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Aplicação de Deep Learning em dados refinados para Mineração de Opiniões
(Universidade do Vale do Rio dos Sinos, 2015-02-26)
Deep Learning is a Machine Learning’s sub-area that have achieved satisfactory results in different application areas, implemented by different algorithms, such as Stacked Auto- encoders or Deep Belief Networks. This work ...
Assessing irace for automated machine and deep learning in computer vision
(Universidade Federal do Rio Grande do NorteBrasilUFRNPROGRAMA DE PÓS-GRADUAÇÃO EM TECNOLOGIA DA INFORMAÇÃO, 2021-06-29)
Going deep into schizophrenia with artificial intelligence
(Elsevier, 2022)
Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity ...
Comparação de algoritmos de aprendizagem por reforço profundo na navegação do robô móvel e desvio de trajetória
(Universidade Federal de Santa MariaBrasilUFSMCentro de Tecnologia, 2022-09-23)
This work presents two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. The methodology focus on comparing a Deep-RL technique based on the Deep ...
How to add new knowledge to already trained deep learning models applied to semantic localization
(06/19/2019)
The capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ...
End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA
(2021)
Purpose: To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA).
Deep learning for biological image classification
(2017-11-01)
A number of industries use human inspection to visually classify the quality of their products and the raw materials used in the production process, this process could be done automatically through digital image processing. ...
System-level prognostics and health management: A graph convolutional network-based framework
(SAGE, 2020)
Sensing technologies have been used to gather massive amounts of data to improve system reliability analysis with the use of deep learning. Their use has been mainly focused on specific components or for the whole system, ...