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20th IEEE/ACIS International Summer Semi-Virtual Conference on Computer and Information Science, ICIS 2021 ; 985:111-124, 2021.
Article in English | Scopus | ID: covidwho-1345087


The controversy over fairness and objectivity in the job market, due to hiring irregularities, has led to calls for transparent and fair recruitment procedures. Advances in IT technology have led to the emergence of a non-face-to-face “AI recruitment system” in which artificial intelligence (AI) conducts interviews, instead of human interviews. As the introduction of the non-face-to-face method is encouraged in the hiring process due to the COVID-19 virus pandemics, the number of companies introducing AI recruitment systems is steadily increasing. In this study, the factors affecting the intention of use of AI-based recruitment system were analyzed by utilizing TOE and TAM. As a result, it was shown that the reliability, security, suitability, new technology, partiality, readiness, and legal and policy environment of the TOE affected the intention of using the system. It was also identified to have the moderating effect of the number of employees in the firm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

International Journal of Networked and Distributed Computing ; 9(1):59-74, 2021.
Article in English | Scopus | ID: covidwho-1219466


As COVID-19 enters the pandemic stage, the resulting infections, deaths and economic shocks are emerging. To minimize anxiety and uncertainty about socio-economic damage caused by the COVID-19 pandemic, it is necessary to reasonably predict the economic impact of future disease trends by scientific means. Based on previous cases of epidemic (such as influenza) and economic trends, this study has established an epidemic disease spread model and economic situation prediction model. Based on this model, the author also predict the economic impact of future COVID-19 spread. The results of this study are as follows. First, the deep learning-based economic impact prediction model, which was built based on historical infectious disease data, was verified with verification data to ensure 77% accuracy in predicting inflation rates. Second, based on the economic impact prediction model of the deep learning-based infectious disease, the author presented the COVID-19 trend and future economic impact prediction results for the next 1 year. Currently, most of the published studies on COVID-19 are on the prediction of disease spread by statistical mathematical calculations. This study is expected to be used as an empirical reference to efficient and preemptive decision making by predicting the spread of diseases and economic conditions related to COVID-19 using deep learning technology and historical infectious disease data. © 2021 The Authors. Published by Atlantis Press B.V.

Stud. Comput. Intell. ; 951:229-241, 2021.
Article in English | Scopus | ID: covidwho-1144300


The new virus COVID-19 outbreak in Wuhan, China on Dec. 08, 2019 has had a huge impact on all sectors of society including economy, politics, and science sector, around the world. As a result, the government is continuing its contactless lifestyle by implementing life prevention guidelines which prevent the spread of COVID-19 by minimizing human-to-human contact. Several public user services are also being converted to non-contact way, changing the overall environment of our society. The Library Information System, one of the user services, provides various services through online and offline, including lending and reading books, non-books lending and reading, and operating cultural spaces. However, due to the influence of COVID-19, online user services are required to be expanded, making it important for the Library Information System to improve stability and reliability. This study defined the user service of the Library Information System and examined the variables that are needed for the construction of next generation Library Information System through a group of experts. Based on these selected factors, the research contributes to the construction of the next generation Library Information System with the correlation results of the empirical analysis. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.