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About

Brief Biographical History: 1994 concluded the BSc degree in Electrical Engineering, Institute if Engineering of Coimbra, Polytechnic Institute of Coimbra, Portugal. 1996 concluded the Licenciatura degree in Electrical and Computer Engineering, Faculty of Engineering, the University of Porto, Portugal. 1999 concluded the MSc degree in Electrical and Computer Engineering, Faculty of Engineering, the University of Porto, Portugal. 2006 concluded the Ph.D. degree in Electrical Engineering, Faculty of Engineering, the University of Trás-dos-Montes e Alto Douro, Portugal.

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Publications

2019

A Review on Relations Extraction in Police Reports

Authors
Carnaz, G; Quaresma, P; Nogueira, VB; Antunes, M; Fonseca Ferreira, NM;

Publication
Advances in Intelligent Systems and Computing - New Knowledge in Information Systems and Technologies

Abstract

2019

Comparison of Evolutionary Algorithms for Coordination of Cooperative Bioinspired Multirobots

Authors
Saraiva, AA; Silva, FVN; Sousa, JVM; Fonseca Ferreira, NM; Valente, A; Soares, S;

Publication
New Knowledge in Information Systems and Technologies - Volume 2, World Conference on Information Systems and Technologies, WorldCIST 2019, Galicia, Spain, 16-19 April

Abstract

2019

Comparison of Evolutionary Algorithms for Coordination of Cooperative Bioinspired Multirobots

Authors
Saraiva, AA; Silva, FVN; Sousa, JVM; Ferreira, NMF; Valente, A; Soares, S;

Publication
Dynamic Programming for Impulse Feedback and Fast Controls - Lecture Notes in Control and Information Sciences

Abstract

2019

A GENERALIZED EDUCATIONAL ROBOT BASED ON MATLAB/ROS/ROBOTIC PLATFORM

Authors
Rosillo-Guerrero, N; Montés, N; Fonseca Ferreira, NM;

Publication
INTED2019 Proceedings

Abstract

2019

Classification of images of childhood pneumonia using convolutional neural networks

Authors
Saraiva, AA; Fonseca Ferreira, NM; de Sousa, LL; Costa, NJC; Sousa, JVM; Santos, DBS; Valente, A; Soares, S;

Publication
BIOIMAGING 2019 - 6th International Conference on Bioimaging, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019

Abstract
In this paper we describe a comparative classification of Pneumonia using Convolution Neural Network. The database used was the dataset Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification made available by (Kermany, 2018) with a total of 5863 images, with 2 classes: normal and pneumonia. To evaluate the generalization capacity of the models, cross-validation of k-fold was used. The classification models proved to be efficient compared to the work of (Kermany et al., 2018) which obtained 92.8 % and the present work had an average accuracy of 95.30 %.