Learning Kernels for Support Vector Machines with Polynomial Powers of Sigmoid. - Silas Evandro Nachif Fernandes; André Luiz Pilastri; Luís A. M. Pereira; Rafael Goncalves Pires; João Paulo Papa (SIBGRAPI, 2014)

LibViews - An Information Visualization Application for Third-Party Libraries on Software Projects. - Juliana Cassiano Ferrarezi; Mário Popolin Neto; Diego Roberto Colombo Dias; André Luiz Pilastri; Marcelo de Paiva Guimarães; José Remo Ferreira Brega (IV, 2016)

An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management. - Luís Ferreira 002; André Luiz Pilastri; Carlos Martins; Pedro Santos; Paulo Cortez 001 (ICAART (2), 2020)

A Scalable and Automated Machine Learning Framework to Support Risk Management. - Luís Ferreira 002; André Luiz Pilastri; Carlos Martins; Pedro Santos; Paulo Cortez 001 (ICAART (Revised Selected Papers), 2020)

Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology. - Rui Ribeiro; André Luiz Pilastri; Carla Moura; Filipe Rodrigues; Rita Rocha; Paulo Cortez 001 (ICEIS (1), 2020)

Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features. - Rui Ribeiro; André Luiz Pilastri; Carla Moura; Filipe Rodrigues; Rita Rocha; José Morgado; Paulo Cortez 001 (AIAI (2), 2020)

Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples. - António João Silva; Paulo Cortez 001; André Luiz Pilastri (AIAI (2), 2020)

Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds. - Alexandrine Ribeiro; Luís Miguel Matos; Pedro José Pereira; Eduardo C. Nunes; André L. Ferreira; Paulo Cortez 001; André Luiz Pilastri (CoRR, 2020)

A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data. - Pedro José Pereira; Adriana Pereira; Paulo Cortez 001; André Luiz Pilastri (EPIA, 2021)

A Comparison of Anomaly Detection Methods for Industrial Screw Tightening. - Diogo Ribeiro 002; Luís Miguel Matos; Paulo Cortez 001; Guilherme Moreira; André Luiz Pilastri (ICCSA (2), 2021)

Prediction of Maintenance Equipment Failures Using Automated Machine Learning. - Luís Ferreira 002; André Luiz Pilastri; Vítor Sousa; Filipe Romano; Paulo Cortez 001 (IDEAL, 2021)

A Comparison of Machine Learning Approaches for Predicting In-Car Display Production Quality. - Luís Miguel Matos; André Domingues; Guilherme Moreira; Paulo Cortez 001; André Luiz Pilastri (IDEAL, 2021)

An Intelligent Decision Support System for Production Planning in Garments Industry. - Rui Ribeiro; André Luiz Pilastri; Hugo Carvalho; Arthur Matta; Pedro José Pereira; Pedro Rocha; Marcelo Pitanga Alves; Paulo Cortez 001 (IDEAL, 2021)

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection. - Gabriel Coelho; Pedro José Pereira; Luís Miguel Matos; Alexandrine Ribeiro; Eduardo C. Nunes; André L. Ferreira; Paulo Cortez 001; André Luiz Pilastri (AIAI, 2021)

A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost. - Luís Ferreira 002; André Luiz Pilastri; Carlos Manuel Martins; Pedro Miguel Pires; Paulo Cortez 001 (IJCNN, 2021)

Using Deep Autoencoders for In-vehicle Audio Anomaly Detection. - Pedro José Pereira; Gabriel Coelho; Alexandrine Ribeiro; Luís Miguel Matos; Eduardo C. Nunes; André L. Ferreira; André Luiz Pilastri; Paulo Cortez 001 (KES, 2021)

Business analytics in Industry 4.0: A systematic review. - António João Silva; Paulo Cortez 001; Carlos Pereira; André Luiz Pilastri (Expert Syst. J. Knowl. Eng., 2021)

A Machine Learning Approach for Spare Parts Lifetime Estimation. - Luísa Macedo; Luís Miguel Matos; Paulo Cortez 001; André Domingues; Guilherme Moreira; André Luiz Pilastri (ICAART (3), 2022)

An Intelligent Decision Support System for Road Freight Transport. - Hugo Carvalho; André Luiz Pilastri; Arthur Matta; Luís Miguel Matos; Rui Novais; Paulo Cortez 001 (IDEAL, 2022)

