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Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of building samples. While some building datasets are available for model training, there remains a lack of high-quality building datasets covering urban and rural areas in China. To fill this gap, this study creates a high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six Chinese cities. The dataset comprises 5,175 pairs of 512 × 512 image tiles, covering 573.17 km2. It contains 170,015 buildings, with 84.8% of the buildings in urban areas and 15.2% in rural areas. The usability of the GF-7 Building dataset has been proved with seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that the GF-7 building dataset can be used for building extraction in urban and rural scenarios. The proposed dataset boasts high quality and high diversity. It supplements existing building datasets and will contribute to promoting new algorithms for building extraction, as well as facilitating intelligent building interpretation in China. [ABSTRACT FROM AUTHOR]

Among the existing bearing fault diagnosis algorithms, the diagnosis time of algorithms with high accuracy is relatively long, and the accuracy of some lightweight methods is low compared to other methods. This work proposes a bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features (Bi-BUR) for bearing fault diagnosis, enabling both high accuracy and high-speed characteristics. The Bi-BUR has two levels of classifiers. The first level begins with a signal-to-picture method based on integer binary coding to convert the bearing fault signals into binary pictures; then, the first classification of the converted pictures is performed by the underlying feature extraction-residual network 50, which can extract both the underlying and the advanced features. The second level performs modal decomposition of the signals that are not classified successfully in the first level, performs the second classification with a fully connected classifier inserted into the underlying feature extraction module, and exports the final classification results after weighted summation of the results of each type. The Bi-BUR is simulated on the Case Western Reserve University motor, the self-priming centrifugal pump, and the axial piston hydraulic pump bearing failure datasets. All three experiments achieved more than 95% accuracy in the first level of categorization and 100% diagnosis after the second level of categorization. And the Bi-BUR is faster than other high-accuracy methods. • A bearing fault diagnosis method with fusion of speed and accuracy is considered. • A two-layer bearing fault diagnosis method is proposed. • A signal-to-picture method based on integer binary coding is proposed. • A method to extract both bottom and deep level features is proposed. • The method fills the gap of bearing fault diagnosis with high speed and accuracy. [ABSTRACT FROM AUTHOR]

The importance of automatic pollen recognition has been examined in several areas ranging from paleoclimate studies to some daily practice such as pollen hypersensitivity forecasting. This paper attempts to present an automatic 3D pollen image recognition method based on convolutional neural network. To achieve this purpose, high feature dimensions and complex posture transformation should be taken into account. Therefore, this work focuses on a three-part novel approach: constructing spatial local key points to obtain local stable points of pollen images, computing orientational local binary pattern using local stable points as the inputs, and identifying the pollen grains using convolutional neural network as the classifier. Experiments are performed on two standard pollen image datasets: Confocal-E dataset and Pollenmonitor dataset. It is concluded that the proposed approach can effectively extract the features of pollen images and is robust to posture transformation, slight occlusion, and pollution. [ABSTRACT FROM AUTHOR]

Pose-based action recognition has aroused increasing attention for its broad application prospects and excellent performance. Though the pose-based action recognition methods have been significantly advanced, pose-based action recognition remains a challenging task for various human action categories and subtle changes in human poses. To solve those problems, we propose pose-based multisource networks. First, human pose features are extracted from the raw video, followed by a filtration. Then, using a convolutional neural network (CNN) and long short-term memory (LSTM), the extracted pose sequence is fed into the proposed multisource networks. Subsequently, the CNN-based spatial model processes the relative position in each frame, and the LSTM-based temporal model is built to learn the temporal correlation of pose sequence. Afterward, the temporal model contains three sublevels to fully exploit the subtle information in the temporal domain. Finally, the experimental results verify the effectiveness of the proposed approach on SUB-JHMDB, MPII Cooking Activities, SYSU 3D Human-Object Interaction, and NTU RGB+D. [ABSTRACT FROM AUTHOR]

Prostate magnetic resonance imaging (MRI) is widely used in the diagnosis of prostate cancer and other prostate diseases. The automatic segmentation of images from prostate MRI plays an important role in the auxiliary diagnosis of prostate diseases. Currently, there are two commonly used methods for automatic segmentation of prostate MRI, which are 2D image segmentation and 3D image segmentation. In this paper, a two-stage CNN method for MRI image segmentation of prostate with lesion is proposed. At the first stage, we used a CNN model incorporating the Squeeze-Excitation module to discriminate whether the image contains prostate or not. At the second stage, we proposed a Residual-Attention U-Net for segmentation of images containing prostate. Eventually, the 3D prostate MRI segmentation results are obtained and fully automated segmentation is accomplished. We evaluated our proposed method and other common 2D and 3D segmentation methods on the test dataset and compared their results based on Dice Similarity Coefficient (DSC) value. Our method performed the best and achieved the DSC metric value of 0.860. • Highly automated segmentation method. • Combination of classification and segmentation models based on neural networks. • Improve the segmentation accuracy of MRI images of prostate cancer patients. • The use of two different kinds of Attention Mechanisms. [ABSTRACT FROM AUTHOR]

Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phase, an encoder learns the conditional distribution of the multi-omics data. As a generative model, a decoder takes the conditional distribution and the original feature as inputs to generate the enhanced features. The cancer prognosis prediction model consists of a two-layer graph convolutional neural network and a Cox proportional risk network. The Cox proportional risk network consists of fully connected layers. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. LAGProg improved the C-index values by an average of 8.5% over the state-of-the-art graph neural network method. Moreover, we confirmed that the local augmentation technique could enhance the model's ability to represent multi-omics features, improve the model's robustness to missing multi-omics features, and prevent the model's over-smoothing during training. Finally, based on genes identified through differential expression analysis, we discovered 13 prognostic markers highly associated with breast cancer, among which ten genes have been proved by literature review. • Prediction cancer prognosis based on graph neural network. • Adopting a local augmentation strategy to represent genes' features. • Outperforming the competing methods and mining novel prognostic biomarkers for breast cancer. [ABSTRACT FROM AUTHOR]

With recent advances in human pose estimation and human skeleton capture systems, pose-based action recognition has drawn lots of attention among researchers. Although most existing action recognition methods are based on convolutional neural network and long short-term memory, which present outstanding performance, one of the shortcomings of these methods is that they lack the ability to explicitly exploit the rich spatial-temporal information between the skeletons in the behavior, so they are not conducive to improving the accuracy of action recognition. To better address this issue, the two-stream spatial-temporal neural networks for pose-based action recognition is introduced. First, the pose features that are extracted from the raw video are processed by an action modeling module. Then, the temporal information and the spatial information, in the form of relative speed and relative distance, are fed into the temporal neural network and the spatial neural network, respectively. Afterward, the outputs of two-stream networks are fused for better action recognition. Finally, we perform comprehensive experiments on the SUB-JHMDB, SYSU, MPII-Cooking, and NTU RGB+D datasets, the results of which demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In recent years, progressive application of convolutional neural networks in image processing has successfully filtered into medical diagnosis. As a prerequisite for images detection and classification, object segmentation in medical images has attracted a great deal of attention. This study is based on the fact that most of the analysis of pathological diagnoses requires nuclei detection as the starting phase for obtaining an insight into the underlying biological process and further diagnosis. In this paper, we introduce an embedded attention model in multi-bridge Wnet (AMB-Wnet) to achieve suppression of irrelevant background areas and obtain good features for learning image semantics and modality to automatically segment nuclei, inspired by the 2018 Data Science Bowl. The proposed architecture, consisting of the redesigned down sample group, up-sample group, and middle block (a new multiple-scale convolutional layers block), is designed to extract different level features. In addition, a connection group is proposed instead of skip-connection to transfer semantic information among different levels. In addition, the attention model is well embedded in the connection group, and the performance of the model is improved without increasing the amount of calculation. To validate the model's performance, we evaluated it using the BBBC038V1 data sets for nuclei segmentation. Our proposed model achieves 85.83% F1-score, 97.81% accuracy, 86.12% recall, and 83.52% intersection over union. The proposed AMB-Wnet exhibits superior results compared to the original U-Net, MultiResUNet, and recent Attention U-Net architecture. [ABSTRACT FROM AUTHOR]

• LSTM modeling on each intrinsic mode function improves prediction accuracy. • CEEMDAN is optimized by calculating fuzzy entropy. • Deeper network structure is constructed by combining residual networks and LSTM. • The residual learning mechanism and gating structure increase prediction accuracy. • Hydrodynamic forecasting is considered for electric fields under extreme conditions. As the global trend of frequent extreme weather grows more pronounced, and factors such as wind and light grow more uncertain, the power system encounters a severe test of extreme operating conditions. Hydraulic scheduling, as a factor that can be controlled by humans, requires an accurate and feasible method of predicting future hydraulic demands. This work proposes a hydrodynamic scheduling demand forecasting method that combines fuzzy entropy, complete ensemble empirical mode decomposition with adaptive noise, residual networks, and long short-term memory networks. By introducing fuzzy entropy in complete ensemble empirical mode decomposition with adaptive noise, more complex signals in modal components can be extracted and classification accuracy can be increased. The combination of residual and long short-term memory networks contributes to a better representation of the features in the data, thus model expressive power is improved. The experimental results show that the mean square error of the results of the method in this work reaches 141.6576 and the mean absolute error is 6.2101, with 98.4 % and 91.6 % reduction than the optimal results of the compared methods. [ABSTRACT FROM AUTHOR]