| 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 | 25* | 2021 |
| Using deep learning for mobile marketing user conversion prediction LM Matos, P Cortez, R Mendes, A Moreau 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 19 | 2019 |
| Forecasting store foot traffic using facial recognition, time series and support vector machines P Cortez, LM Matos, PJ Pereira, N Santos, D Duque International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián …, 2017 | 17 | 2017 |
| A comparison of data-driven approaches for mobile marketing user conversion prediction LM Matos, P Cortez, R Mendes, A Moreau 2018 International Conference on Intelligent Systems (IS), 140-146, 2018 | 12 | 2018 |
| 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 | 8 | 2021 |
| 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 | 7 | 2022 |
| 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 | 6 | 2022 |
| 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 | 6 | 2021 |
| A categorical clustering of publishers for mobile performance marketing S Silva, P Cortez, R Mendes, PJ Pereira, LM Matos, L Garcia International Joint Conference SOCO’18-CISIS’18-ICEUTE’18: San Sebastián …, 2019 | 6 | 2019 |
| A comparison of machine learning approaches for predicting in-car display production quality LM Matos, A Domingues, G Moreira, P Cortez, A Pilastri Intelligent Data Engineering and Automated Learning–IDEAL 2021: 22nd …, 2021 | 5 | 2021 |
| Using deep learning for ordinal classification of mobile marketing user conversion LM Matos, P Cortez, RC Mendes, A Moreau Intelligent Data Engineering and Automated Learning–IDEAL 2019: 20th …, 2019 | 5 | 2019 |
| An intelligent decision support system for road freight transport HS Carvalho, A Pilastri, A Matta, LM Matos, R Novais, P Cortez Intelligent Data Engineering and Automated Learning–IDEAL 2022: 23rd …, 2022 | 3 | 2022 |
| A Machine Learning Approach for Spare Parts Lifetime Estimation. L Macedo, LM Matos, P Cortez, A Domingues, G Moreira, AL Pilastri ICAART (3), 765-772, 2022 | 3 | 2022 |
| A Deep Learning-Based Decision Support System for Mobile Performance Marketing LM Matos, P Cortez, R Mendes, A Moreau International Journal of Information Technology & Decision Making (IJITDM …, 2023 | 2 | 2023 |
| 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 | 2 | 2022 |
| An empirical study on anomaly detection algorithms for extremely imbalanced datasets G Fontes, LM Matos, A Matta, A Pilastri, P Cortez Artificial Intelligence Applications and Innovations: 18th IFIP WG 12.5 …, 2022 | 2 | 2022 |
| 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 | 1 | 2022 |
| Machine Learning for Predicting Production Disruptions in the Wood-Based Panels Industry: A Demonstration Case C Afonso, A Matta, LM Matos, MB Gomes, A Santos, A Pilastri, P Cortez Artificial Intelligence Applications and Innovations: 19th IFIP WG 12.5 …, 2023 | | 2023 |
| A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting L Santos, LM Matos, L Ferreira, P Alves, M Viana, A Pilastri, P Cortez Intelligent Data Engineering and Automated Learning–IDEAL 2022: 23rd …, 2022 | | 2022 |
| Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach J Azevedo, R Ribeiro, LM Matos, R Sousa, JP Silva, A Pilastri, P Cortez Procedia Computer Science 207, 2301-2310, 2022 | | 2022 |
Paulo CortezFull Professor, ALGORITMI/Dep. Information Systems, University of MinhoEmail confirmado em dsi.uminho.pt
