Abstract

This study investigates how artificial intelligence can enhance telecom network management by forecasting internet usage, predicting congestion, and identifying user behavior patterns from mobile phone activity data. The study made use of anonymized logs for calls, SMS and internet, and put up a multi-model analytical pipeline, which was composed of time-series forecasting (ARIMA, LSTM), clustering (K-Means), and classification (XGBoost), to perform the analysis. Among the time-series methods, ARIMA ranked first in the forecast performance (RMSE=0.31) and gave LSTM a convincing defeat in the case of this particular short and stable dataset. Based on K-Means segmentation, users were sorted into five behavioral groups according to their communication patterns, and each group had its own distinct profile of usage. In the case of the XGBoost classifier, when it was tested for predicting periods of high congestion, it scored 90.

Library of Congress Subject Headings

Cell phones--Data processing; Mobile communication systems--Management; User-centered system design; Predictive analytics; Machine learning

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Boutheina Tlili

Campus

RIT Dubai

Plan Codes

PROFST-MS

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