Andre Pilastri
Andre Pilastri
CCG/ZGDV - ICT Innovation Institute
Verified email at - Homepage
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Cited by
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
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
Business analytics in Industry 4.0: A systematic review
AJ Silva, P Cortez, C Pereira, A Pilastri
Expert systems 38 (7), e12741, 2021
Reconstruction algorithms in compressive sensing: An overview
AL Pilastri, JMRS Tavares
11th edition of the Doctoral Symposium in Informatics Engineering (DSIE-16), 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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