| 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 …, 2021 | 58 | 2021 |
| Self-diagnosis of multiphase flow meters through machine learning-based anomaly detection T Barbariol, E Feltresi, GA Susto Energies 13 (12), 3136, 2020 | 40 | 2020 |
| TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios T Barbariol, GA Susto Information Sciences 610, 126-143, 2022 | 32 | 2022 |
| Machine learning approaches for anomaly detection in multiphase flow meters T Barbariol, E Feltresi, GA Susto IFAC-PapersOnLine 52 (11), 212-217, 2019 | 26 | 2019 |
| Active Learning-based Isolation Forest (ALIF): Enhancing anomaly detection with expert feedback E Marcelli, T Barbariol, D Sartor, GA Susto Information Sciences 678, 121012, 2024 | 18 | 2024 |
| 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 | 17 | 2024 |
| 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 | 11 | 2023 |
| Uncertainty estimation for machine learning models in multiphase flow applications L Frau, GA Susto, T Barbariol, E Feltresi Informatics 8 (3), 58, 2021 | 9 | 2021 |
| 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 | 7 | 2022 |
| A Machine Learning-Based System for Self-Diagnosis Multiphase Flow Meters T Barbariol, E Feltresi, GA Susto International Petroleum Technology Conference, D021S042R003, 2020 | 6 | 2020 |
| 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 | 4 | 2020 |
| 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 | 3 | 2022 |
| 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 | 3 | 2019 |
| Improving Anomaly Detection for Industrial Applications T Barbariol Università degli studi di Padova, 2023 | 1 | 2023 |
| Time Series Forecasting to Detect Anomalous Behavior in Multiphase Flow Meters T Barbariol, D Masiero, M Fanan, E Feltresi, GA Susto 2024 IEEE 8th Forum on Research and Technologies for Society and Industry …, 2024 | | 2024 |
| Unveiling Circumnutation in Pea Plants via Supervised Machine Learning Q Wang, T Barbariol, GA Susto, B Bonato, S Guerra, U Castiello Preprints, 2023 | | 2023 |
Gian Antonio SustoAssociate Professor @ University of Padova, Co-founder @ StatwolfVerified email at dei.unipd.it
