COVID-19: A Comprehensive Review of Learning Models.
COVID-19: A Comprehensive Review of Learning Models.
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DOI：
10.1007/s11831-021-09641-3
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Roy PK
中科院分区：
文献类型：
--
作者：
Chahar S;Roy PK
冠状病毒疾病是传染性的，会抑制感染者的免疫系统。它属于冠状病毒科，迄今已影响213个国家和地区。正在进行多种研究，以过滤建议并提供监督，以监测这一爆发。本文对有关症状的早期识别、流行结束的估计和用户生成对话的检查的研究进行了比较和简要的回顾。胸部X光图像，腹部计算机断层扫描，社交媒体上分享的推文是研究人员使用的几个数据集。使用机器学习和深度学习方法，如K均值聚类，随机森林，卷积神经网络，长短期记忆，自动编码器和回归方法，处理上述数据集。本文概述了使用机器学习和深度学习模型对COVID-19的研究及其结果和局限性。最后讨论了未来研究方向面临的挑战。
Coronavirus disease is communicable and inhibits the infected person’s immune system. It belongs to the Coronaviridae family and has affected 213 nations and territories so far. Many kinds of studies are being carried out to filter advice and provide oversight to monitor this outbreak. A comparative and brief review was carried out in this paper on research concerning the early identification of symptoms, estimation of the end of the pandemic, and examination of user-generated conversations. Chest X-ray images, abdominal computed tomography scan, tweets shared on social media are several of the datasets used by researchers. Using machine learning and deep learning methods such as K-means clustering, Random Forest, Convolutional Neural Network, Long Short-Term Memory, Auto-Encoder, and Regression approaches, the above-mentioned datasets are processed. The studies on COVID-19 with machine learning and deep learning models with their results and limitations are outlined in this article. The challenges with open future research directions are discussed at the end.
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影响因子：
8.5
作者：
Ayyoubzadeh, Seyed Mohammad;Ayyoubzadeh, Seyed Mehdi;Kalhori, Sharareh R. Niakan
通讯作者：
Kalhori, Sharareh R. Niakan
DOI：
10.1148/ryai.2020200048
发表时间：
2020-07-01
期刊：
RADIOLOGY-ARTIFICIAL INTELLIGENCE
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