Conference Paper

Base station anomaly prediction leveraging model-driven framework for classification in Neo4j

Published in: International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)

Jul 12, 2022

Nenad Petrovic Issam Al-Azzoni Dragana Krstic Abdullah Alqahtani

Machine learning is one of key-enablers in case of novel usage scenarios and adaptive behavior within next generation mobile networks. In this paper, it is examined how model-driven approach can be adopted to automatize machine learning tasks aiming mobile network data analysis. The framework is evaluated on classification task for purpose of base station anomaly detection relying on Neo4j graph database. According to the experiments performed on publicly available dataset, such approach shows ...


Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

Published in: Mathematical Problems in Engineering

Apr 08, 2022

Dragana Krstic Nenad Petrovic / Issam Al-Azzoni

Efficient resource planning is recognized as one of the key enablers making the large-scale deployment of next-generation wireless networks available for mass usage. Modelling, planning, and software simulation tools reduce both the time needed and costs of their tuning and realization. In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. The predictions about service demand and energy consumption ...

Conference Paper

Model-Driven Approach to COVID-19 Vaccination Planning Leveraging Multi-Objective Optimization and Deep Learning

Published in: Small Systems Simulation Symposium (SSSS)

Feb 28, 2022

Nenad Petrovic / Issam Al-Azzoni

Vaccination is recognized as one of crucial measures in battle against COVID-19, contributing to both the reduction of its negative impact on infected person and overall spread reduction. In this paper, we focus on adoption of model-driven approach to proactive and cost-effective vaccine distribution, relying on deep-learning (for vaccine-demand predictions) and multi-objective optimization (for solving the allocation problem). As outcome, software simulation tool for efficient vaccination plan...

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