Goutam Mylavarapu, K. A. Viswanathan, Johnson P. Thomas
{"title":"文本数据的上下文感知自动质量评估","authors":"Goutam Mylavarapu, K. A. Viswanathan, Johnson P. Thomas","doi":"10.1504/ijbidm.2023.10044353","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":35458,"journal":{"name":"International Journal of Business Intelligence and Data Mining","volume":"1 1","pages":"451-469"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Context-aware automated quality assessment of textual data\",\"authors\":\"Goutam Mylavarapu, K. A. Viswanathan, Johnson P. Thomas\",\"doi\":\"10.1504/ijbidm.2023.10044353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":35458,\"journal\":{\"name\":\"International Journal of Business Intelligence and Data Mining\",\"volume\":\"1 1\",\"pages\":\"451-469\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Business Intelligence and Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijbidm.2023.10044353\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Business Intelligence and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijbidm.2023.10044353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Decision Sciences","Score":null,"Total":0}
期刊介绍：
IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks. IJBIDM highlights intelligent techniques used for business modelling, including all areas of data visualisation, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, data mining techniques, tools and applications, neurocomputing, evolutionary computing, fuzzy techniques, expert systems, knowledge filtering, and post-processing. Topics covered include Data extraction/reporting/cleaning/pre-processing OLAP, decision analysis, causal modelling Reasoning under uncertainty, noise in data Business intelligence cycle Model specification/selection/estimation Web technology, mining, agents Fuzzy, neural, evolutionary approaches Genetic algorithms, machine learning, expert/hybrid systems Bayesian inference, bootstrap, randomisation Exploratory/automated data analysis Knowledge-based analysis, statistical pattern recognition Data mining algorithms/processes Classification, projection, regression, optimisation clustering Information extraction/retrieval, human-computer interaction Multivariate data visualisation, tools.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
