Results 231 to 240 of about 102,906 (305)

Explainable Temporal Deep Learning for EEG-Based Depression Detection Using Resting-State Brain Dynamics. [PDF]

Int J Methods Psychiatr Res
Naeim M, Atadokht A.
europepmc   +1 more source

Explainable AI in Pharmaceutics: Grad-CAM Analysis of Surface Dissolution Imaging Using Convolutional Neural Networks. [PDF]

Pharmaceutics
Al-Baghdadi A, Pacławski A, Szlęk J, Mendyk A.   +3 more
europepmc   +1 more source

Herakleja - Heraklov grad?

Latina et Graeca, 2017
Barbara Pavlek
doaj  

Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease. [PDF]

PLoS One
Park JB, Shin Y, Kim J, Kim YJ, Lee SB, Kim EH, Kim JW, Kim SK, Kim HS, Kim YG.   +9 more
europepmc   +1 more source

Vitez in grad

, 2005
openaire   +1 more source

Grad-CAM-Based Explainable Artificial Intelligence Related to Medical Text Processing

Bioengineering, 2023
The opacity of deep learning makes its application challenging in the medical field. Therefore, there is a need to enable explainable artificial intelligence (XAI) in the medical field to ensure that models and their results can be explained in a manner ...
Katsuhiko Ogasawara, Hongjian Zhang
exaly   +2 more sources

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A Policy Grad Grad Grad Grad ient Reinforcement Learning Algorithm with Fuzzy Function Approximation

2004 IEEE International Conference on Robotics and Biomimetics, 2005
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuous form due to large state or action spaces. In this paper, the generalisation of reinforcement learning to continuous state space is investigated by using a policy gradient approach. Fuzzy logic is used as a function approximation in the generalisation.
Dongbing Gu, Erfu Yang
openaire   +1 more source

Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging

Journal of Neuroscience Methods, 2021
BACKGROUND Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation.
Yunyan Zhang
exaly   +2 more sources