… A key contribution of this work is to directly learn relational visual features, which indicate
identity similarities, from raw pixels of face pairs with a hybrid deep network. The deep …

… 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 …

… hybrid deep learning”, “hybrid deep learning review”, “hybrid … the hybrid model”, “temporal
classification using the hybrid … using the hybrid model”, and “fusion of deep learning models”. …

… Recently, deep learning-deep neural networks and … Deep learning integrates inherent features
extracted by … Recent evidences reveal that deep learning is a promising tool for traffic flow …

… Thus, we review the ARIMA, LSTM, CNN, Hybrid LSTM and Hybrid CNN models and … as
another deep learning model in terms of predicting accuracy. The LSTM, CNN, and their hybrid …

… hybrid deep learning architecture which is highly efficient for sentiment analysis in resource-poor
languages. We learn … very first attempt where such a deep learning model is used for …

… 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 …

… 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 …

… a hybrid structure which includes Convolutional Neural Network (CNN) and Extreme Learning
… The hybrid architecture makes the most of their advantages: CNN is used to extract the …

… 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 …