Mr. Zhuozhu Liu Profile

Zhuozhu Liu

at Wuhan Univ

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Proceedings Article | 11 November 2021 Paper

Informetric analysis of researches on application of artificial intelligence in COVID-19 prevention and control

Zhuozhu Liu, Sijing Chen, Qing Han

Proceedings Volume 12076, 120760H (2021) https://doi.org/10.1117/12.2612150

KEYWORDS: Artificial intelligence, Analytical research, Data analysis, Visualization

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The COVID-19 (2019 novel Coronavirus) is the most widespread pandemic infectious disease encountered in human history. Its economic losses and the number of countries involved rank first in the history of human viruses. Since the outbreak of the COVID-19 pandemic around the world, AI has made a great contribution to the prevention and control of the COVID-19 pandemic. In this paper, researches on the application of artificial intelligence in COVID-19 pandemic prevention and control were analyzed by informetric method. 432 papers indexed in Thomson Reuters’s Web of Science were studied by the perspectives of categories of researches, high frequency keywords, authors, institutions, journals and countries, and we get conclusions as follows: The analysis of keywords cooccurence shows application of machine learning and deep learning in COVID-19 pandemic diagnosis and prediction. The journal that received the most cites was (Radiology) and the journal that published the most papers was (Journal of Medical Internet Research). USA, India and China have the largest number of published articles. USA, China and UK are most influential countries. We also analyzed the review literature on the application of AI in COVID-19 pandemic prevention and control in the Web of Science, and found that these papers specifically can be divided into the following three categories: The first is the application of AI in clinical diagnosis and treatment, the second is the application of AI in the development of anti-epidemic drugs, and the third is the role of AI in the epidemiological research of COVID-19 and the social governance of pandemic prevention and control.

Proceedings Article | 11 November 2021 Open Access

Theoretical research and improvement of knowledge conversion (Withdrawal Notice)

Rongying Zhao, Zhuozhu Liu, Junling Wang, Yuxin Hu, Yiran Li

Proceedings Volume 12076, 1207615 (2021) https://doi.org/10.1117/12.2612191

KEYWORDS: Knowledge management, 3D modeling, Vector spaces, Transform theory

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This paper reconsiders the SECI model proposed by Ikujiro Nonaka, and points out the inconformity between theory and practice in the original model when knowledge conversion is based on the theoretical basis and the actual situation, that is, socialization or combination can not be realized directly, but can only be realized indirectly through the combination of internalization and externalization. On this basis, the model is improved, and the spatial vector model of knowledge conversion is proposed. The SECI model is innovated, which provides theoretical support for the subsequent research on knowledge conversion. Through the establishment of knowledge conversion model in the spatial vector coordinate system, the specific transformation mode between explicit knowledge and tacit knowledge is embodied, and the inner conversion mode of combinatorial and socialized is refined. Through the study of knowledge conversion model, this paper holds that there are two direct ways of knowledge conversion, namely externalization and internalization.

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