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[18]
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Alessandro Rocchi, Andrea Chiozzi, Marco Nale, Zeljana Nikolic, Fabrizio
Riguzzi, Luana Mantovan, Alessandro Gilli, and Elena Benvenuti.
A machine learning framework for multi-hazard risk assessment at the
regional scale in earthquake and flood-prone areas.
Applied Sciences, 12(2), 2022.
[ bib |
DOI |
http ]
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[17]
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Damiano Azzolini, Elena Bellodi, Stefano Ferilli, Fabrizio Riguzzi, and
Riccardo Zese.
Abduction with probabilistic logic programming under the distribution
semantics.
International Journal of Approximate Reasoning, 142:41--63,
2022.
[ bib |
DOI |
http ]
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[16]
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Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi.
Symbolic DNN-Tuner: A Python and ProbLog-based system for
optimizing deep neural networks hyperparameters.
SoftwareX, 17:100957, 2022.
[ bib |
DOI |
http ]
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[15]
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Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini,
Giovanni Bechini, Giuseppe Cota, and Fabrizio Riguzzi.
Prediction of gas turbine trip: A novel methodology based on random
forest models.
Journal of Engineering for Gas Turbines and Power, 144(3),
2022.
GTP-21-1324.
[ bib |
DOI ]
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[14]
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Damiano Azzolini, Fabrizio Riguzzi, Elena Bellodi, and Evelina Lamma.
A probabilistic logic model of lightning network.
In Witold Abramowicz, Sören Auer, and Milena Stróżyna,
editors, Business Information Systems Workshops, Lecture Notes in
Business Information Processing (LNBIP), pages 321--333, Cham, Switzerland,
2022. Springer International Publishing.
[ bib |
DOI |
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.pdf ]
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[13]
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Arnaud Nguembang Fadja, Fabrizio Riguzzi, Giorgio Bertorelle, and Emiliano
Trucchi.
Identification of natural selection in genomic data with deep
convolutional neural network.
BioData Mining, 14(1):51, 2021.
[ bib |
DOI ]
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[12]
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Michele Fraccaroli, Evelina Lamma, and Fabrizio Riguzzi.
Symbolic DNN-Tuner.
Machine Learning, © Springer, 2021.
[ bib |
DOI ]
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[11]
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Arnaud Nguembang Fadja, Fabrizio Riguzzi, and Evelina Lamma.
Learning hierarchical probabilistic logic programs.
Machine Learning, 110(7):1637--1693, © Springer,
2021.
[ bib |
DOI |
.pdf ]
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[10]
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Damiano Azzolini and Fabrizio Riguzzi.
Syntactic requirements for well-defined hybrid probabilistic logic
programs.
In Andrea Formisano, Yanhong Annie Liu, Bart Bogaerts, Alex Brik,
Veronica Dahl, Carmine Dodaro, Paul Fodor, Gian Luca Pozzato, Joost
Vennekens, and Neng-Fa Zhou, editors, Proceedings 37th International
Conference on Logic Programming (Technical Communications), pages 14--26,
Waterloo, Australia, 2021. © by the authors, Open Publishing
Association.
[ bib |
DOI |
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.pdf ]
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[9]
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Damiano Azzolini, Fabrizio Riguzzi, and Evelina Lamma.
Summary of semantics for hybrid probabilistic logic programs with
function symbols.
In Andrea Formisano, Yanhong Annie Liu, Bart Bogaerts, Alex Brik,
Veronica Dahl, Carmine Dodaro, Paul Fodor, Gian Luca Pozzato, Joost
Vennekens, and Neng-Fa Zhou, editors, Proceedings 37th International
Conference on Logic Programming (Technical Communications), pages 234--235,
Waterloo, Australia, 2021. © by the authors, Open Publishing
Association.
[ bib |
DOI |
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.pdf ]
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[8]
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Damiano Azzolini and Fabrizio Riguzzi.
Optimizing probabilities in probabilistic logic programs.
Theory and Practice of Logic Programming, 21(5):543--556,
© Cambridge University Press, 2021.
[ bib |
DOI |
http |
.pdf ]
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[7]
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Damiano Azzolini and Fabrizio Riguzzi.
Reducing probabilistic logic programs.
In Ahmet Soylu, Alireza Tamaddoni Nezhad, Nikolay Nikolov, Ioan Toma,
Anna Fensel, and Joost Vennekens, editors, Proceedings of the 15th
International Rule Challenge, 7th Industry Track, and 5th Doctoral Consortium
at RuleML+RR 2021 co-located with 17th Reasoning Web Summer School (RW 2021)
and 13th DecisionCAMP 2021 as part of Declarative AI 2021, CEUR Workshop
Proceedings, pages 1--13, Aachen, Germany, 2021. © By the
authors, Sun SITE Central Europe.
[ bib |
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.pdf ]
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[6]
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Damiano Azzolini, Fabrizio Riguzzi, and Evelina Lamma.
A semantics for hybrid probabilistic logic programs with function
symbols.
Artificial Intelligence, 294:103452, © Elsevier,
2021.
The final publication is available at Elsevier via
https://doi.org/10.1016/j.artint.2021.103452.
[ bib |
DOI |
.pdf ]
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[5]
|
Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini,
Giovanni Bechini, Giuseppe Cota, and Fabrizio Riguzzi.
Structured methodology for clustering gas turbine transients by means
of multi-variate time series.
Journal of Engineering for Gas Turbines and Power,
143(3):031014--1 (13 pages), 2021.
[ bib |
DOI ]
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[4]
|
Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini,
Giovanni Bechini, Giuseppe Cota, and Fabrizio Riguzzi.
Data selection and feature engineering for the application of machine
learning to the prediction of gas turbine trip.
In Proceedings of the ASME Turbo Expo 2021: Turbomachinery
Technical Conference and Exposition, June 7–11, 2021 Virtual, Online,
volume 8, page V008T20A004. ASME, 2021.
[ bib |
DOI ]
|
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[3]
|
Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini,
Giovanni Bechini, Giuseppe Cota, and Fabrizio Riguzzi.
Prediction of gas turbine trip: a novel methodology based on random
forest models.
In Proceedings of the ASME Turbo Expo 2021: Turbomachinery
Technical Conference and Exposition, June 7–11, 2021 Virtual, Online,
volume 8, page V008T20A005. ASME, 2021.
[ bib |
DOI ]
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[2]
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Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese, Marco Alberti, and Evelina
Lamma.
Probabilistic inductive constraint logic.
Machine Learning, 110:723--754, 2021.
[ bib |
DOI |
.pdf ]
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[1]
|
Elena Bellodi, Marco Gavanelli, Riccardo Zese, Evelina Lamma, and Fabrizio
Riguzzi.
Nonground abductive logic programming with probabilistic integrity
constraints.
Theory and Practice of Logic Programming, 21(5):557--574,
© Cambridge University Press, 2021.
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DOI |
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.pdf ]
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