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About

About

Areas of research:

- Knowledge discovery

  • Supervised learning   
  • Multiple predictive models
  • Applied knowledge discovery

- Intelligent transportation systems

  • Planning and operations of public transports

Interest
Topics
Details

Details

  • Name

    João Mendes Moreira
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2011
009
Publications

2022

Graph Multi-Head Convolution for Spatio-Temporal Attention in Origin Destination Tensor Prediction

Authors
Bhanu, M; Kumar, R; Roy, S; Mendes-Moreira, J; Chandra, J;

Publication
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2022, PT I

Abstract
Capturing complex spatio-temporal features of thousands of correlated taxi-demand time-series in the city makes the traffic flow prediction problem a challenging task. Hence, several Deep Neural Network (DNN) models have been developed to mimic the latent spatio-temporal behaviour of taxi-demand time-series in a city to improve the prediction results. Despite, good performance of recent DNN based traffic prediction techniques, such models can only identify either adjacent or connected regions with direct or transitive connection; hence they fail to capture spatio-temporal correlation among regions that exhibit implicit or latent connection. Additionally, the dependency of the recent DNN models on recursive components facilitates error propagation during feature aggregation without any counter strategy for it. In view of these existing glitches, we introduce a novel DNN model, graph Multi-Head Convolution for Spatio-Temporal Aggregation (gMHC-STA) which supports capturing spatio-temporal correlation among regions with explicit and implicit connection both. Moreover, gMHC-STA aggregates both spatial and temporal characteristics using multi-head attention; thus overriding recursive RNN or its variant approach to prevent noise propagation. The experimental results of gMHC-STA on two real-world city taxi-demand datasets report minimum of 6.5–10% improvement over the best state-of-the-art on standard benchmark metric in varying experimental conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Improving the Prediction of Age of Onset of TTR-FAP Patients Using Graph-Embedding Features

Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract

2022

Density Estimation in High-Dimensional Spaces: A Multivariate Histogram Approach

Authors
Strecht, P; Mendes-Moreira, J; Soares, C;

Publication
Advanced Data Mining and Applications - Lecture Notes in Computer Science

Abstract

2021

Embedding Traffic Network Characteristics Using Tensor for Improved Traffic Prediction

Authors
Bhanu, M; Mendes Moreira, J; Chandra, J;

Publication
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Abstract

2021

Novelty Detection in Physical Activity

Authors
Leite, B; Abdalrahman, A; Castro, J; Frade, J; Moreira, J; Soares, C;

Publication
ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2

Abstract

Supervised
thesis

2021

Semantic Measures in Large Semantic Graphs

Author
André Fernandes dos Santos

Institution
UP-FCUP

2021

Multicriteria evaluation as a tool for decision support in the design of sustainable routes

Author
Joana Sofia Campos Santos

Institution
UP-FEUP

2021

Text2Icons: using AI to tell a story with icons

Author
Joana Maria Lima Valente

Institution
UP-FCUP

2021

Ensembling Neural Networks for Regression

Author
João Miguel Mendes Ribeiro Agulha

Institution
UP-FEUP

2021

Assessing Risks in Software Projects Through Machine Learning Approaches

Author
André Oliveira Sousa

Institution
UP-FEUP