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Gian Antonio Susto
Gian Antonio Susto
Associate Professor @ University of Padova, Co-founder @ Statwolf
Verified email at dei.unipd.it - Homepage
Title
Cited by
Cited by
Year
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
8902014
Control of PDE-ODE cascades with Neumann interconnections
GA Susto, M Krstic
Journal of the Franklin Institute 347 (1), 284-314, 2010
2402010
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
1572022
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
1342018
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
1102012
A convolutional autoencoder approach for feature extraction in virtual metrology
M Maggipinto, C Masiero, A Beghi, GA Susto
Procedia Manufacturing 17, 126-133, 2018
932018
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
892019
Anomaly detection approaches for semiconductor manufacturing
GA Susto, M Terzi, A Beghi
Procedia Manufacturing 11, 2018-2024, 2017
832017
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
822017
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
802022
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
762015
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
762014
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
722015
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
652018
Dealing with time-series data in predictive maintenance problems
GA Susto, A Beghi
2016 IEEE 21st International Conference on Emerging Technologies and Factory …, 2016
592016
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
572018
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
542020
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
532023
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
512018
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
452019
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