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Tommaso Barbariol
Tommaso Barbariol
Ph.D. Student, University of Padova
Verified email at phd.unipd.it
Title
Cited by
Cited by
Year
Self-diagnosis of multiphase flow meters through machine learning-based anomaly detection
T Barbariol, E Feltresi, GA Susto
Energies 13 (12), 3136, 2020
262020
A review of tree-based approaches for anomaly detection
T Barbariol, FD Chiara, D Marcato, GA Susto
Control Charts and Machine Learning for Anomaly Detection in Manufacturing …, 2022
252022
Machine learning approaches for anomaly detection in multiphase flow meters
T Barbariol, E Feltresi, GA Susto
IFAC-PapersOnLine 52 (11), 212-217, 2019
172019
TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios
T Barbariol, GA Susto
Information Sciences 610, 126-143, 2022
142022
Uncertainty estimation for machine learning models in multiphase flow applications
L Frau, GA Susto, T Barbariol, E Feltresi
Informatics 8 (3), 58, 2021
62021
A machine learning-based system for self-diagnosis multiphase flow meters
T Barbariol, E Feltresi, GA Susto
International Petroleum Technology Conference, D021S042R003, 2020
52020
Classifying circumnutation in pea plants via supervised machine learning
Q Wang, T Barbariol, GA Susto, B Bonato, S Guerra, U Castiello
Plants 12 (4), 965, 2023
32023
A revised isolation forest procedure for anomaly detection with high number of data points
E Marcelli, T Barbariol, V Savarino, A Beghi, GA Susto
2022 IEEE 23rd Latin American Test Symposium (LATS), 1-5, 2022
22022
Validity and consistency of MPFM data through a Machine Learning-based system
T Barbariol, E Feltresi, GA Susto
Proceeding 37th North Sea Flow measurement Wor shop, 2019
22019
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems
E Marcelli, T Barbariol, GA Susto
arXiv preprint arXiv:2207.03934, 2022
12022
Sensor fusion and machine learning techniques to improve water cut measurements accuracy in multiphase application
T Barbariol, E Feltresi, GA Susto, D Tescaro, S Galvanin
SPE Annual Technical Conference and Exhibition?, D022S061R003, 2020
12020
Bayesian active learning isolation forest (B-ALIF): A weakly supervised strategy for anomaly detection
D Sartor, T Barbariol, GA Susto
Engineering Applications of Artificial Intelligence 130, 107671, 2024
2024
Improving Anomaly Detection for Industrial Applications
T Barbariol
Universitą degli studi di Padova, 2023
2023
Unveiling Circumnutation in Pea Plants via Supervised Machine Learning
Q Wang, T Barbariol, GA Susto, B Bonato, S Guerra, U Castiello
Preprints, 2023
2023
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
T Barbariol, D Masiero, E Feltresi, GA Susto
arXiv preprint arXiv:2301.00014, 2022
2022
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