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Mostrando ítems 11-20 de 1980
Failure mode detection of reinforced concrete shear walls using ensemble deep neural networks
(Springer, 2022)
Reinforced concrete structural walls (RCSWs) are one of the most efficient lateral force-resisting systems used in buildings, providing sufficient strength, stiffness, and deformation capacities to withstand the forces ...
Denoising digital breast tomosynthesis projections using convolutional neural networks
(2021-01-01)
The Digital Breast Tomosynthesis (DBT) projections are obtained with low quality, being essential to use denoising methods to increase the quality of the projections. Currently, deep learning methods have become the ...
Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks
Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition ...
MaxDropout: Deep neural network regularization based on maximum output values
(2020-01-01)
Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few. In lockstep, regularization methods, which ...
Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning
The discrimination of the clutter interfering signal is
a current problem in modern radars’ design, especially in coastal
or offshore environments where the histogram of the background
signal often displays heavy tails. ...
Utilização de deep learning para reconhecimento de gestos com imagens do sensor Kinect nas bases de dados MSRC-12 e NTU RGB+D
(Universidade Federal de Santa MariaBrasilUFSMCentro de Tecnologia, 2021-02-11)
This work presents an application of deep neural networks for gesture recognition through images captured by a Kinect sensor. In order to perform the training of neural networks, two datasets are used: MSRC-12 and NTU RGB ...
Self-improving generative artificial neural network for pseudorehearsal incremental class learning
(Algorithms, 2019)
Deep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid ...
Physics-Informed Deep Equilibrium Models for Solving ODEs
(Florianópolis, SC., 2022)
Exudate detection in fundus images using deeply-learnable features
(2019-01-01)
Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. Convolutional Neural Networks (CNNs) have been used for automatic exudate ...