Peng Wang, Yang Wu, Wen Wang, Yao Yang, Guanglu Feng
{"title":"Polymorphism prediction method based on big data artificial intelligence algorithm","authors":"Peng Wang, Yang Wu, Wen Wang, Yao Yang, Guanglu Feng","doi":"10.1117/12.2671558","DOIUrl":null,"url":null,"abstract":"In the innovation and development of modern information technology, big data analysis method with artificial intelligence technology as the core has been widely used in all fields. At present, big data analysis has achieved excellent results in practical exploration. It not only completes cluster analysis, association analysis, classification prediction of big data efficiently, but also realizes distributed deep learning in Map Reduce, Spark and other platforms, and uses Map Reduce programming framework to study the application advantages of deep learning models. As an important resource for modern social and economic development, big data information contains not only rich experience and knowledge, but also speeds up social and economic development in a certain sense. Therefore, it is necessary to strengthen the research and innovation of big data analysis methods. On the basis of understanding big data artificial intelligence algorithms, this paper mainly studies polymorphism prediction methods with big data artificial intelligence algorithms as the core, so as to understand the correlation between information in a limited time, mine the hidden content of a large amount of information, and make effective decisions according to actual characteristics.","PeriodicalId":202840,"journal":{"name":"International Conference on Mathematics, Modeling and Computer Science","volume":" 44","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Mathematics, Modeling and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2671558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the innovation and development of modern information technology, big data analysis method with artificial intelligence technology as the core has been widely used in all fields. At present, big data analysis has achieved excellent results in practical exploration. It not only completes cluster analysis, association analysis, classification prediction of big data efficiently, but also realizes distributed deep learning in Map Reduce, Spark and other platforms, and uses Map Reduce programming framework to study the application advantages of deep learning models. As an important resource for modern social and economic development, big data information contains not only rich experience and knowledge, but also speeds up social and economic development in a certain sense. Therefore, it is necessary to strengthen the research and innovation of big data analysis methods. On the basis of understanding big data artificial intelligence algorithms, this paper mainly studies polymorphism prediction methods with big data artificial intelligence algorithms as the core, so as to understand the correlation between information in a limited time, mine the hidden content of a large amount of information, and make effective decisions according to actual characteristics.