{"title":"图DEAF：使用面部融合步态特征预测自闭症谱系障碍的混合深度学习算法的新实现","authors":"A. Saranya, R. Anandan","doi":"10.1007/s10619-021-07361-y","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":"40 1","pages":"753-778"},"PeriodicalIF":1.5000,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10619-021-07361-y","citationCount":"6","resultStr":"{\"title\":\"FIGS-DEAF: an novel implementation of hybrid deep learning algorithm to predict autism spectrum disorders using facial fused gait features\",\"authors\":\"A. Saranya, R. Anandan\",\"doi\":\"10.1007/s10619-021-07361-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":50568,\"journal\":{\"name\":\"Distributed and Parallel Databases\",\"volume\":\"40 1\",\"pages\":\"753-778\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1007/s10619-021-07361-y\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Distributed and Parallel Databases\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10619-021-07361-y\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Distributed and Parallel Databases","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10619-021-07361-y","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
期刊介绍：
Distributed and Parallel Databases publishes papers in all the traditional as well as most emerging areas of database research, including:
Availability and reliability;
Benchmarking and performance evaluation, and tuning;
Big Data Storage and Processing;
Cloud Computing and Database-as-a-Service;
Crowdsourcing;
Data curation, annotation and provenance;
Data integration, metadata Management, and interoperability;
Data models, semantics, query languages;
Data mining and knowledge discovery;
Data privacy, security, trust;
Data provenance, workflows, Scientific Data Management;
Data visualization and interactive data exploration;
Data warehousing, OLAP, Analytics;
Graph data management, RDF, social networks;
Information Extraction and Data Cleaning;
Middleware and Workflow Management;
Modern Hardware and In-Memory Database Systems;
Query Processing and Optimization;
Semantic Web and open data;
Social Networks;
Storage, indexing, and physical database design;
Streams, sensor networks, and complex event processing;
Strings, Texts, and Keyword Search;
Spatial, temporal, and spatio-temporal databases;
Transaction processing;
Uncertain, probabilistic, and approximate databases.
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