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pro vyhledávání: '"Ribeiro, Bernardete"'
Condition-Based Maintenance is pivotal in enabling the early detection of potential failures in engineering systems, where precise prediction of the Remaining Useful Life is essential for effective maintenance and operation. However, a predominant fo
Externí odkaz:
http://arxiv.org/abs/2406.12914
Autor:
Phong, Nguyen Huu, Ribeiro, Bernardete
Recognizing human actions in video sequences, known as Human Action Recognition (HAR), is a challenging task in pattern recognition. While Convolutional Neural Networks (ConvNets) have shown remarkable success in image recognition, they are not alway
Externí odkaz:
http://arxiv.org/abs/2302.09187
Autor:
Phong, Nguyen Huu, Ribeiro, Bernardete
Publikováno v:
RECPAD 2017
In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are ve
Externí odkaz:
http://arxiv.org/abs/2205.12261
Convolutional Neural Networks (ConvNets or CNNs) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computationally expensi
Externí odkaz:
http://arxiv.org/abs/2205.10456
Autor:
Marques, Armando E., Parreira, Tomás G., Pereira, André F.G., Ribeiro, Bernardete M., Prates, Pedro A.
Publikováno v:
In International Journal of Solids and Structures 15 October 2024 303
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In Artificial Intelligence In Medicine April 2024 150
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In Applied Soft Computing March 2024 153
Convolutional Spiking Neural Networks targeting learning and inference in highly imbalanced datasets
Publikováno v:
In Pattern Recognition Letters August 2024
Publikováno v:
In Expert Systems With Applications January 2024 235
Autor:
Phong, Nguyen Huu, Ribeiro, Bernardete
Publikováno v:
IbPRIA 2019: Pattern Recognition and Image Analysis
Capsule Networks face a critical problem in computer vision in the sense that the image background can challenge its performance, although they learn very well on training data. In this work, we propose to improve Capsule Networks' architecture by re
Externí odkaz:
http://arxiv.org/abs/2007.15167