Machine Learning Techniques for Extracting Relevant Features from Clinical Data for COVID-19 Mortality Prediction. - Michele Fraccaroli; Giulia Mazzuchelli; Alice Bizzarri (ISCC, 2021)

Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients. - Arnaud Nguembang Fadja; Michele Fraccaroli; Alice Bizzarri; Giulia Mazzuchelli; Evelina Lamma (Medical Biol. Eng. Comput., 2022)

Regularization in Probabilistic Inductive Logic Programming. - Elisabetta Gentili; Alice Bizzarri; Damiano Azzolini; Riccardo Zese; Fabrizio Riguzzi (ILP, 2023)

Efficient Resource-Aware Neural Architecture Search with a Neuro-Symbolic Approach. - Elena Bellodi; Davide Bertozzi; Alice Bizzarri; Michele Favalli; Michele Fraccaroli; Riccardo Zese (MCSoC, 2023)

Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection. - Alice Bizzarri; Chung-En Yu; Brian Jalaian; Fabrizio Riguzzi; Nathaniel D. Bastian (AI*IA, 2024)

A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System. - Alice Bizzarri; Brian Jalaian; Fabrizio Riguzzi; Nathaniel D. Bastian (ICCCN, 2024)

A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI. - Alice Bizzarri; Chung-En Yu; Brian Jalaian; Fabrizio Riguzzi; Nathaniel D. Bastian (CoRR, 2024)

Integration between constrained optimization and deep networks: a survey. - Alice Bizzarri; Michele Fraccaroli; Evelina Lamma; Fabrizio Riguzzi (Frontiers Artif. Intell., 2024)

Exploiting CNN's visual explanations to drive anomaly detection. - Michele Fraccaroli; Alice Bizzarri; Paolo Casellati; Evelina Lamma (Appl. Intell., 2024)

Neurosymbolic AI for network intrusion detection systems: A survey. - Alice Bizzarri; Chung-En Johnny Yu; Brian Jalaian; Fabrizio Riguzzi; Nathaniel D. Bastian (J. Inf. Secur. Appl., 2025)