基于神经网络方法的全球Covid-19疫苗名称分类

Kristiawan Nugroho
{"title":"基于神经网络方法的全球Covid-19疫苗名称分类","authors":"Kristiawan Nugroho","doi":"10.24167/JBT.V1I1.3219","DOIUrl":null,"url":null,"abstract":"The Covid-19 pandemic has occurred for a year on earth. Various attempts have been made to overcome this pandemic, especially in making various types of vaccines developed around the world. The level of vaccine effectiveness in dealing with Covid-19 is one of the questions that is often asked by the public. This research is an attempt to classify the names of vaccines that have been used in various nations by using one of the robust machine learning methods, namely the Neural Network. The results showed that the Neural Network method provides the best accuracy, which is 99.9% higher than the Random Forest and Support Vector Machine(SVM) methods.","PeriodicalId":319600,"journal":{"name":"Journal of Business and Technology Law","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"World Covid-19 Vaccine Names Classification Using Neural Network Method\",\"authors\":\"Kristiawan Nugroho\",\"doi\":\"10.24167/JBT.V1I1.3219\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Covid-19 pandemic has occurred for a year on earth. Various attempts have been made to overcome this pandemic, especially in making various types of vaccines developed around the world. The level of vaccine effectiveness in dealing with Covid-19 is one of the questions that is often asked by the public. This research is an attempt to classify the names of vaccines that have been used in various nations by using one of the robust machine learning methods, namely the Neural Network. The results showed that the Neural Network method provides the best accuracy, which is 99.9% higher than the Random Forest and Support Vector Machine(SVM) methods.\",\"PeriodicalId\":319600,\"journal\":{\"name\":\"Journal of Business and Technology Law\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Business and Technology Law\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24167/JBT.V1I1.3219\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business and Technology Law","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24167/JBT.V1I1.3219","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

Covid-19大流行已经在地球上发生了一年。为克服这一流行病已作出各种努力,特别是在世界各地研制各种类型的疫苗。应对Covid-19的疫苗有效性水平是公众经常提出的问题之一。这项研究是试图使用强大的机器学习方法之一,即神经网络,对各国使用的疫苗名称进行分类。结果表明,神经网络方法的准确率最高,比随机森林和支持向量机(SVM)方法提高了99.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
World Covid-19 Vaccine Names Classification Using Neural Network Method
The Covid-19 pandemic has occurred for a year on earth. Various attempts have been made to overcome this pandemic, especially in making various types of vaccines developed around the world. The level of vaccine effectiveness in dealing with Covid-19 is one of the questions that is often asked by the public. This research is an attempt to classify the names of vaccines that have been used in various nations by using one of the robust machine learning methods, namely the Neural Network. The results showed that the Neural Network method provides the best accuracy, which is 99.9% higher than the Random Forest and Support Vector Machine(SVM) methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信