL with 42 classes has been developed based on different scenarios of 30 participants and images from the Internet to develop a convolutional neural network (CNN) with the YOLOv8 algorithm, and a Machine Learning (ML) recognition model using MediaPipe, which can be updated through a cloud service. The results for the CNN dedicated to the LSM show a 90% mAP50, and for the ML model the accuracy is 0.817. The CNN dedicated to the translation of objects into Spanish consists of 79 classes from the COCO dataset.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[conjunto de datos]]></kwd>
<kwd lng="es"><![CDATA[visión computacional]]></kwd>
<kwd lng="es"><![CDATA[personas sordomudas]]></kwd>
<kwd lng="es"><![CDATA[discapacidad]]></kwd>
<kwd lng="es"><![CDATA[LSM]]></kwd>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[dataset]]></kwd>
<kwd lng="en"><![CDATA[computer vision]]></kwd>
<kwd lng="en"><![CDATA[deaf and mute people]]></kwd>
<kwd lng="en"><![CDATA[disability]]></kwd>
<kwd lng="en"><![CDATA[MSL]]></kwd>
</kwd-group>
</article-meta>
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