A model for recommending suitable LSP for improved service delivery in mobile network using affinity propagation machine learning

Abstract

Abstract Network service request for voice and Internet may differ across locations. Network service providers are encouraged to conduct a quarterly check to identify the suitable network service request peculiar to locations of coverage so as to improve quality of service. In this work, a model that identifies and recommends the suitable location service plan for network providers is proffered. The 3-task model extracts data using quarterly averages, clusters the extracted data using affinity propagation machine learning and classifies the clusters into linguistic variables using the mean of the respective clusters. Using a dataset obtained from the Nigerian Bureau of Statistics on mobile telecommunication for three quarters in 2021, obtained results show that the model was able to identify states with heavy as well as low subscription rates across the states. Mtn, Airtel and Glo mobile network providers recorded equal voice and internet subscription rates across the states while 9Mobile showed signs of improvement in voice and Internet subscription for some states.