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  <titleInfo>
    <title>Mapping the Landscape of Artificial Intelligence Applications against COVID-19</title>
  </titleInfo>
  <name type="personal">
    <namePart>Bullock, Joseph</namePart>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="marc">periodical</genre>
  <originInfo>
    <dateIssued>2020</dateIssued>
    <issuance>continuing</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
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    <extent>14 p.; 1 documento PDF</extent>
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  <abstract>In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. We finish with a discussion of promising future directions of research and the tools and resources needed to facilitate AI research.</abstract>
  <note type="statement of responsibility">/ Joseph Bullock ... [et al.]</note>
  <note>Referencias bibliográficas: p. 11-14</note>
  <subject authority="lcsh">
    <topic>Inteligencia Artificial</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Sociedad digital</topic>
  </subject>
  <subject>
    <topic>COVID-19</topic>
  </subject>
  <subject>
    <topic>computers </topic>
  </subject>
  <subject>
    <topic>AI</topic>
  </subject>
  <subject>
    <topic>artificial intelligence</topic>
  </subject>
  <subject>
    <topic>machine learning </topic>
  </subject>
  <identifier type="uri">https://arxiv.org/pdf/2003.11336.pdf</identifier>
  <location>
    <url displayLabel="Acceso al documento">https://arxiv.org/pdf/2003.11336.pdf</url>
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    <recordCreationDate encoding="marc">200406</recordCreationDate>
    <recordChangeDate encoding="iso8601">20211004062551.0</recordChangeDate>
    <recordIdentifier source="ES-MaONT">00005885</recordIdentifier>
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