Skip to content

Alice Bizzarri

Alice Bizzarri's photo
PhD Student - Dottorato Nazionale in Intelligenza Artificiale per Industria 4.0 - "Neural symbolic computation for Industry 4.0"
Dipartimento di Ingegneria, Università di Ferrara,

Laboratorio 331
Blocco A, Polo Scientifico Tecnologico
University of Ferrara
Via Saragat 1, 44122, Ferrara, Italy
E-Mail: alice.bizzarri@unife.it
Ricevimento: su appuntamento richiesto via email o telefono..

Publications

2024

[4] Alice Bizzarri, Brian Jalaian, Fabrizio Riguzzi, and Nathaniel D. Bastian. A neuro-symbolic artificial intelligence network intrusion detection system. In International Conference on Computer Communications and Networks, ICCCN, 2024. [ bib | DOI ]
[3] Damiano Azzolini, Alice Bizzarri, Michele Fraccaroli, Francesco Bertasi, and Evelina Lamma. A machine learning pipeline to analyse multispectral and hyperspectral images: Full/regular research paper (csci-rthi). In 2023 International Conference on Computational Science and Computational Intelligence (CSCI), pages 1306--1311, 2023. [ bib | DOI ]
[2] Alice Bizzarri, Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi. Integration between constrained optimization and deep networks: a survey. Frontiers in Artificial Intelligence, 7, 2024. [ bib | DOI | http ]
[1] Niccolò Ferrari, Nicola Zanarini, Michele Fraccaroli, Alice Bizzarri, and Evelina Lamma. Integration of deep generative anomaly detection algorithm in high-speed industrial line. 2024. [ bib | http ]

2023

[3] Elisabetta Gentili, Alice Bizzarri, Damiano Azzolini, Riccardo Zese, and Fabrizio Riguzzi. Regularization in probabilistic inductive logic programming. In Elena Bellodi, Francesca Alessandra Lisi, and Riccardo Zese, editors, Inductive Logic Programming - ILP 2023, volume 14363 of Lecture Notes in Computer Science, pages 16--29, Cham, 2023. Springer Nature Switzerland. [ bib | DOI | http ]
[2] Michele Fraccaroli, Alice Bizzarri, Paolo Casellati, and Evelina Lamma. Exploiting cnn's visual explanations to drive anomaly detection. Applied Intelligence, 2023. [ bib | DOI | http ]
[1] Elena Bellodi, Davide Bertozzi, Alice Bizzarri, Michele Favalli, Michele Fraccaroli, and Riccardo Zese. Efficient resource-aware neural architecture search with a neuro-symbolic approach. pages 171--178, 2023. [ bib | DOI ]

2022

[2] Michele Fraccaroli, Alice Bizzarri, Paolo Casellati, and Evelina Lamma. Cross entropy overlap distance. Accepted and Presented at ITAL-IA 2022, workshop on AI for Industry, feb 2022. [ bib | .pdf ]
[1] Arnaud Nguembang Fadja, Michele Fraccaroli, Alice Bizzarri, Giulia Mazzuchelli, and Evelina Lamma. Neural-symbolic ensemble learning for early-stage prediction of critical state of covid-19 patients. Medical & Biological Engineering & Computing, 2022. [ bib | DOI | http ]

2021

[1] Michele Fraccaroli, Giulia Mazzucchelli, and Alice Bizzarri. Machine learning techniques for extracting relevant features from clinical data for COVID-19 mortality prediction. In 2021 Symposium on Computers and Communications (ISCC): 26th IEEE Symposium on Computers and Communications - Workshop on ICT Solutions for eHealth (ICTS4eHealth) (ICTS4eHealth2021), pages 1--7, Athens, Greece, September 2021. [ bib | DOI ]