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The 2019 coronavirus (COVID‐19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre‐trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X‐ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre‐trained models such as ResNet‐50, Inception V3, VGG‐16, VGG‐19, ResNet50V2, and Xception are used for better feature extraction and Chest X‐ray image classification. The overfitting issue were resolved using K‐fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques. [ABSTRACT FROM AUTHOR]

Automatic classification of ancient books is an important component of the digital platform of ancient books, while automatic classification of ancient books is more challenging. In view of the ancient books script identification task of different ethnic minorities in China, this paper proposes a deep convolutional neural network (CNN) ancient books script identification method with multi-branch structure and spatial pyramid pooling (SPP), called MbSPPVGG. We build a dataset of Chinese ethnic ancient handwritten books, and crop and standardize preprocessing images of ancient books. In order to improve the identification accuracy of ancient books and ability of CNN to perceive multi-scale changes in image, bottom-level and high-level features of CNN are merged by multi-branch structure to enhance the networks expression ability, and then use SPP to multi-scale de-dimensionality of convolutional features, increase the spatial scale invariance of CNN. The introduction of multi-branch structure and SPP in the CNN model constitutes a new ancient books identification model. The experimental results show that the precision, recall and F1-score of MbSPPVGG model are all 99.94%. As demonstrated by comparison experiments, the classification accuracy of MbSPPVGG model is better than that of state-of-the-art GhostNet, CSPDenseNet, MixNet and other deep learning methods, and its effectiveness is verified on multiple datasets. [ABSTRACT FROM AUTHOR]

A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset. There are two alternative cross attention models: (1) using the CNN features as the key–value pair, and the Bi-LSTM features as the query, this we called a XA model, and (2) using the Bi-LSTM features as the key–value pair, and the CNN features as the query, this we called a XA_I model; we found empirically for a properly chosen sliding window length, whichever CNN model or Bi-LSTM model performs well in the 1-step ahead prediction, then its features should be used as the query in the cross attention mechanism, thus, the XA achieves better results than XA_I on the East-China dataset, while XA_I achieves better results than XA on the Yalova dataset, and on the 16 MW dataset. Where comparable results using other state-of-the-art models are available on the Yalova dataset and the 16 MW dataset, we found that our XA_I results are superior. Moreover, it is found empirically, that the cross attention mechanism model produces stable k -step ahead predictions, for k ≤ W 2 , where W is the sliding window length. • A novel prediction model for wind generated power based on cross attention mechanism has been proposed. • The proposed method demonstrated on three practical datasets to have superior performance. • The cross attention mechanism based models give a stable multi-step ahead prediction. [ABSTRACT FROM AUTHOR]

• An automated Kashin Beck Disease (KBD) diagnosis algorithm was proposed based on deep learning. • The method focuses on multi-features fusion for detection. The features are extracted from hand X-ray images by DCNN, including global and local features. • Global features represent structures of hand bones, and local features represent subtle edge information from critical regions of metaphysis based on the domain knowledge of KBD. • The model can effectively diagnose KBD and provide substantial benefits to reduce largescale screening costs and missed diagnosis rate. Kashin-Beck Disease (KBD) is a serious endemic bone disease leading to short stature. The early radiological examinations are crucial for potential patients. However, many children in rural China cannot be diagnosed in time due to the shortage of professional orthopedists. In this paper, an algorithm is developed to automatically screening KBD based on hand X-ray images of subjects, which can help the government reducing human resources investment and assisting the poor precisely. The KBD diagnosis method focuses on multi-feature fusion for classification. Two kinds of features presented in X-ray images are extracted by a deep convolutional neural network (DCNN). One is the global features that represent shapes and structures of the whole hand bone. The other is local features that represent edge and texture information from critical regions of the metaphysis. The global features tend to sketch the major informative parts, whereas other fine local features can provide supplementary information. Then both kinds of features are combined and fed into the KBD classifier of a fully connected neural network (FCNN) to obtain diagnostic results. Our research team collected 960 samples in KBD endemic areas of Tibet from 2017 to 2018. The dataset contains 219 KBD positive images and 741 negative images. Experiments indicate that the method based on multi-feature achieves the best average accuracy and sensitivity rate of of 98.5% and 97.6% for diagnosis, which is 4.0% and 7.6% higher than the method with only the global features respectively. The KBD diagnosis method shows that our proposed multi-feature fusion helps to achieve higher diagnosis performance and stability compared with only using global features for detection. The automated KBD diagnosis algorithm provides substantial benefits to reduce large-scale screening costs and missed diagnosis rate. [ABSTRACT FROM AUTHOR]