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Fighting against COVID-19: Innovations and applications Abbreviations AI artificial intelligence CNN convolutional neural network CT computerized tomography CXR chest x-ray radiograph DL deep learning DNN deep neural network DSS decision support system ML machine learning COVID-19 has triggered over 6.90 million deaths worldwide until the cutoff date of April 23, 2023. Furthermore, chest CT can be used to evaluate the response to treatment in COVID-19 patients.[33] Repeat chest CT scans can show whether the lung damage is improving or stabilizing, which can help healthcare professionals to adjust the treatment plan as needed.[34] In this virtual special issue, many CT-based AI models are reported. COLI-Net: deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. [Extracted from the article]

It relies on the synergy between artificial intelligence (AI) and software engineering, improving both software productivity and quality, for example, providing coding suggestions for better productivity or locating defects to avoid poor quality. Intelligent software engineering is an emerging topic that has attracted great attention in both academia and industry. The first paper, titled 'Syntax-based metamorphic relation prediction via the bagging framework' by Li et al., proposes a multi-dimensional program structure-based metamorphic relation prediction approach, which is composed of feature extraction and prediction model building. [Extracted from the article]

Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]