… convolutional networks – the so-called hybrid convolutional neural network (HCNN). HCNN-…
, combining the advantages of LeCun’s convolutional networks with a rapid training seems …

… hybrid convolutional network … by hybrid convolution. Firstly, the average spectra were
extracted from the region of interests (ROIs) and processed with one-dimensional (1D) convolution …

… Although deep convolutional neural networks (… hybrid convolutional network (LW-HCN) to
segment the liver and its tumors in CT volumes. Instead of combining a 2D and a 3D networks …

… So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based
hybrid ID framework that predicts and classifies malicious cyberattacks in the network. …

… [19] introduce a hybrid PCA technique with optimized algorithm named whale optimization
… Convolutional Networks are a type of Neural Network that has shown to be particularly good …

… We name the hybrid convolutional network as HybridNet, which gets benefits from both
ResNet and VGGNet. Particularly, we do some control experiments in Section 4.2.1 to evaluate …

… novel architecture, named Hybrid CNN, which is composed of A-Net and S-Net. They describe
… propose a novel convolutional neural network-based architecture, named Hybrid CNN, for …

… In particular, we propose an effective hybrid fully convolutional network … convolutional
networks is firstly described as preparation. Then, we propose hybrid fully convolutional networks …

… In this letter, we propose a hybrid convolution network (HCN) to … Net, and VGG suit for
different situations and they are very complementary, all of them are included in our hybrid network …

… of large-scale network sequences. This work proposes a hybrid prediction method named …
of the Savitzky–Golay filter, the temporal convolutional network (TCN), and the long short-term …