Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
Split computing and early exiting for deep learning applications: Survey and research challenges
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …
neural networks (DNNs) to execute complex inference tasks such as image classification …
[图书][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation–related stroke
Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it
goes undetected. If new-onset AF could be predicted, targeted screening could be used to …
goes undetected. If new-onset AF could be predicted, targeted screening could be used to …
Simple convolutional neural network on image classification
T Guo, J Dong, H Li, Y Gao - 2017 IEEE 2nd International …, 2017 - ieeexplore.ieee.org
In recent years, deep learning has been used in image classification, object tracking, pose
estimation, text detection and recognition, visual saliency detection, action recognition and …
estimation, text detection and recognition, visual saliency detection, action recognition and …
Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation
M Hashemi - Journal of Big Data, 2019 - Springer
The input to a machine learning model is a one-dimensional feature vector. However, in
recent learning models, such as convolutional and recurrent neural networks, two-and three …
recent learning models, such as convolutional and recurrent neural networks, two-and three …
Lung nodule detection in CT images using deep convolutional neural networks
R Golan, C Jacob, J Denzinger - 2016 international joint …, 2016 - ieeexplore.ieee.org
Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great
importance for the successful diagnosis and treatment of lung cancer. Due to improvements …
importance for the successful diagnosis and treatment of lung cancer. Due to improvements …
EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
Deep learning in robotics: a review of recent research
HA Pierson, MS Gashler - Advanced Robotics, 2017 - Taylor & Francis
Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …
application of deep artificial neural networks to robotic systems, with at least 30 papers …
Transfer learning-assisted multi-resolution breast cancer histopathological images classification
Breast cancer is one of the leading death cause among women nowadays. Several methods
have been proposed for the detection of breast cancer. Various machine learning-based …
have been proposed for the detection of breast cancer. Various machine learning-based …