| A comparison of AutoML tools for machine learning, deep learning and XGBoost L Ferreira, A Pilastri, CM Martins, PM Pires, P Cortez 2021 international joint conference on neural networks (IJCNN), 1-8, 2021 | 92 | 2021 |
| Deep dense and convolutional autoencoders for machine acoustic anomaly detection G Coelho, P Pereira, L Matos, A Ribeiro, EC Nunes, A Ferreira, P Cortez, ... Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 …, 2021 | 40* | 2021 |
| Business analytics in Industry 4.0: A systematic review AJ Silva, P Cortez, C Pereira, A Pilastri Expert systems 38 (7), e12741, 2021 | 37 | 2021 |
| Reconstruction algorithms in compressive sensing: An overview AL Pilastri, JMRS Tavares 11th edition of the Doctoral Symposium in Informatics Engineering (DSIE-16), 2016 | 29 | 2016 |
| Predicting the Tear Strength of Woven Fabrics via Automated Machine Learning: An Application of the CRISP-DM Methodology PC Rui Ribeiro, André Pilastri, Carla Moura, Filipe Rodrigues, Rita Rocha 22th International Conference on Enterprise Information Systems -- ICEIS 2020, 2020 | 20* | 2020 |
| Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features R Ribeiro, A Pilastri, C Moura, F Rodrigues, R Rocha, J Morgado, ... Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 …, 2020 | 17 | 2020 |
| Using deep autoencoders for in-vehicle audio anomaly detection PJ Pereira, G Coelho, A Ribeiro, LM Matos, EC Nunes, A Ferreira, ... Procedia Computer Science 192, 298-307, 2021 | 16 | 2021 |
| Isolation forests and deep autoencoders for industrial screw tightening anomaly detection D Ribeiro, LM Matos, G Moreira, A Pilastri, P Cortez Computers 11 (4), 54, 2022 | 15 | 2022 |
| An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management PC L Ferreira, A Pilastri, C Martins, P Santos 12th International Conference on Agents and Artificial Intelligence …, 2020 | 13* | 2020 |
| Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing LM Matos, J Azevedo, A Matta, A Pilastri, P Cortez, R Mendes Software Impacts 13, 100359, 2022 | 12 | 2022 |
| A scalable and automated machine learning framework to support risk management L Ferreira, A Pilastri, C Martins, P Santos, P Cortez International Conference on Agents and Artificial Intelligence, 291-307, 2020 | 11 | 2020 |
| A comparison of anomaly detection methods for industrial screw tightening D Ribeiro, LM Matos, P Cortez, G Moreira, A Pilastri Computational Science and Its Applications–ICCSA 2021: 21st International …, 2021 | 9 | 2021 |
| Using supervised and one-class automated machine learning for predictive maintenance L Ferreira, A Pilastri, F Romano, P Cortez Applied Soft Computing 131, 109820, 2022 | 8 | 2022 |
| Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio G Coelho, LM Matos, PJ Pereira, A Ferreira, A Pilastri, P Cortez Neural Computing and Applications 34 (22), 19485-19499, 2022 | 8 | 2022 |
| Prediction of maintenance equipment failures using automated machine learning L Ferreira, A Pilastri, V Sousa, F Romano, P Cortez International Conference on Intelligent Data Engineering and Automated …, 2021 | 8 | 2021 |
| Learning kernels for support vector machines with polynomial powers of sigmoid SEN Fernandes, AL Pilastri, LAM Pereira, RG Pires, JP Papa 2014 27th SIBGRAPI conference on graphics, patterns and images, 259-265, 2014 | 7 | 2014 |
| A deep learning approach to prevent problematic movements of industrial workers based on inertial sensors C Fernandes, LM Matos, D Folgado, ML Nunes, JR Pereira, A Pilastri, ... 2022 International Joint Conference on Neural Networks (IJCNN), 01-08, 2022 | 6 | 2022 |
| A comparison of machine learning methods for extremely unbalanced industrial quality data PJ Pereira, A Pereira, P Cortez, A Pilastri Progress in Artificial Intelligence: 20th EPIA Conference on Artificial …, 2021 | 6 | 2021 |
| Chemical laboratories 4.0: A two-stage machine learning system for predicting the arrival of samples AJ Silva, P Cortez, A Pilastri IFIP International Conference on Artificial Intelligence Applications and …, 2020 | 6 | 2020 |
| A Machine Learning Approach for Spare Parts Lifetime Estimation L Macedo, LM Matos, P Cortez, A Domingues, G Moreira, A Pilastri 14th International Conference on Agents and Artificial Intelligence - ICAART …, 2022 | 5 | 2022 |
Paulo CortezFull Professor, ALGORITMI, Department Information Systems, University of MinhoE-mail confirmado em dsi.uminho.pt
