Comparative Analyses of Machine Learning Paradigms for Operators’ Voice Call Quality of Service

Abstract

Mobile network operators render data, audio calls, and multimedia services to their customers. Though operators enjoy patronage, the number of customers increases over limited resources, the challenge of value for money paid becomes the concern of MNOs, and their customers. A significant weakness in the researchers’ assessment of operators’ voice call services is lack of performance evaluation on models developed base on a data set covering a broader area. This paper carried out the comparative performance analyses of operators’ audio quality of service (QoS) using the six machine language algorithms. The crowdsourcing approach was used to captutre desired data. The results of comparative analyses shows that in terms of accuracy, ID3 ranked first to be followed by support vector machine (SVM), C4.5, neural network while Fuzzy and adaptive neuro fuzzy inference system (ANFIS) ranked fifth and sixth, respectively. The precision result shows that ID3, SVM, ANFIS, and C4.5 ranked first, second, third, and fourth, respectively. In terms of overall ranking, ID3 demonstrated most superior algorithm because it ranked first, SVM, C4.5, ANFIS ranked second, third and fourth, respectively. Evaluated machine learning-based models will have a positive impact on the service delivery of telecommunication network operators.