December 1, 2019 Thesis Open Access

Tewodros Kibatu

DataCite XML Export

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  <identifier identifierType="DOI">10.20372/nadre:2123</identifier>
  <creators>
    <creator>
      <creatorName>Tewodros Kibatu</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Recurrent Neural Network-based Base Transceiver Station Power System  Failure Prediction</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-12-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Thesis</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/2123</alternateIdentifier>
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  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Global network infrastructures are increasing with the development of new technologies and growth in Internet traffic. As network infrastructures increases, maintaining and monitoring them will become very challenging since thousands of alarms are generated every day. Clearing those alarms by corrective maintenance activities require considerable effort and resources (car, labour, and budget).&lt;/p&gt;</description>
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