数据分析和信息处理(英文)最新文献_第2页

Backward Support Computation Method for Positive and Negative Frequent Itemset Mining 正负频繁项集挖掘的后向支持计算方法

数据分析和信息处理(英文) Pub Date : 2023-01-01 DOI: 10.4236/jdaip.2023.111003

Mrinmoy Biswas Akash, I. Mandal, Md. Selim Al Mamun

引用次数: 1

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis 基于主成分分析的集成分类器对Elvira生物医学数据集肺癌预测

数据分析和信息处理(英文) Pub Date : 2023-01-01 DOI: 10.4236/jdaip.2023.112010

T. Abuya

引用次数: 0

Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag 用具有时滞的梯度消失弹性优化门控循环单元预测小波变换后的股票价格

数据分析和信息处理(英文) Pub Date : 2023-01-01 DOI: 10.4236/jdaip.2023.111004

Luyandza Sindi Mamba, Antony Ngunyi, L. Nderu

引用次数: 0

Typos Correction in Overseas Chinese Learning Based on Chinese Character Semantic Knowledge Graph 基于汉字语义知识图的海外汉语学习错别字校正

数据分析和信息处理(英文) Pub Date : 2023-01-01 DOI: 10.4236/jdaip.2023.112011

Jing Xiong, Xue Zhai, Zhan Zhang, Feng Gao

{"title":"Typos Correction in Overseas Chinese Learning Based on Chinese Character Semantic Knowledge Graph","authors":"Jing Xiong, Xue Zhai, Zhan Zhang, Feng Gao","doi":"10.4236/jdaip.2023.112011","DOIUrl":"https://doi.org/10.4236/jdaip.2023.112011","url":null,"abstract":"In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70998548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Considerations for a Planned Democratizing Data Framework for Valid and Trusted Data 对有效和可信数据的计划民主化数据框架的考虑

数据分析和信息处理(英文) Pub Date : 2023-01-01 DOI: 10.4236/jdaip.2023.113013

Tambe Mariam Takang, A. Amaechi

引用次数: 0

COVID-19 Virus Prediction Using CNN and Logistic Regression Classification Strategies 基于CNN和逻辑回归分类策略的COVID-19病毒预测

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.101005

A. Srinivasulu, Tarkeshwar Barua, Srinivas Nowduri, M. Subramanyam, Sivaram Rajeyyagari

引用次数: 5

Autonomous Surveillance of Infants’ Needs Using CNN Model for Audio Cry Classification 基于CNN模型的婴儿哭声分类自主监测

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.104013

Geofrey Owino, A. Waititu, A. Wanjoya, John P O Okwiri