| Machine learning for predictive maintenance: A multiple classifier approach GA Susto, A Schirru, S Pampuri, S McLoone, A Beghi IEEE transactions on industrial informatics 11 (3), 812-820, 2014 | 1063 | 2014 |
| Control of PDE-ODE cascades with Neumann interconnections GA Susto, M Krstic Journal of the Franklin Institute 347 (1), 284-314, 2010 | 256 | 2010 |
| An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery LC Brito, GA Susto, JN Brito, MAV Duarte Mechanical Systems and Signal Processing 163, 108105, 2022 | 254 | 2022 |
| Time-series classification methods: Review and applications to power systems data GA Susto, A Cenedese, M Terzi Big data application in power systems, 179-220, 2018 | 165 | 2018 |
| A predictive maintenance system for epitaxy processes based on filtering and prediction techniques GA Susto, A Beghi, C De Luca IEEE Transactions on Semiconductor Manufacturing 25 (4), 638-649, 2012 | 131 | 2012 |
| Algorithmic fairness datasets: the story so far A Fabris, S Messina, G Silvello, GA Susto Data Mining and Knowledge Discovery 36 (6), 2074-2152, 2022 | 127 | 2022 |
| Explainable machine learning in industry 4.0: Evaluating feature importance in anomaly detection to enable root cause analysis M Carletti, C Masiero, A Beghi, GA Susto 2019 IEEE international conference on systems, man and cybernetics (SMC), 21-26, 2019 | 118 | 2019 |
| A convolutional autoencoder approach for feature extraction in virtual metrology M Maggipinto, C Masiero, A Beghi, GA Susto Procedia Manufacturing 17, 126-133, 2018 | 110 | 2018 |
| Interpretable anomaly detection with diffi: Depth-based feature importance of isolation forest M Carletti, M Terzi, GA Susto Engineering Applications of Artificial Intelligence 119, 105730, 2023 | 98 | 2023 |
| Anomaly detection approaches for semiconductor manufacturing GA Susto, M Terzi, A Beghi Procedia Manufacturing 11, 2018-2024, 2017 | 96 | 2017 |
| Anomaly detection through on-line isolation forest: An application to plasma etching GA Susto, A Beghi, S McLoone 2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC …, 2017 | 88 | 2017 |
| A one-class svm based tool for machine learning novelty detection in hvac chiller systems A Beghi, L Cecchinato, C Corazzol, M Rampazzo, F Simmini, GA Susto IFAC Proceedings Volumes 47 (3), 1953-1958, 2014 | 87 | 2014 |
| Supervised aggregative feature extraction for big data time series regression GA Susto, A Schirru, S Pampuri, S McLoone IEEE Transactions on Industrial Informatics 12 (3), 1243-1252, 2015 | 77 | 2015 |
| Multi-step virtual metrology for semiconductor manufacturing: A multilevel and regularization methods-based approach GA Susto, S Pampuri, A Schirru, A Beghi, G De Nicolao Computers & Operations Research 53, 328-337, 2015 | 77 | 2015 |
| Data-driven anomaly recognition for unsupervised model-free fault detection in artificial pancreas L Meneghetti, M Terzi, S Del Favero, GA Susto, C Cobelli IEEE Transactions on Control Systems Technology 28 (1), 33-47, 2018 | 76 | 2018 |
| Dealing with time-series data in predictive maintenance problems GA Susto, A Beghi 2016 IEEE 21st International Conference on Emerging Technologies and Factory …, 2016 | 73 | 2016 |
| Gender stereotype reinforcement: Measuring the gender bias conveyed by ranking algorithms A Fabris, A Purpura, G Silvello, GA Susto Information Processing & Management 57 (6), 102377, 2020 | 71 | 2020 |
| A computer vision-inspired deep learning architecture for virtual metrology modeling with 2-dimensional data M Maggipinto, M Terzi, C Masiero, A Beghi, GA Susto IEEE Transactions on Semiconductor Manufacturing 31 (3), 376-384, 2018 | 68 | 2018 |
| DeepVM: A Deep Learning-based approach with automatic feature extraction for 2D input data Virtual Metrology M Maggipinto, A Beghi, S McLoone, GA Susto Journal of Process Control 84, 24-34, 2019 | 55 | 2019 |
| A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing GA Susto, A Schirru, S Pampuri, A Beghi, G De Nicolao Control Engineering Practice 74, 84-94, 2018 | 51 | 2018 |
Alessandro BeghiUniversity of PadovaEmail verificata su dei.unipd.it