Research Article
Machine learning based Quality of Experience (QoE) Prediction Approach in Enterprise Multimedia Networks
@INPROCEEDINGS{10.4108/eai.24-11-2022.2329806, author={H. Omar Hamidou and Justin P. Kouraogo and Oumarou Sie and David Tapsoba}, title={Machine learning based Quality of Experience (QoE) Prediction Approach in Enterprise Multimedia Networks}, proceedings={Proceedings of the 5th edition of the Computer Science Research Days, JRI 2022, 24-26 November 2022, Ouagadougou, Burkina Faso}, publisher={EAI}, proceedings_a={JRI}, year={2023}, month={5}, keywords={quality of experience (qoe) machine learning (lm) opinion score (mos)}, doi={10.4108/eai.24-11-2022.2329806} }- H. Omar Hamidou
Justin P. Kouraogo
Oumarou Sie
David Tapsoba
Year: 2023
Machine learning based Quality of Experience (QoE) Prediction Approach in Enterprise Multimedia Networks
JRI
EAI
DOI: 10.4108/eai.24-11-2022.2329806
Abstract
In this paper, we discuss quality of experience in multimedia networks. We present an architecture and a survey of machine learning methods to predict the quality of experience in an enterprise multimedia network environment. Our approach is based on subjective methods. It consists of the use PRTG (Paessler Router Traffic Grapher) for QoS (quality of service) data collection and Google Forms for the different users of the network MOS (Minimum Score Opinion) parameters collection. We then implement different supervised machine learning schemes using the data collected, and finally analyze their performance. We compare two classes of algorithms namely regression algorithms and classification algorithms. The Random Forest Classifier in the second class algorithm give the best results.
