… hybrid deep learning modeling approach, this review mainly focuses on studies that applied
hybrid deep learning for … Other interesting deep learning applications in hydrology, such as …

… Regarding the issues above, an air quality forecasting (mainly predicting PM2.5) method
based on a hybrid deep learning architecture is proposed in this paper. In general, because the …

… methods and different deep learning models. We propose a new hybrid deep learning model
that uses different representations of data and different deep learning model to analyze a …

… We propose a hybrid deep learning model for three types of BT classification (pituitary, …
with nine other pretrained deep learning models using transfer learning techniques. We also …

… Different forms of existing deep learning … a new hybrid deep learning framework by combining
VGG, data augmentation and spatial transformer network (STN) with CNN. This new hybrid …

… Deep learning models, recently applied to large-scale big … This paper proposes a hybrid
deep learning model to efficiently … We use the deep CNN to extract meaningful features from IDS …

… on the proper extraction and the learning data. The success of the deep learning led to many
… human action recognition based on hybrid deep learning model. The proposed approach is …

… 1) We introduce a hybrid user-cloud … deep learning architectures for gender classification
and activity recognition, based on the proposed privacy measure, transfer learning, and deep …

… In order to bridge the aforementioned research gap, this study proposes a new hybrid deep
learning model that uses the CWT preprocessing to combined CNN and gcForest for bearing …

… Compared with conventional PV power forecasting methods, the proposed hybrid deep
learning model in this study has the following advantages. (1) The proposed model addresses …