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Details

  • Name

    Carlos Ferreira
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2010
002
Publications

2022

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Authors
Oliveira, J; Renna, F; Costa, PD; Nogueira, M; Oliveira, C; Ferreira, C; Jorge, A; Mattos, S; Hatem, T; Tavares, T; Elola, A; Rad, AB; Sameni, R; Clifford, GD; Coimbra, MT;

Publication
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

Abstract

2022

The robustness of Random Forest and Support Vector Machine Algorithms to a Faulty Heart Sound Segmentation

Authors
Oliveira, J; Nogueira, DM; Ferreira, CA; Jorge, AM; Coimbra, MT;

Publication
44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022

Abstract

2022

Temporal Nodes Causal Discovery for in Intensive Care Unit Survival Analysis

Authors
Nogueira, AR; Ferreira, CA; Gama, J;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract
In hospital and after ICU discharge deaths are usual, given the severity of the condition under which many of them are admitted to these wings. Because of this, there is an urge to identify and follow these cases closely. Furthermore, as ICU data is usually composed of variables measured in varying time intervals, there is a need for a method that can capture causal relationships in this type of data. To solve this problem, we propose ItsPC, a causal Bayesian network that can model irregular multivariate time-series data. The preliminary results show that ItsPC creates smaller and more concise networks while maintaining the temporal properties. Moreover, its irregular approach to time-series can capture more relationships with the target than the Dynamic Bayesian Networks.

2022

Semi-causal decision trees

Authors
Nogueira, AR; Ferreira, CA; Gama, J;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE

Abstract
Typically, classification algorithms use correlation analysis to make decisions. However, these decisions and the models they learn are not easily understandable for the typical user. Causal discovery is the field that studies the means to find causal relationships in observational data. Although highly interpretable, causal discovery algorithms tend to not perform so well in classification problems. This paper aims to propose a hybrid decision tree approach (SC tree) that mixes causal discovery with correlation analysis through the implementation of a custom metric to split the data in the tree's construction (Semi-causal gain ratio). In the results, the proposed methodology obtained a significant performance improvement (11.26% mean error rate) when compared to several causal baselines CDT-PS (23.67% ) and CDT-SPS (25.14%), matching closely the performance of J48 (10.20%), used as a correlation baseline, in ten binary data sets. Besides, when compared with PC in discrete data sets, the proposed approach obtained substantial improvement (16.17% against 28.07% in terms of mean error rate).

2021

A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population

Authors
Oliveira, M; Oliveira, J; Camacho, R; Ferreira, C;

Publication
BIOSIGNALS: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 4: BIOSIGNALS

Abstract

Supervised
thesis

2021

Previsão da Evolução do Covid-19 por Região

Author
MARCELO ZEFERINO VIEIRA FERREIRA

Institution
IPP-ISEP

2021

Football Learning - Avaliação de Jogadores de Futebol usando Machine Learning

Author
DOMINGOS BERNARDINO PEREIRA DA COSTA

Institution
IPP-ISEP

2020

Sistema de Recomendação em tempo real para E-commerce

Author
RICARDO JORGE COELHO LIMA

Institution
IPP-ISEP

2020

Recommender Systems for Grocery Retail - A Machine Learning Approach

Author
XAVIER DOS SANTOS SILVA

Institution
IPP-ISEP

2020

Validação Farmacêutica Automática de Prescrições Médicas em Ambiente Hospitalar

Author
ANDRÉ MANUEL GONÇALVES PORTELA FALHAS DA COSTA

Institution
IPP-ISEP