BIZZARRI, ALICE

BIZZARRI, ALICE  

Dipartimento di Automatica e Informatica  

091588  

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Citazione Data di pubblicazione Autori File
A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System / Bizzarri, Alice; Jalaian, Brian; Riguzzi, Fabrizio; Bastian, Nathaniel D.. - (2024), pp. 1-9. (Intervento presentato al convegno 2024 33rd International Conference on Computer Communications and Networks (ICCCN) tenutosi a Kailua-Kona, HI (USA) nel 29-31 July 2024) [10.1109/icccn61486.2024.10637618]. 1-gen-2024 Bizzarri, Alice + A_Neuro-Symbolic_Artificial_Intelligence_Network_Intrusion_Detection_System.pdf
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI / Bizzarri, Alice; Yu, Chung-En; Jalaian, Brian; Riguzzi, Fabrizio; Bastian, Nathaniel D.. - (2024). 1-gen-2024 Alice Bizzarri + 2406.00938v1.pdf
Exploiting CNN’s visual explanations to drive anomaly detection / Fraccaroli, Michele; Bizzarri, Alice; Casellati, Paolo; Lamma, Evelina. - In: APPLIED INTELLIGENCE. - ISSN 0924-669X. - 54:(2024), pp. 414-427. [10.1007/s10489-023-05177-0] 1-gen-2024 Alice Bizzarri + s10489-023-05177-0.pdf
Integration between constrained optimization and deep networks: a survey / Bizzarri, Alice; Fraccaroli, Michele; Lamma, Evelina; Riguzzi, Fabrizio. - In: FRONTIERS IN ARTIFICIAL INTELLIGENCE. - ISSN 2624-8212. - 7:(2024). [10.3389/frai.2024.1414707] 1-gen-2024 Bizzarri, Alice + frai-07-1414707.pdf
Integration of Deep Generative Anomaly Detection Algorithm in High-Speed Industrial Line / Ferrari, Niccolò; Zanarini, Nicola; Fraccaroli, Michele; Bizzarri, Alice; Lamma, Evelina. - (2024). [10.2139/ssrn.4858664] 1-gen-2024 Bizzarri, Alice + ssrn-4858664.pdf
A Machine Learning Pipeline to Analyse Multispectral and Hyperspectral Images: Full/Regular Research Paper (CSCI-RTHI) / Azzolini, Damiano; Bizzarri, Alice; Fraccaroli, Michele; Bertasi, Francesco; Lamma, Evelina. - (2023), pp. 1306-1311. (Intervento presentato al convegno 2023 International Conference on Computational Science and Computational Intelligence (CSCI) tenutosi a Las Vegas, NV (USA) nel 13-15 December 2023) [10.1109/csci62032.2023.00216]. 1-gen-2023 Bizzarri, Alice + A_Machine_Learning_Pipeline_to_Analyse_Multispectral_and_Hyperspectral_Images_Full_Regular_Research_Paper_CSCI-RTHI.pdf
Efficient Resource-Aware Neural Architecture Search with a Neuro-Symbolic Approach / Bellodi, Elena; Bertozzi, Davide; Bizzarri, Alice; Favalli, Michele; Fraccaroli, Michele; Zese, Riccardo. - STAMPA. - (2023), pp. 171-178. (Intervento presentato al convegno 2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip tenutosi a Singapore, Singapore nel 18-21 December 2023) [10.1109/MCSoC60832.2023.00034]. 1-gen-2023 Alice Bizzarri + main.pdfEfficient_Resource-Aware_Neural_Architecture_Search_with_a_Neuro-Symbolic_Approach.pdf
Regularization in Probabilistic Inductive Logic Programming / Gentili, Elisabetta; Bizzarri, Alice; Azzolini, Damiano; Zese, Riccardo; Riguzzi, Fabrizio. - 14363:(2023), pp. 16-29. (Intervento presentato al convegno Inductive Logic Programming 32nd International Conference, ILP 2023 tenutosi a Bari (ITA) nel November 13–15, 2023) [10.1007/978-3-031-49299-0_2]. 1-gen-2023 Bizzarri, Alice + 978-3-031-49299-0_2.pdf
Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients / Fadja, Arnaud Nguembang; Fraccaroli, Michele; Bizzarri, Alice; Mazzuchelli, Giulia; Lamma, Evelina. - In: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING. - ISSN 0140-0118. - 60:(2022), pp. 3461-3474. [10.1007/s11517-022-02674-1] 1-gen-2022 Bizzarri, Alice + s11517-022-02674-1.pdf
Machine Learning Techniques for Extracting Relevant Features from Clinical Data for {COVID}-19 Mortality Prediction / Fraccaroli, Michele; Mazzuchelli, Giulia; Bizzarri, Alice. - (2021), pp. 1-7. (Intervento presentato al convegno 2021 IEEE Symposium on Computers and Communications (ISCC) nel 05-08 September 2021) [10.1109/iscc53001.2021.9631477]. 1-gen-2021 Alice Bizzarri + Machine_Learning_Techniques_for_Extracting_Relevant_Features_from_Clinical_Data_for_COVID-19_Mortality_Prediction.pdf