A Cloud-based Framework for COVID-19 Media Classification, Information Extraction, and Trends Analysis

Q1 Computer Science
H. El-Kassabi, M. Serhani, Khaled Khalil, A. Benharref
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引用次数: 0

Abstract

The coronavirus COVID-19 pandemic has become the center of concern worldwide and hence the focus of media attention. Checking the coronavirus-related news and updates has become a daily routine of everyone. Hence, news processing and analytics become key solutions to harvest the real value of this massive amount of news. This conscious growth of published news about COVID-19 makes it hard for a variety of audiences to navigate through, analyze, and select the most important news (e.g., relevant information about the pandemic, its evolution, the vital precautions, and the necessary interventions). This can be realized using current and emerging technologies including Cloud computing, Artificial Intelligence (AI) and Deep Learning (DL). In this paper, we propose a framework to analyze the massive amount of public Covid-19 media reports over the Cloud. This framework encompasses four modules, including text preprocessing, deep learning, and machine learning-based news information extraction, and recommendation. We conducted experiments to evaluate three modules of our framework and the results we have obtained prove that combining derived information from the news reports provides the policymakers, health authorities, and the public, a complete picture of the way this virus is proliferating. Analyzing this data swiftly is a powerful tool to provide imperative answers to questions that are relevant to public health.
基于云的COVID-19媒体分类、信息提取和趋势分析框架
新冠肺炎疫情已成为全球关注的焦点,成为媒体关注的焦点。查看与冠状病毒相关的新闻和更新已经成为每个人的日常生活。因此,新闻处理和分析成为获取海量新闻真正价值的关键解决方案。关于COVID-19的已发表新闻有意识地增长,使各种受众难以浏览、分析和选择最重要的新闻(例如,有关大流行的相关信息、演变、重要预防措施和必要的干预措施)。这可以通过云计算、人工智能(AI)和深度学习(DL)等当前和新兴技术来实现。在本文中,我们提出了一个框架来分析云上的大量公共Covid-19媒体报道。该框架包含四个模块,包括文本预处理、深度学习和基于机器学习的新闻信息提取和推荐。我们进行了实验来评估我们的框架的三个模块,我们获得的结果证明,结合从新闻报道中获得的信息,可以为政策制定者、卫生当局和公众提供一幅关于这种病毒扩散方式的完整图景。迅速分析这些数据是为与公共卫生有关的问题提供必要答案的有力工具。
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来源期刊
IEEE Cloud Computing
IEEE Cloud Computing Computer Science-Computer Networks and Communications
CiteScore
11.20
自引率
0.00%
发文量
0
期刊介绍: Cessation. IEEE Cloud Computing is committed to the timely publication of peer-reviewed articles that provide innovative research ideas, applications results, and case studies in all areas of cloud computing. Topics relating to novel theory, algorithms, performance analyses and applications of techniques are covered. More specifically: Cloud software, Cloud security, Trade-offs between privacy and utility of cloud, Cloud in the business environment, Cloud economics, Cloud governance, Migrating to the cloud, Cloud standards, Development tools, Backup and recovery, Interoperability, Applications management, Data analytics, Communications protocols, Mobile cloud, Private clouds, Liability issues for data loss on clouds, Data integration, Big data, Cloud education, Cloud skill sets, Cloud energy consumption, The architecture of cloud computing, Applications in commerce, education, and industry, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Business Process as a Service (BPaaS)
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