Research Article
Optimising performance indicators in the telecommunications sector
@ARTICLE{10.4108/eetismla.8717, author={Fiston Chrisnovic Balanganayi Kabutakapua and Pierre Kafunda Katalay and Simon Ntumba Badibanga and Eug\'{e}ne Mbuyi Mukendi}, title={Optimising performance indicators in the telecommunications sector}, journal={EAI Endorsed Transactions on Intelligent Systems and Machine Learning}, volume={1}, number={1}, publisher={EAI}, journal_a={ISMLA}, year={2025}, month={6}, keywords={performance in the telecommunication sector, neural network on performance indicators, supervised learning, multilayer neural network, public telecommunication sector}, doi={10.4108/eetismla.8717} }- Fiston Chrisnovic Balanganayi Kabutakapua
Pierre Kafunda Katalay
Simon Ntumba Badibanga
Eugène Mbuyi Mukendi
Year: 2025
Optimising performance indicators in the telecommunications sector
ISMLA
EAI
DOI: 10.4108/eetismla.8717
Abstract
INTRODUCTION: This study analyses and predicts performance in the public telecommunication sector using neural networks on key performance indicators in the telecommunication sector. OBJECTIVES: Although there are several key performance indicators in the telecommunications sector, we have selected a few and assessed their correlation with the variable to be predicted. In this study, we used supervised learning based on a multi-layer neural network. METHODS: The algorithm used in this study is the retro propagation algorithm because of its simplicity and accuracy of estimation. RESULTS: The results show that the selected indicators, including accessibility, maintainability, satisfaction, network operating cost and availability, explain more than 80% of the performance in the telecommunications sector, and the area of the ROC curve is equal to 0.97, which means that the classifier is almost perfect. This is also justified by the sensitivity and specificity, which are close to 1 when observing the ROC curve and the confusion matrix. The classification error found from the confusion matrix is equal to 1%, which means that our model has very high accuracy. CONCLUSION: The other indicators presented were not selected in the model because of their low correlation with the variable of interest and the difficulty of collecting the data.
Copyright © 2025 F. C. Balanganayi Kabutakapua et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
