February 1, 2020 Thesis Open Access

digis weldu

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  <identifier identifierType="DOI">10.20372/nadre:1973</identifier>
  <creators>
    <creator>
      <creatorName>digis weldu</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Machine Learning Based QoE Estimation Model for Video Streaming over UMTS Network</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-02-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Thesis</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/1973</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.20372/nadre:1972</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/aau</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/zenodo</relatedIdentifier>
  </relatedIdentifiers>
  <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;The advent of data-intensive services needs quality Internet services. This in turn, makes Quality&lt;br&gt;
of Experience (QoE) gain prominent recognition in the telecommunications industry. Ethio telecom&lt;br&gt;
uses network Quality of Service (QoS) monitoring data obtained from Network Management&lt;br&gt;
Systems (NMS) tools to comprehend its network performances. However, as QoS measurement&lt;br&gt;
refers to network performances, this method does not generally give QoE data as perceived&lt;br&gt;
by the user. Therefore, QoE estimation models are proposed as solutions in the literature,&lt;br&gt;
recently.&lt;br&gt;
This study focuses on developing QoE estimation models using QoS features of round-trip time&lt;br&gt;
(RTT), jitter, loss rate (LR) and throughput, and QoE scores collected using Application for prediCting&lt;br&gt;
QUality of experience at Interne Access (ACQUA)-based crowdsourcing in Universal&lt;br&gt;
Mobile Telecommunication Systems (UMTS) networks in a real-time basis. Data preparations&lt;br&gt;
techniques such as data cleaning and dataset imbalance corrections have been applied to the&lt;br&gt;
collected datasets. Machine Learning (ML) algorithms of Articial Neural Network (ANN), KNearest&lt;br&gt;
Neighbor (KNN) and Random Forest (RF) are selected based on their suitability for multilabel&lt;br&gt;
problems. After training these models developed, they are evaluated using commonly used&lt;br&gt;
performance metrics such as accuracy, Root Mean Square Error (RMSE) and Receiver Operating&lt;br&gt;
Characteristics (ROC).&lt;/p&gt;</description>
  </descriptions>
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