Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Fechar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Elsa Ferreira Gomes
  • Cluster

    Informática
  • Cargo

    Investigador Afiliado
  • Desde

    01 novembro 2016
Publicações

2019

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Autores
Nogueira, DM; Ferreira, CA; Gomes, EF; Jorge, AM;

Publicação
Journal of Medical Systems

Abstract

2018

Studying Programming Students Motivation using Association Rules

Autores
Tavares, PC; Gomes, EF; Henriques, PR;

Publicação
Proceedings of the 10th International Conference on Computer Supported Education

Abstract

2017

A Computer Platform to Increase Motivation in Programming Students - PEP

Autores
Tavares, PC; Henriques, PR; Gomes, EF;

Publicação
CSEDU 2017 - Proceedings of the 9th International Conference on Computer Supported Education, Volume 1, Porto, Portugal, April 21-23, 2017.

Abstract

2016

Automatic Classification of Anuran Sounds Using Convolutional Neural Networks

Autores
Colonna, J; Peet, T; Ferreira, CA; Jorge, AM; Gomes, EF; Gama, J;

Publicação
Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, C3S2E '16, Porto, Portugal, July 20-22, 2016

Abstract
Anurans (frogs or toads) are closely related to the ecosystem and they are commonly used by biologists as early indicators of ecological stress. Automatic classification of anurans, by processing their calls, helps biologists analyze the activity of anurans on larger scale. Wireless Sensor Networks (WSNs) can be used for gathering data automatically over a large area. WSNs usually set restrictions on computing and transmission power for extending the network's lifetime. Deep Learning algorithms have gathered a lot of popularity in recent years, especially in the field of image recognition. Being an eager learner, a trained Deep Learning model does not need a lot of computing power and could be used in hardware with limited resources. This paper investigates the possibility of using Convolutional Neural Networks with Mel-Frequency Cepstral Coefficients (MFCCs) as input for the task of classifying anuran sounds. © 2016 ACM.

2016

Using Smartphones to Classify Urban Sounds

Autores
Gomes, EF; Batista, F; Jorge, AM;

Publicação
Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, C3S2E '16, Porto, Portugal, July 20-22, 2016

Abstract
The aim of this work is to develop an application for Android able to classifying urban sounds in a real life context. It also enables the collection and classification of new sounds. To train our classifier we use the UrbanSound8K data set available online. We have used a hybrid approach to obtain features, by combining SAX-based multiresolution motif discovery with Mel-Frequency Cepstral Coefficients (MFCC). We also describe different configurations of motif discovery for defining attributes and compare the use of Random Forest and SVM algorithms on this kind of data. Copyright 2016 ACM.

Teses
supervisionadas

2019

Análise e previsão de acidentes rodoviários usando data mining

Autor
BRUNO MIGUEL FERREIRA TEIXEIRA

Instituição
IPP-ISEP

2018

Desenvolvimento de aplicação móvel multiplataforma (Apache Cordova vs Xamarin)

Autor
DIOGO MIGUEL RODRIGUES E SILVA

Instituição
IPP-ISEP

2018

Deteção de arritmias cardíacas em eletrocardiogramas usando deep learning

Autor
GABRIEL MOREIRA DA ROCHA

Instituição
IPP-ISEP

2018

Aplicação Android de tracking e recomendação automática de rotas

Autor
TIAGO MARQUES OLIVEIRA

Instituição
IPP-ISEP

2018

Ferramenta de Data Mining para Dados Educacionais

Autor
DIOGO MANUEL PEREIRA VIEIRA

Instituição
IPP-ISEP