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ORCIDLuís Miguel Matos
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2020 – today
- 2024
[j6]Luís Miguel Matos
, Paula Dias, Arthur Matta, Dário Machado, Rosane Sampaio, André Luiz Pilastri, Paulo Cortez:
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
- [j5]Luís Miguel Matos, Paulo Cortez, Rui Mendes, Antoine Moreau:
A Deep Learning-Based Decision Support System for Mobile Performance Marketing. Int. J. Inf. Technol. Decis. Mak. 22(2): 679-703 (2023) - [j4]Paula Dias, Arthur Matta, André Luiz Pilastri, Luís Miguel Matos, Paulo Cortez:
RTSIMU: Real-Time Simulation tool for IMU sensors. Softw. Impacts 17: 100522 (2023) - [c16]Cláudia Afonso, Arthur Matta, Luís Miguel Matos, Miguel Bastos Gomes, Antonina Santos, André Luiz Pilastri, Paulo Cortez:
Machine Learning for Predicting Production Disruptions in the Wood-Based Panels Industry: A Demonstration Case. AIAI (2) 2023: 340-351 - 2022
- [j3]Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Luiz Pilastri, Paulo Cortez:
Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection. Comput. 11(4): 54 (2022) - [j2]Gabriel Coelho, Luís Miguel Matos, Pedro José Pereira, André L. Ferreira, André Luiz Pilastri, Paulo Cortez:
Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio. Neural Comput. Appl. 34(22): 19485-19499 (2022) - [j1]Luís Miguel Matos, João Azevedo, Arthur Matta, André Luiz Pilastri, Paulo Cortez, Rui Mendes:
Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing. Softw. Impacts 13: 100359 (2022) - [c15]Luísa Macedo, Luís Miguel Matos, Paulo Cortez, André Domingues, Guilherme Moreira, André Luiz Pilastri:
A Machine Learning Approach for Spare Parts Lifetime Estimation. ICAART (3) 2022: 765-772 - [c14]Hugo Carvalho, André Luiz Pilastri, Arthur Matta, Luís Miguel Matos, Rui Novais, Paulo Cortez:
An Intelligent Decision Support System for Road Freight Transport. IDEAL 2022: 146-156 - [c13]Luís Santos, Luís Miguel Matos, Luís Ferreira, Pedro Alves, Mário Viana, André Luiz Pilastri, Paulo Cortez:
A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting. IDEAL 2022: 465-473 - [c12]Gonçalo Fontes, Luís Miguel Matos, Arthur Matta, André Luiz Pilastri, Paulo Cortez:
An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets. AIAI (1) 2022: 85-95 - [c11]Cristiana Fernandes, Luís Miguel Matos, Duarte Folgado, Maria Lua Nunes, João Rui Pereira, André Luiz Pilastri, Paulo Cortez:
A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors. IJCNN 2022: 1-8 - [c10]João Azevedo, Rui Ribeiro, Luís Miguel Matos, Rui Sousa, João Paulo Silva, André Luiz Pilastri, Paulo Cortez:
Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach. KES 2022: 2301-2310 - 2021
- [b1]Luís Miguel Matos:
An intelligent decision support system for mobile performance marketing. University of Minho, Portugal, 2021 - [c9]Diogo Ribeiro, Luís Miguel Matos, Paulo Cortez, Guilherme Moreira, André Luiz Pilastri:
A Comparison of Anomaly Detection Methods for Industrial Screw Tightening. ICCSA (2) 2021: 485-500 - [c8]Luís Miguel Matos, André Domingues, Guilherme Moreira, Paulo Cortez, André Luiz Pilastri:
A Comparison of Machine Learning Approaches for Predicting In-Car Display Production Quality. IDEAL 2021: 3-11 - [c7]Gabriel Coelho, Pedro José Pereira, Luís Miguel Matos, Alexandrine Ribeiro, Eduardo C. Nunes, André L. Ferreira, Paulo Cortez, André Luiz Pilastri:
Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection. AIAI 2021: 337-348 - [c6]Pedro José Pereira, Gabriel Coelho, Alexandrine Ribeiro, Luís Miguel Matos, Eduardo C. Nunes, André L. Ferreira, André Luiz Pilastri, Paulo Cortez:
Using Deep Autoencoders for In-vehicle Audio Anomaly Detection. KES 2021: 298-307 - 2020
- [i1]Alexandrine Ribeiro, Luís Miguel Matos, Pedro José Pereira, Eduardo C. Nunes, André L. Ferreira, Paulo Cortez, André Luiz Pilastri:
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds. CoRR abs/2006.10417 (2020)
2010 – 2019
- 2019
- [c5]Luís Miguel Matos, Paulo Cortez, Rui Castro Mendes, Antoine Moreau:
Using Deep Learning for Ordinal Classification of Mobile Marketing User Conversion. IDEAL (1) 2019: 60-67 - [c4]Luís Miguel Matos, Paulo Cortez, Rui Mendes, Antoine Moreau:
Using Deep Learning for Mobile Marketing User Conversion Prediction. IJCNN 2019: 1-8 - 2018
- [c3]Luís Miguel Matos, Paulo Cortez, Rui Mendes, Antoine Moreau:
A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction. IEEE Conf. on Intelligent Systems 2018: 140-146 - [c2]Susana Silva, Paulo Cortez, Rui Mendes, Pedro José Pereira, Luís Miguel Matos, Luís Garcia:
A Categorical Clustering of Publishers for Mobile Performance Marketing. SOCO-CISIS-ICEUTE 2018: 145-154 - 2016
- [c1]Paulo Cortez, Luís Miguel Matos, Pedro José Pereira, Nuno Santos, Duarte Duque:
Forecasting Store Foot Traffic Using Facial Recognition, Time Series and Support Vector Machines. SOCO-CISIS-ICEUTE 2016: 267-276
Coauthor Index
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