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Sobre

Sobre

João Gama é Professor Catedrático da Faculdade de Economia da Universidade do Porto. É investigador e vice-diretor do LIAAD, INESC TEC. Concluiu o doutoramento na Universidade do Porto, em 2000. É Fellow do IEEE e EurIA Fellow. Trabalhou em vários projetos nacionais e europeus sobre sistemas de aprendizagem incremental e adaptativo, descoberta de conhecimento em tempo real, e aprendizagem de dados massivos e estruturados. Foi PC chair no ECML2005, DS2009, ADMA2009, IDA '2011 e ECMLPKDD'2015 e ECMLPKDD 2025. Foi track chair ACM SAC de 2007 a 2018. Organizou uma série de Workshops sobre Descoberta de Conhecimento de fluxos de dados no ECMLPKDD, ICML, e no ACM SIGKDD. É autor de vários livros em Data Mining e autoria de uma monografia sobre Descoberta de Conhecimento a partir de fluxos de Dados. É autor de mais de 250 papéis peer-reviewed em áreas relacionadas com a aprendizagem automática, aprendizagem de dados em tempo real e fluxos de dados. É membro do conselho editorial de revistas internacionais ML, DMKD, TKDE, IDA, NGC e KAIS. Supervisionou mais de 15 estudantes de doutoramento e 50 alunos de mestrado.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Gama
  • Cargo

    Investigador Coordenador
  • Desde

    01 abril 2009
019
Publicações

2025

Early Failure Detection for Air Production Unit in Metro Trains

Autores
Zafra, A; Veloso, B; Gama, J;

Publicação
HYBRID ARTIFICIAL INTELLIGENT SYSTEM, PT I, HAIS 2024

Abstract
Early identification of failures is a critical task in predictive maintenance, preventing potential problems before they manifest and resulting in substantial time and cost savings for industries. We propose an approach that predicts failures in the near future. First, a deep learning model combining long short-term memory and convolutional neural network architectures predicts signals for a future time horizon using real-time data. In the second step, an autoencoder based on convolutional neural networks detects anomalies in these predicted signals. Finally, a verification step ensures that a fault is considered reliable only if it is corroborated by anomalies in multiple signals simultaneously. We validate our approach using publicly available Air Production Unit (APU) data from Porto metro trains. Two significant conclusions emerge from our study. Firstly, experimental results confirm the effectiveness of our approach, demonstrating a high fault detection rate and a reduced number of false positives. Secondly, the adaptability of this proposal allows for the customization of configuration of different time horizons and relationship between the signals to meet specific detection requirements.

2025

Decision-making systems improvement based on explainable artificial intelligence approaches for predictive maintenance

Autores
Rajaoarisoa, L; Randrianandraina, R; Nalepa, GJ; Gama, J;

Publicação
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Abstract
To maintain the performance of the latest generation of onshore and offshore wind turbine systems, a new methodology must be proposed to enhance the maintenance policy. In this context, this paper introduces an approach to designing a decision support tool that combines predictive capabilities with anomaly explanations for effective IoT predictive maintenance tasks. Essentially, the paper proposes an approach that integrates a predictive maintenance model with an explicative decision-making system. The key challenge is to detect anomalies and provide plausible explanations, enabling human operators to determine the necessary actions swiftly. To achieve this, the proposed approach identifies a minimal set of relevant features required to generate rules that explain the root causes of issues in the physical system. It estimates that certain features, such as the active power generator, blade pitch angle, and the average water temperature of the voltage circuit protection in the generator's sub-components, are particularly critical to monitor. Additionally, the approach simplifies the computation of an efficient predictive maintenance model. Compared to other deep learning models, the identified model provides up to 80% accuracy in anomaly detection and up to 96% for predicting the remaining useful life of the system under study. These performance metrics and indicators values are essential for enhancing the decision-making process. Moreover, the proposed decision support tool elucidates the onset of degradation and its dynamic evolution based on expert knowledge and data gathered through Internet of Things (IoT) technology and inspection reports. Thus, the developed approach should aid maintenance managers in making accurate decisions regarding inspection, replacement, and repair tasks. The methodology is demonstrated using a wind farm dataset provided by Energias De Portugal.

2025

Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset

Autores
Jakobs, M; Veloso, B; Gama, J;

Publicação
CoRR

Abstract

2025

A Deep Learning Framework for Medium-Term Covariance Forecasting in Multi-Asset Portfolios

Autores
Reis, P; Serra, AP; Gama, J;

Publicação
CoRR

Abstract

2025

On-device edge learning for IoT data streams: a survey

Autores
Lourenço, A; Rodrigo, J; Gama, J; Marreiros, G;

Publicação
CoRR

Abstract

Teses
supervisionadas

2023

Comparative Study of VAE and GAN Based Models for Graph Anomaly Detection

Autor
Diogo Gomes Abreu

Instituição
UP-FEP

2023

Applied Machine Learning Fairness in Business to Consumer Services Industry

Autor
Nuno Filipe Loureiro Paiva

Instituição
UP-FEP

2023

Causal Reasoning in Data

Autor
Ana Rita Dias Nogueira

Instituição
UP-FEP

2023

Instalação e validação de algoritmos no NDtech para deteção de anomalias

Autor
Gustavo Nuno Prata Baldaia

Instituição
UP-FEP

2023

Mobility Patterns from Data

Autor
Thiago de Andrade Silva

Instituição
UP-FEP