Deep learning-based methods based on Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in hyperspectral image (HSI) classification. Most of these approaches are only based on 2-D CNN or 3-D CNN. It is dramatic from the literature that using just 2-D CNN may result in missing channel relationship information and using just 3-D CNN may make the model very complex. Moreover, the existing network models do not pay enough attention to extracting spectral-spatial correlation information. To address these issues, we propose a deep collaborative attention network for HSI classification by combining 2-D CNN and 3-D CNN (CACNN). Specifically, we first extract spectral-spatial features by using 2-D CNN and 3-D CNN, respectively, and then use a "NonLocalBlock" to combine these two kinds of features. This block serves as a typical spatial attention mechanism and makes salient features be emphasized. Then, we propose a "Conv Block" that is similar to the lightweight dense block to extract correlation information contained in the feature maps. Finally, we consider a deep multilayer feature fusion strategy, and thereby combine the features of different hierarchical layers to extract the strong correlated spectral-spatial information among them. To test the performance of CACNN approach, several experiments are performed on four well-known hyperspectral images. The results are compared with the state-of-the-art approaches and satisfactory performance is obtained by our proposed method.

Automatic analysis of epileptic encephalography (EEG) signals with intelligent models can greatly reduce the workload of doctors. However, the lack of data, insufficient labels, and inconsistent data distribution in real-world scenarios significantly affect the performance of intelligent models. Transfer learning plays an important role in solving the above problems but some challenges remain. First, while various feature extraction methods are available to extract features from the original epilepsy signal, it is difficult to determine which features are effective. Second, transfer learning may lead to domain information loss since the original feature representation from different domains is changed. Third, most of the existing models lack transparency to provide medical practitioners confidence of use. To this end, this paper proposes the novel method Multi-view Information Preservation Transfer Representation Learning based on Fuzzy Systems (MIP-TRL-FS) to address the issues. First, MIP-TRL-FS utilizes multiple views to get rid of the feature selection process. Second, information preservation techniques are utilized to maintain the data information from the aspects of sample level and feature level, thus minimizing information loss during the transfer learning process. Third, by using Takagi-Sugeno-Kang Fuzzy Systems (TSK-FS) as the base model, the output of the proposed method can be interpreted linguistically with IF-THEN rules to makes the model transparent. Extensive experiments were conducted on the CHB-MIT dataset and the results demonstrate the effectiveness of the proposed method.