February 1, 2020 Thesis Open Access

digis weldu

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    <subfield code="a">Machine Learning Based QoE Estimation Model for Video Streaming over UMTS Network</subfield>
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    <subfield code="a">&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;</subfield>
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