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Tinggui Chen,1 Lijuan Peng,1 Xiaohua Yin,1 Bailu Jing,2 Jianjun Yang,3 Guodong Cong,4 Gongfa Li5 1School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, People’s Republic of China; 2School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, People’s Republic of China; 3Department of Computer Science and Information Systems, University of North Georgia, Oakwood, GA 30566, USA; 4School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, People’s Republic of China; 5Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, People’s Republic of ChinaCorrespondence: Guodong Cong Email cgd@mail.zjgsu.edu.cnBackground and Aim: At the end of 2019, the outbreak of COVID-19 had a significant impact on China’s tourism industry, which was almost at a standstill in the short-term. After reaching the preliminarily stable state, the government and the scenic area management department implemented a series of incentive policies in order to speed up the recovery of the tourism industry. Therefore, analyzing all sorts of social effects after policy implementation is of guiding significance for the government and the scenic areas.Methods: Targeted as the social effect with the implementation of tourism promotion policy during the COVID-19 pandemic, this paper briefly analyzes the impact of COVID-19 on the national cultural and tourism industry and selects several representative types of tourism policies, crawls the comment data of Weibo users, analyzes users’ perception and emotional preference to the policy, and thus mines the social effect of various policies. Subsequently, by identifying the social effects of various policies as dependent variables, a binary logistic regression model is constructed to obtain the best combination of tourism promotio

Qianqian Li,1 Tinggui Chen,2 Jianjun Yang,3 Guodong Cong4 1School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, People’s Republic of China; 2School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, People’s Republic of China; 3Department of Computer Science and Information Systems, University of North Georgia, Oakwood, Georgia 30566, USA; 4School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, People’s Republic of ChinaCorrespondence: Tinggui Chen Tel +86-152-681-34776Email ctgsimon@mail.zjgsu.edu.cnBackground and Aim: The spread of the COVID-19 pandemic has led to a number of instances of large-scale panic buying. Taking the COVID-19 pandemic as an example, this paper explores the impact of panic in uncertain environments on panic buying behavior. Under certain circumstances, the spread of rumors about shortage of goods is likely to cause large-scale panic buying. This paper focuses on the study of such panic buying caused by online rumors.Methods: Firstly, based on the improved BA network, this paper constructs a directed network for public opinion communication and integrates an offline communication network to build a two-layer synchronous coupling network based on online and offline communications. Secondly, the individual decision model and the panic emotion transmission model under the uncertain environment are constructed. Netizens judge the authenticity of network information, determine their own panic degree according to the above two models, and judge whether they participate in the panic buying based on the above factors. Finally, the spread of the public opinion of goods buying under the panic state is simulated and analyzed.Results: The experimental results of the two-layer synchronous network that integrates offline interaction are significantly different from the results of pure online interaction, which increases the speed

In this study, an empirical mode decomposition (EMD) based multi-resolution neural network learning paradigm via Hilbert-Huang transform (HHT) is proposed to predict currency crisis for early-warning purpose. In the proposed learning paradigm, the original currency exchange rate series are first decomposed into various independent intrinsic mode components (IMCs) with a multi-resolution Hilbert-EMD algorithm. Then these IMCs with different scales are input into an artificial neural network (ANN) for training purpose. Using the trained ANN, the future currency crisis conditions can be predicted based on the historical data. For verification, two typical currencies — South Korean Won and Thai Baht — are used to test the effectiveness of the proposed multi-resolution neural learning paradigm., The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html, Proceedings of KSS'2007 : The Eighth International Symposium on Knowledge and Systems Sciences : November 5-7, 2007, [Ishikawa High-Tech Conference Center, Nomi, Ishikawa, JAPAN], Organized by: Japan Advanced Institute of Science and Technology, identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/4145