A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting. - Luís Santos; Luís Miguel Matos; Luís Ferreira 002; Pedro Alves; Mário Viana; André Luiz Pilastri; Paulo Cortez 001 (IDEAL, 2022)

An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets. - Gonçalo Fontes; Luís Miguel Matos; Arthur Matta; André Luiz Pilastri; Paulo Cortez 001 (AIAI (1), 2022)

Production Time Prediction for Contract Manufacturing Industries Using Automated Machine Learning. - Afonso Sousa; Luís Ferreira 002; Rui Ribeiro; João Xavier; André Luiz Pilastri; Paulo Cortez 001 (AIAI (2), 2022)

A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors. - Cristiana Fernandes; Luís Miguel Matos; Duarte Folgado; Maria Lua Nunes; João Rui Pereira; André Luiz Pilastri; Paulo Cortez 001 (IJCNN, 2022)

Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach. - João Azevedo; Rui Ribeiro; Luís Miguel Matos; Rui Sousa; João Paulo Silva; André Luiz Pilastri; Paulo Cortez 001 (KES, 2022)

A federated machine learning approach to detect international revenue share fraud on the 5G edge. - Luís Ferreira 002; Leopoldo Silva; Diana Pinho; Francisco Morais; Carlos Manuel Martins; Pedro Miguel Pires; Pedro Fidalgo; Helena Rodrigues 001; Paulo Cortez 001; André Luiz Pilastri (SAC, 2022)

Using supervised and one-class automated machine learning for predictive maintenance. - Luís Ferreira 002; André Luiz Pilastri; Filipe Romano; Paulo Cortez 001 (Appl. Soft Comput., 2022)

Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection. - Diogo Ribeiro 002; Luís Miguel Matos; Guilherme Moreira; André Luiz Pilastri; Paulo Cortez 001 (Comput., 2022)

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio. - Gabriel Coelho; Luís Miguel Matos; Pedro José Pereira; André L. Ferreira; André Luiz Pilastri; Paulo Cortez 001 (Neural Comput. Appl., 2022)

Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing. - Luís Miguel Matos; João Azevedo; Arthur Matta; André Luiz Pilastri; Paulo Cortez 001; Rui Mendes 001 (Softw. Impacts, 2022)

RanCoord - A random geographic coordinates generator for transport and logistics research and development activities. - Hugo Carvalho; André Luiz Pilastri; Rui Novais; Paulo Cortez 001 (Softw. Impacts, 2022)

Machine Learning for Predicting Production Disruptions in the Wood-Based Panels Industry: A Demonstration Case. - Cláudia Afonso; Arthur Matta; Luís Miguel Matos; Miguel Bastos Gomes; Antonina Santos; André Luiz Pilastri; Paulo Cortez 001 (AIAI (2), 2023)

AI4CITY - An Automated Machine Learning Platform for Smart Cities. - Pedro José Pereira; Carlos Gonçalves; Lara Lopes Nunes; Paulo Cortez 001; André Luiz Pilastri (SAC, 2023)

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics. - Rui Ribeiro; André Luiz Pilastri; Carla Moura; José Morgado; Paulo Cortez 001 (Neural Comput. Appl., 2023)

International revenue share fraud prediction on the 5G edge using federated learning. - Luís Ferreira 002; Leopoldo Silva; Francisco Morais; Carlos Manuel Martins; Pedro Miguel Pires; Helena Rodrigues 001; Paulo Cortez 001; André Luiz Pilastri (Computing, 2023)

RTSIMU: Real-Time Simulation tool for IMU sensors. - Paula Dias; Arthur Matta; André Luiz Pilastri; Luís Miguel Matos; Paulo Cortez 001 (Softw. Impacts, 2023)

A Data Drift Approach to Update Deployed Energy Prediction Machine Learning Models. - Hélder Teixeira; Arthur Matta; André Luiz Pilastri; Luís Ferreira 002; Pedro José Pereira; Carlos Gonçalves; Paulo Cortez 001 (EPIA (3), 2024)

Ahead of Time Prediction of Decorated Particleboard Production Disruptions and Defects Using Single and Multi-Target AutoML. - Arthur Matta; Luís Miguel Matos; André Luiz Pilastri; Jorge Miguel Silva; Miguel Bastos Gomes; Paulo Cortez 001 (KES, 2024)

Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning. - Luís Miguel Matos; Paula Dias; Arthur Matta; Dário Machado; Rosane Sampaio; André Luiz Pilastri; Paulo Cortez 001 (Eng. Appl. Artif. Intell., 2024)