Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Yacoubi, Mounîm A. El"'
As a representative of a new generation of biometrics, vein identification technology offers a high level of security and convenience. Convolutional neural networks (CNNs), a prominent class of deep learning architectures, have been extensively utili
Externí odkaz:
http://arxiv.org/abs/2405.12721
Accessibility measures how well a location is connected to surrounding opportunities. We focus on accessibility provided by Public Transit (PT). There is an evident inequality in the distribution of accessibility between city centers or close to main
Externí odkaz:
http://arxiv.org/abs/2310.04348
Standard objective functions used during the training of neural-network-based predictive models do not consider clinical criteria, leading to models that are not necessarily clinically acceptable. In this study, we look at this problem from the persp
Externí odkaz:
http://arxiv.org/abs/2009.10514
Progress in the biomedical field through the use of deep learning is hindered by the lack of interpretability of the models. In this paper, we study the RETAIN architecture for the forecasting of future glucose values for diabetic people. Thanks to i
Externí odkaz:
http://arxiv.org/abs/2009.04524
Publikováno v:
BIBE 2019: 19th International Conference on Bioinformatics and Bioengineering
This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose v
Externí odkaz:
http://arxiv.org/abs/2009.04410
Publikováno v:
2019 International Joint Conference on Neural Networks (IJCNN)
Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to asse
Externí odkaz:
http://arxiv.org/abs/2009.04409
The adoption of deep learning in healthcare is hindered by their "black box" nature. In this paper, we explore the RETAIN architecture for the task of glusose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-ne
Externí odkaz:
http://arxiv.org/abs/2009.03732
Publikováno v:
ICONIP 2019: Neural Information Processing pp 510-521
In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction
Externí odkaz:
http://arxiv.org/abs/2009.03722
Due to the sensitive nature of diabetes-related data, preventing them from being shared between studies, progress in the field of glucose prediction is hard to assess. To address this issue, we present GLYFE (GLYcemia Forecasting Evaluation), a bench
Externí odkaz:
http://arxiv.org/abs/2006.15946
Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data fro
Externí odkaz:
http://arxiv.org/abs/2006.15940