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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
007
Publications

2023

Estimating the Likelihood of Financial Behaviours Using Nearest Neighbors

Authors
Mendes-Neves, T; Seca, D; Sousa, R; Ribeiro, C; Mendes-Moreira, J;

Publication
Computational Economics

Abstract
AbstractAs many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts’ efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.

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, ADMA 2022, PT II

Abstract

2022

Tracking Data Visual Representations for Sports Broadcasting Enrichment

Authors
Couceiro, M; Lima, IR; Ulisses, A; Neves, TM; Moreira, JM;

Publication
Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support, icSPORTS 2022, Valletta, Malta, October 27-28, 2022.

Abstract
The broadcast of audio-video sports content is a field with increasingly larger audiences demanding higher quality content and involvement. This growth creates the necessity to develop more content to engage the users and keep this trend. Otherwise, it may stall or even diminish. Therefore, enhancing the user experience, engagement, and involvement during live sports event broadcasts is of utmost importance. This paper proposes a solution to extract event’s information from video, resorting to Computer Vision techniques and Deep Learning algorithms. More specifically, the project encompassed the definition and implementation of field registration, object detection and tracking tasks. Focusing on football sports events, a novel dataset combining several video sources was created and used for analysis and metadata extraction. In particular, the proposed solution can detect and track players with acceptable precision using state-of-the-art methods, like YOLOv5 and DeepSORT. Furthermore, resorting to unsupervised learning techniques, the system provides team segmentation based on the colour of the players’ kits. A series of visual representations regarding the players’ movements on the field enables broadcast enrichment and increased user experience. The presented solution is framed in the H2020 DataCloud project and will be deployed in a cloud environment simplifying its access and utilisation. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Supervised
thesis

2022

Time-To-Event Prediction

Author
Maria José Gomes Pedroto

Institution
UP-FEUP

2022

Unsupervised learning approach for predictive maintenance in power transformers

Author
Duarte Miguel de Novo Faria

Institution
UP-FEUP

2022

Energy-Computing Efficient Classification Techniques for Mobile-Based HAR Systems

Author
Paulo Jorge Silva Ferreira

Institution
UP-FEUP

2022

Player Tracking System for Sports Events based on Computer Vision

Author
Murilo de Mendonça Couceiro

Institution
UP-FEUP

2022

Transportation Mode Detection for Real Mobile Crowdsourced Datasets

Author
Akilu Rilwan Muhammad

Institution
UP-FEUP