A novel UMIDAS-SVQR model with mixed frequency investor sentiment for predicting stock market volatility

: At present, cancer imaging examination relies mainly on manual reading of doctors, which requests a high standard of doctors' professional skills, clinical experience and concentration. However, the increasing amount of medical imaging data has brought more and more challenges to radiologists. The detection of digestive system cancer (DSC) based on artificial intelligence (AI) can provide a solution for automatic analysis of medical images and assist doctors to achieve high-precision intelligent diagnosis of cancers. : The main goal of this paper is to introduce the main research methods of the AI based detection of DSC, and provide relevant learning and reference for relevant researchers. Meantime, it summarizes the main problems existing in these methods, and provides better guidance for future research. : The automatic classification, recognition and segmentation of DSC can be better realized through the detection methods of machine learning and deep learning, which minimize the internal information of images that are difficult for humans to discover. In the diagnosis of DSC for many organs and high incidence, the use of AI to assist imaging surgeons in tumor detection can achieve rapid and effective cancer detection and save doctors' diagnosis time. These can lay the foundation for better clinical diagnosis, treatment planning and accurate quantitative evaluation of DSC.