Despite the increased exploration of machine learning (ML) techniques for the realization of
autonomous optical networks, less attention has been paid to data quality, which is critical …

The uneven occurrence of certain types of failures in optical networks results in a scarcity of
data for less frequent failures, leading to imbalanced datasets for training machine learning …

Clustering of live network alarms using unsupervised statistical models Page 1 Clustering of
Live Network Alarms Using Unsupervised Statistical Models Diane Maillot-Tchofo(1,2), Ahmed …

Failure management in optical networks usually deals with the processing of alarms,
including alarm classification. The alarms data obtained from network management systems …

We compare model-centric and data-centric machine learning (ML) approaches to address
the issue of insufficient training data for ML-based failure identification. The results suggest …

In recent times, machine learning (ML) and deep learning (DL) algorithms have been
integrated into existing control and management tools of optical networks but their …

Despite demonstrating exceptional performance in optical networks, neural networks often
receive criticism due to their significant computational complexity. To address this, we …