Antonio Napoli

Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection

Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro, Sasipim Srivallapanondh, Antonio Napoli, Sergei K. Turitsyn, et al.

We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-based selective labeling, our method achieves nearceiling accuracy and AUC scores while querying only 3.4% of streaming samples, wi…Read more

Resilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring

Gulmina Malik, Imran Chowdhury Dipto, Muhammad Umar Masood, Mashboob Cheruvakkadu Mohamed, Stefano Straullu, Sai Kishore Bhyri, et al.

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) ove…Read more

Reinforcement Learning to Enhance Digital Twin-Based EDFA Fault Prediction in Optical Networks

Mashboob Cheruvakkadu Mohamed, Renato Ambrosone, Muhammad Umar Masood, Gulmina Malik, Stefano Straullu, Antonino Nespola, et al.