Qualitative Analysis of QoE Based on Machine Learning Methods

This study explores the use of machine learning algorithms to evaluate the Quality of Experience (QoE) for Voice over Internet Protocol (VoIP) users. It emphasizes the significance of considering personal perspectives in assessing communication experiences and examines how language affects QoE. The research will conduct a comparative analysis of algorithm efficiency, including RandomForestClassifier, DecisionTreeClassifier, Linear Regression, DecisionTreeRegressor, RandomForestRegressor, Naive Bayes, and K-Nearest Neighbors. Objective and subjective metrics, such as noise levels, average PESQ, and user feedback, will be used to provide comprehensive insights. The study aims to help providers optimize their networks for reliable, high-quality services.

Keywords:

Quality of experience (QoE) , Voice over Internet protocol (VoIP) , Machine Learning (ML)