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Andre Luiz Pilastri

Observed names: Andre Pilastri, André Pilastri, Pilastri, Andre, Pilastri, Andre Luiz, Pilastri, André, Pilastri, André Luiz
Observed institutions: Universidade do Porto, Universidade do Estado de Mato Grosso, Centro de Computação Gráfica, Zentrum für Foren in der Grafischen Datenverarbeitung, Centro ALGORITMI, University of Minho
ORCID: 0000-0002-4380-3220
Observed ORCID: 0000-0002-4380-3220
OpenAlex ID: A5014796819
A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost
2021 · Luis Ferreira, Andre Pilastri, et al. · International Joint Conference on Neural Networks (IJCNN) · 163
Business analytics in Industry 4.0: A systematic review
2021 · António João Silva, Paulo Cortez, et al. · Expert Systems · 81
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Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection
2022 · Diogo Ribeiro, Luís Miguel Matos, et al. · Computers · 57
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Holistic Framework to Data-Driven Sustainability Assessment
2023 · Paulo Peças, Lenin John, et al. · Sustainability · 45
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A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics
2023 · Rui Ribeiro, André Pilastri, et al. · Neural Computing and Applications · 38
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Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio
2022 · Gabriel Coelho, Luís Miguel Matos, et al. · Neural Computing and Applications · 30
Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology
2020 · Rui Ribeiro, André Pilastri, et al. · International Conference on Enterprise Information Systems · 30
Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features
2020 · Rui Ribeiro, André Pilastri, et al. · IFIP advances in information and communication technology · 30
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Using supervised and one-class automated machine learning for predictive maintenance
2022 · Luís Ferreira, André Pilastri, et al. · Applied Soft Computing · 29
Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach
2022 · João Azevedo, Rui Ribeiro, et al. · Procedia Computer Science · 27
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Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds
2020 · Ribeiro, Alexandrine, Matos, Luis Miguel, et al. · arXiv (Cornell University) · 27
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Reconstruction Algorithms in Compressive Sensing: An Overview
2016 · André Pilastri, João Manuel R. S. Tavares · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 25
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Prediction of Maintenance Equipment Failures Using Automated Machine Learning
2021 · Luís Ferreira, André Pilastri, et al. · Lecture notes in computer science · 23
Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing
2022 · Luís Miguel Matos, João Azevedo, et al. · Software Impacts · 22
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Using Deep Autoencoders for In-vehicle Audio Anomaly Detection
2021 · Pedro José Pereira, Gabriel Coelho, et al. · Procedia Computer Science · 22
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Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning
2024 · Luís Miguel Matos, Paula Dias, et al. · Engineering Applications of Artificial Intelligence · 18
An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management
2020 · Luís Ferreira, André Pilastri, et al. · International Conference on Agents and Artificial Intelligence (ICAART) · 14
A Scalable and Automated Machine Learning Framework to Support Risk Management
2021 · Luís Ferreira, André Pilastri, et al. · Lecture notes in computer science · 13
International revenue share fraud prediction on the 5G edge using federated learning
2023 · Luís Ferreira, Leopoldo Silva, et al. · Computing · 11
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Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection
2021 · Gabriel Coelho, Pedro Pereira, et al. · IFIP advances in information and communication technology · 11
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A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors
2022 · Cristiana Fernandes, Luis Miguel Matos, et al. · International Joint Conference on Neural Networks (IJCNN) · 9
Production Time Prediction for Contract Manufacturing Industries Using Automated Machine Learning
2022 · Afonso Sousa, Luís Ferreira, et al. · IFIP advances in information and communication technology · 9
Learning Kernels for Support Vector Machines with Polynomial Powers of Sigmoid
2014 · Silas Evandro Nachif Fernandes, Andre Luiz Pilastri, et al. · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · 9
A Comparison of Anomaly Detection Methods for Industrial Screw Tightening
2021 · Diogo Ribeiro, Luís Miguel Matos, et al. · Lecture notes in computer science · 8
A Machine Learning Approach for Spare Parts Lifetime Estimation
2022 · Luísa Macedo, Luís Miguel Matos, et al. · International Conference on Agents and Artificial Intelligence (ICAART) · 7
Works count: 58
Citation count: 831
H-index: 16
I10-index: 20
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