Prediction model of urban epidemic prevention and control based on multivariate Bi-A-LSTM

The Novel Coronavirus outbreak has since spread rapidly across the globe, seriously affecting the quality of life and economic development of all countries. By predicting the epidemic situation in a certain area, government departments can take corresponding measures to prevent and control the epidemic according to the forecast results. However, with the implementation of various prevention and control measures, vaccination and the impact of virus mutation, the traditional epidemic model and regression model have limitations in the prediction performance, there is a large error in the prediction accuracy. To improve the prediction accuracy, this paper proposes an Attentional mechanism-based LSTM network (A-LSTM), which takes the multiple factors affecting the epidemic trend as the input of the model. Bidirectional A-LSTM is constructed by a-LSTM neural network unit, and the best fitting degree is obtained by training in bidirectional A-LSTM network. Multivariate Bi-A-LSTM epidemic prevention and control prediction model was obtained. In this paper, the actual data as reference, the average absolute error, average absolute percentage error, root mean square error as the evaluation index of the model, and the improved model and other models are compared, experimental results show that the improved model is more accurate than the traditional model in prediction accuracy. © 2022 IEEE.

COVID-19 Detection Method Based on Attentional Mechanism and LSTM

Since 2020, the Novel Coronavirus virus, which can cause upper respiratory and lung infections and even kill in severe cases, has been ravaging the globe. Rapid diagnostic tests have become one of the main challenges due to the severe shortage of test kits. This article proposes a model combining Long short-term Memory (LSTM) and Convolutional Block Attention Module for detection of COVID-19 from chest X-ray images. In this article, chest X-ray images from the COVID-19 radiology standard data set in the Kaggle repository were used to extract features by MobileNet, VGG19, VGG16 and ResNet50. CBAM and LSTM were used for classifcation detection. The simulation results showed that the experimental results showed that VGG16–CBAM–LSTM combination was the best combination to detect and classify COVID-19 from chest X-ray images. The classification accuracy of VGG-16-CBAM-LSTM combination was 95.80% for COVID-19, pneumonia and normal. The sensitivity and specificity of the combination were 96.54% and 98.21%. The F1 score was 94.11%. The CNN model proposed in this article contributes to automated screening of COVID-19 patients and reduces the burden on the healthcare delivery framework. © 2022, Springer Nature Switzerland AG.

Cropping and attention based approach for masked face recognition.

The global epidemic of COVID-19 makes people realize that wearing a mask is one of the most effective ways to protect ourselves from virus infections, which poses serious challenges for the existing face recognition system. To tackle the difficulties, a new method for masked face recognition is proposed by integrating a cropping-based approach with the Convolutional Block Attention Module (CBAM). The optimal cropping is explored for each case, while the CBAM module is adopted to focus on the regions around eyes. Two special application scenarios, using faces without mask for training to recognize masked faces, and using masked faces for training to recognize faces without mask, have also been studied. Comprehensive experiments on SMFRD, CISIA-Webface, AR and Extend Yela B datasets show that the proposed approach can significantly improve the performance of masked face recognition compared with other state-of-the-art approaches.