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Andre Pilastri
Andre Pilastri
Centro de Computação Gráfica - CCG/Uminho
Email confirmado em ccg.pt - Página inicial
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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
212021
Deep dense and convolutional autoencoders for machine acoustic anomaly detection
G Coelho, P Pereira, L Matos, A Ribeiro, EC Nunes, A Ferreira, P Cortez, ...
IFIP International Conference on Artificial Intelligence Applications and …, 2021
17*2021
Reconstruction algorithms in compressive sensing: An overview
AL Pilastri, JMRS Tavares
11th edition of the Doctoral Symposium in Informatics Engineering (DSIE-16), 2016
172016
Business analytics in industry 4.0: a systematic review
AJ Silva, P Cortez, C Pereira, A Pilastri
Expert Systems 38 (7), e12741, 2021
102021
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, ...
IFIP International Conference on Artificial Intelligence Applications and …, 2020
72020
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
7*2020
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
6*2020
A comparison of anomaly detection methods for industrial screw tightening
D Ribeiro, LM Matos, P Cortez, G Moreira, A Pilastri
International Conference on Computational Science and Its Applications, 485-500, 2021
52021
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
42014
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
32020
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
22022
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
22021
A comparison of machine learning approaches for predicting in-car display production quality
LM Matos, A Domingues, G Moreira, P Cortez, A Pilastri
International Conference on Intelligent Data Engineering and Automated …, 2021
22021
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
22021
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
22020
Libviews-an information visualization application for third-party libraries on software projects
JC Ferrarezi, M Neto, DRC Dias, AL Pilastri, MDP Guimarães, JRF Brega
2016 20th International Conference Information Visualisation (IV), 136-140, 2016
22016
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
12022
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
12022
An empirical study on anomaly detection algorithms for extremely imbalanced datasets
G Fontes, LM Matos, A Matta, A Pilastri, P Cortez
IFIP International Conference on Artificial Intelligence Applications and …, 2022
12022
A comparison of machine learning methods for extremely unbalanced industrial quality data
PJ Pereira, A Pereira, P Cortez, A Pilastri
EPIA Conference on Artificial Intelligence, 561-572, 2021
12021
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Artigos 1–20