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Pedro José Pereira
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2020 – today
- 2024
- [c14]Pedro José Pereira, Nuno Costa, Pedro Mestre, Paulo Cortez:
A Benchmark of Automated Multivariate Time Series Forecasting Tools for Smart Cities. EPIA (3) 2024: 139-150 - [c13]Hélder Teixeira, Arthur Matta, André Luiz Pilastri, Luís Ferreira, Pedro José Pereira, Carlos Gonçalves, Paulo Cortez:
A Data Drift Approach to Update Deployed Energy Prediction Machine Learning Models. EPIA (3) 2024: 151-162 - 2023
- [c12]Daniela Soares, Pedro José Pereira, Paulo Cortez, Carlos Gonçalves:
A Comparison of Automated Machine Learning Tools for Predicting Energy Building Consumption in Smart Cities. EPIA (1) 2023: 311-322 - [c11]Carolina Loureiro, Pedro José Pereira, Paulo Cortez, Pedro Guimarães, Carlos Moreira, André Pinho:
Predicting Multiple Domain Queue Waiting Time via Machine Learning. ICCSA (1) 2023: 404-421 - [c10]Pedro José Pereira, Carlos Gonçalves, Lara Lopes Nunes, Paulo Cortez, André Luiz Pilastri:
AI4CITY - An Automated Machine Learning Platform for Smart Cities. SAC 2023: 886-889 - 2022
- [j3]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) - [c9]Pedro José Pereira, Nuno Costa, Margarida Barros, Paulo Cortez, Dalila Durães, António Silva, José Machado:
A Comparison of Automated Time Series Forecasting Tools for Smart Cities. EPIA 2022: 551-562 - 2021
- [j2]Pedro José Pereira, Paulo Cortez, Rui Mendes:
Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction. Expert Syst. Appl. 168: 114287 (2021) - [c8]Pedro José Pereira, Adriana Pereira, Paulo Cortez, André Luiz Pilastri:
A Comparison of Machine Learning Methods for Extremely Unbalanced Industrial Quality Data. EPIA 2021: 561-572 - [c7]Rui Ribeiro, André Luiz Pilastri, Hugo Carvalho, Arthur Matta, Pedro José Pereira, Pedro Rocha, Marcelo Pitanga Alves, Paulo Cortez:
An Intelligent Decision Support System for Production Planning in Garments Industry. IDEAL 2021: 378-386 - [c6]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 - [c5]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
- [j1]Paulo Cortez, Pedro José Pereira, Rui Mendes:
Multi-step time series prediction intervals using neuroevolution. Neural Comput. Appl. 32(13): 8939-8953 (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
- [c4]Pedro José Pereira, Pedro Pinto, Rui Mendes, Paulo Cortez, Antoine Moreau:
Using Neuroevolution for Predicting Mobile Marketing Conversion. EPIA (2) 2019: 373-384 - 2018
- [c3]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 - 2017
- [c2]Pedro José Pereira, Paulo Cortez, Rui Mendes:
Multi-objective Learning of Neural Network Time Series Prediction Intervals. EPIA 2017: 561-572 - 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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