Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial
capabilities in solving a wide range of optical-network-related tasks such as fault …

Uncertainty in quality-of-transmission (QoT) estimation is traditionally addressed through
empirical, myopic margins, ignoring the fact that each unseen lightpath is subject to different …

Quality-of-transmission (QoT) estimation of unestablished lightpaths has been extensively
studied in the literature through the development of linear physical layer models (PLMs) and …

We demonstrate sophisticated laboratory automation and data pipeline capable of
generating large, diverse, and high-quality public datasets. The demo covers the full …

Generally, the quality of artificial intelligence (AI) models depends on the training dataset.
Therefore, evaluating dataset quality is crucial. Datasets contain considerable attribute …

Quantifying Features’ Contribution for ML-based Quality-of-Transmission Estimation using
Explainable AI Page 1 Quantifying Features’ Contribution for ML-based Quality-of-Transmission …

In recent years, artificial intelligence/machine learning (AI/ML) has played a significant role
in automating optical networks. Despite this, the methods for creating, deploying, and …

Machine-Learning-as-a-Service for Optical Network Automation Page 1 Machine-Learning-as-a-Service
for Optical Network Automation Carlos Natalino,1,* Nasser Mohammadiha,2,3 Ashkan Panahi3 1 …

The recent popularity of using deep learning models for the forecasting of time series calls
for methods to not only predict the target but also measure the uncertainty of the prediction …

We propose a novel approach to perform QoT estimation relying on joint exploitation of
machine learning and analytical formula that offers accurate estimation when applied to …