{"title":"人工智能在COVID-19诊断中的未来","authors":"L. Lisienkova, I. P. Mitrofanova","doi":"10.31145/1999-513x-2022-2-88-92","DOIUrl":null,"url":null,"abstract":"The article discusses the possibilities of using arti cial intelligence to detect COVID-19 disease. Based on the research, the best practices and potentially effective approaches to using effective machine and deep learning algorithms are presented. Various groups of initial data have been selected that are suitable for different tasks, on the basis of which the trained model can predict a fairly correct result. The analysis of modern Russian and foreign scienti c articles published over the past year is carried out.","PeriodicalId":196813,"journal":{"name":"Quality. Innovation. Education","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"FUTURE OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF COVID-19\",\"authors\":\"L. Lisienkova, I. P. Mitrofanova\",\"doi\":\"10.31145/1999-513x-2022-2-88-92\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article discusses the possibilities of using arti cial intelligence to detect COVID-19 disease. Based on the research, the best practices and potentially effective approaches to using effective machine and deep learning algorithms are presented. Various groups of initial data have been selected that are suitable for different tasks, on the basis of which the trained model can predict a fairly correct result. The analysis of modern Russian and foreign scienti c articles published over the past year is carried out.\",\"PeriodicalId\":196813,\"journal\":{\"name\":\"Quality. Innovation. Education\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quality. Innovation. Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31145/1999-513x-2022-2-88-92\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality. Innovation. Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31145/1999-513x-2022-2-88-92","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
FUTURE OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF COVID-19
The article discusses the possibilities of using arti cial intelligence to detect COVID-19 disease. Based on the research, the best practices and potentially effective approaches to using effective machine and deep learning algorithms are presented. Various groups of initial data have been selected that are suitable for different tasks, on the basis of which the trained model can predict a fairly correct result. The analysis of modern Russian and foreign scienti c articles published over the past year is carried out.
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