Quality of Experience Modeling for Fixed Broadband Internet Using Machine Learning Algorithms
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Abayneh Mekonnen | |
| dc.date.accessioned | 2024-09-03T08:24:51Z | |
| dc.date.available | 2024-09-03T08:24:51Z | |
| dc.date.issued | 2024-04 | |
| dc.description.abstract | As the demand for dependable fixed broadband internet services continues to grow, ensuring an excellent Quality of Experience (QoE) for end-users is essential. This thesis centers on QoE modeling, employing advanced machine learning techniques, specifically Support Vector Machine (SVM) and Random Forest algorithms. The study utilizes subjective assessments and Quality of Service (QoS) metrics, including latency, upload speed, download speed, uptime, packet loss, and jitter, to comprehensively comprehend and model the factors influencing user satisfaction. The research incorporates an exhaustive feature selector to extract pertinent features from the dataset, enhancing the precision of the models. Hyperparameter optimization is carried out through a Grid Search approach to fine-tune the models for optimal performance. To assess the models, a robust cross-validation methodology is implemented. The results indicate that SVM surpasses Random Forest in QoE modeling for Virtual Internet Service Providers (vISPs) like Websprix and ZERGAW Cloud with average accuracy score of 92% and 70% respectively. Conversely, Random Forest proves to be the more suitable model for predicting QoE in the case of the national ISP, ethio telecom with average accuracy value of 88%. This comparative performance analysis offers valuable insights into the distinct strengths of each model for different service providers. The research findings also indicate that employing both subjective and QoS metrics in combination to model the user QoE yields superior model performance and predictive outcomes compared to relying solely on subjective assessments and QoS metrics. These findings contribute to the ongoing discussion on QoE enhancement in fixed broadband internet services, providing practical recommendations for service providers based on observed model performances. The application of machine learning, feature selection, and hyperparameter optimization techniques underscores the importance of these methodologies in customizing QoE models to specific service contexts, ultimately enhancing user satisfaction in diverse fixed broadband Internet environments. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/3463 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | QoE | |
| dc.subject | QoS | |
| dc.subject | Support Vector Machine | |
| dc.subject | Random Forest | |
| dc.subject | vISP | |
| dc.subject | Fixed Broadband Internet | |
| dc.subject | Machine Learning Algorithm | |
| dc.title | Quality of Experience Modeling for Fixed Broadband Internet Using Machine Learning Algorithms | |
| dc.type | Thesis |