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André Luiz Pilastri
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2020 – today
- 2024
- [j10]Luís Miguel Matos
, Paula Dias, Arthur Matta
Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning. Eng. Appl. Artif. Intell. 138: 109353 (2024) - 2023
- [j9]Luís Ferreira, Leopoldo Silva, Francisco Morais, Carlos Manuel Martins, Pedro Miguel Pires, Helena Rodrigues
International revenue share fraud prediction on the 5G edge using federated learning. Computing 105(9): 1907-1932 (2023) - [j8]Rui Ribeiro
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics. Neural Comput. Appl. 35(23): 17375-17395 (2023) - [j7]Paula Dias, Arthur Matta
RTSIMU: Real-Time Simulation tool for IMU sensors. Softw. Impacts 17: 100522 (2023) - [c25]Cláudia Afonso
Machine Learning for Predicting Production Disruptions in the Wood-Based Panels Industry: A Demonstration Case. AIAI (2) 2023: 340-351 - [c24]Pedro José Pereira
AI4CITY - An Automated Machine Learning Platform for Smart Cities. SAC 2023: 886-889 - 2022
- [j6]Luís Ferreira
Using supervised and one-class automated machine learning for predictive maintenance. Appl. Soft Comput. 131: 109820 (2022) - [j5]Diogo Ribeiro
Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection. Comput. 11(4): 54 (2022) - [j4]Gabriel Coelho, Luís Miguel Matos
Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio. Neural Comput. Appl. 34(22): 19485-19499 (2022) - [j3]Luís Miguel Matos
Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing. Softw. Impacts 13: 100359 (2022) - [j2]Hugo Carvalho
RanCoord - A random geographic coordinates generator for transport and logistics research and development activities. Softw. Impacts 14: 100428 (2022) - [c23]Luísa Macedo, Luís Miguel Matos
A Machine Learning Approach for Spare Parts Lifetime Estimation. ICAART (3) 2022: 765-772 - [c22]Hugo Carvalho
An Intelligent Decision Support System for Road Freight Transport. IDEAL 2022: 146-156 - [c21]Luís Santos
A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting. IDEAL 2022: 465-473 - [c20]Gonçalo Fontes
An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets. AIAI (1) 2022: 85-95 - [c19]Afonso Sousa
Production Time Prediction for Contract Manufacturing Industries Using Automated Machine Learning. AIAI (2) 2022: 262-273 - [c18]Cristiana Fernandes, Luís Miguel Matos
A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors. IJCNN 2022: 1-8 - [c17]João Azevedo, Rui Ribeiro
Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach. KES 2022: 2301-2310 - [c16]Luís Ferreira
A federated machine learning approach to detect international revenue share fraud on the 5G edge. SAC 2022: 1432-1439 - 2021
- [j1]António João Silva
Business analytics in Industry 4.0: A systematic review. Expert Syst. J. Knowl. Eng. 38(7) (2021) - [c15]Pedro José Pereira
A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data. EPIA 2021: 561-572 - [c14]Diogo Ribeiro, Luís Miguel Matos
A Comparison of Anomaly Detection Methods for Industrial Screw Tightening. ICCSA (2) 2021: 485-500 - [c13]Luís Miguel Matos
A Comparison of Machine Learning Approaches for Predicting In-Car Display Production Quality. IDEAL 2021: 3-11 - [c12]Luís Ferreira
Prediction of Maintenance Equipment Failures Using Automated Machine Learning. IDEAL 2021: 259-267 - [c11]Rui Ribeiro
An Intelligent Decision Support System for Production Planning in Garments Industry. IDEAL 2021: 378-386 - [c10]Gabriel Coelho
Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection. AIAI 2021: 337-348 - [c9]Luís Ferreira
A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost. IJCNN 2021: 1-8 - [c8]Pedro José Pereira
Using Deep Autoencoders for In-vehicle Audio Anomaly Detection. KES 2021: 298-307 - 2020
- [c7]Luís Ferreira
An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management. ICAART (2) 2020: 99-107 - [c6]Luís Ferreira
A Scalable and Automated Machine Learning Framework to Support Risk Management. ICAART (Revised Selected Papers) 2020: 291-307 - [c5]Rui Ribeiro
Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology. ICEIS (1) 2020: 548-555 - [c4]António João Silva
Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples. AIAI (2) 2020: 232-243 - [c3]Rui Ribeiro
Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features. AIAI (2) 2020: 244-255 - [i1]Alexandrine Ribeiro, Luís Miguel Matos
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds. CoRR abs/2006.10417 (2020)
2010 – 2019
- 2016
- [c2]Juliana Cassiano Ferrarezi, Mário Popolin Neto
LibViews - An Information Visualization Application for Third-Party Libraries on Software Projects. IV 2016: 136-140 - 2014
- [c1]Silas Evandro Nachif Fernandes, André Luiz Pilastri
Learning Kernels for Support Vector Machines with Polynomial Powers of Sigmoid. SIBGRAPI 2014: 259-265
Coauthor Index
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