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Detalhes

Detalhes

  • Nome

    Carlos Ferreira
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2010
002
Publicações

2023

Modeling the Ink Tuning Process Using Machine Learning

Autores
Costa, C; Ferreira, CA;

Publicação
Intelligent Data Engineering and Automated Learning – IDEAL 2023 - Lecture Notes in Computer Science

Abstract

2022

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Autores
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;

Publicação
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

Abstract
Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.

2022

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

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

Publicação
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

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

Publicação
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

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

Publicação
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).

Teses
supervisionadas

2022

Avaliação de Jogadores e Equipas de Basquetebol usando Machine Learning

Autor
LUÍS RODOLFO NOGUEIRA E SILVA

Instituição
IPP-ISEP

2022

Machine Learning Aplicado à Teoria de Portefólio

Autor
GONÇALO MENESES DE SOUSA

Instituição
IPP-ISEP

2022

ANÁLISE E MODELAÇÃO DO MERCADO DE AÇÕES

Autor
MÁRIO ROBERTO DOS REIS GOMES

Instituição
IPP-ISEP

2021

Métodos de previsão de tendência de mercados de ações

Autor
MIGUEL ALEXANDRE BORGES DA SILVA

Instituição
IPP-ISEP

2021

Football Learning - Avaliação de Jogadores de Futebol usando Machine Learning

Autor
DOMINGOS BERNARDINO PEREIRA DA COSTA

Instituição
IPP-ISEP