• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.42-51
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• 2020

This study was performed to support the 6th forest basic planning by Korea Forest Service as predicting the change of forestland area by the transition of land use type in the future over 35 years in South Korea. It is very important to analyze upcoming forestland area change for future forest planning because forestland plays a basic role to predict forest resources change for afforestation, production and management in the future. Therefore, the transitional interaction between land use types in future of South Korea was predicted in this study using econometrical models based on past trend data of land use type and related variables. The econometrical model based on maximum discounted profits theory for land use type determination was used to estimate total quantitative change by forestland, agricultural land and urban area at national scale using explanatory variables such as forestry value added, agricultural income and population during over 46 years. In result, it was analyzed that forestland area would decrease continuously at approximately 29,000 ha by 2027 while urban area increases in South Korea. However, it was predicted that the forestland area would be started to increase gradually at 170,000 ha by 2050 because urban area was reduced according to population decrement from 2032 in South Korea. We could find out that the increment of forestland would be attributed to social problems such as urban hollowing and localities extinction phenomenon by steep decrement of population from 2032. The decrement and increment of forestland by unbalanced population immigration to major cities and migration to localities might cause many social and economic problems against national sustainable development, so that future strategies and policies for forestland should be established considering such future change trends of land use type for balanced development and reasonable forestland use and conservation.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.167-187
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• 2020

Currently, Korea is changing into an ultra-aging society, and office workers retire at the age of 49.5 on average from their main jobs, and the national pension is delayed from 62 years old to 65 years old by 2034, so research is needed to prepare for the aging of office workers after retirement. The purpose of this study is to examine the factors affecting the intention to start a business after retirement and the mediating effect of another sense of success expectation on life after retirement, targeting office workers nationwide. Changes in individual attitudes and systematic institutional support are needed to prepare for a sustainable job until the age of 100 after retirement, that is, a start-up utilizing wisdom and experience in work life. As a result of the study, the ability to perform the goal as job performance, economic preparation for retirement preparation, preparation for external relations, and future anxiety have a positive effect on the entrepreneurial will, and the ability to use new technologies as job performance, and physical preparation for retirement. Preparation and preparation for internal relations were found to have no effect. In the influencing relationship between preparation for external relations and the will of start-up, and future anxiety and will of start-up, another sense of success was confirmed to have a partial mediation effect. In the relationship between economic preparation and willingness to start a business, the effect of complete mediation was confirmed. In order to increase the will to start a business after retirement, it was confirmed that another sense of expectation for success was an important variable. Introducing a government-sponsored education system in the company to reduce the government's financial burden due to super-aging and achieve corporate growth through employee training while potential founders, office workers, are employed, and entrepreneurship and goals for the three life goals of office workers By introducing a performance improvement program, we were able to get implications that would be a solution to the growth of individuals and businesses and reducing the government's financial burden.

• Nutrition Research and Practice
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• v.15 no.2
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• pp.213-224
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• 2021

BACKGROUND/OBJECTIVES: To evaluate the nutritional status and prevalence of malnutrition in hospitalized children at admission and during hospitalization in South Korea. SUBJECTS/METHODS: This first cross-sectional nationwide "Pediatric Nutrition Day (pNday)" survey was conducted among 872 hospitalized children (504 boys, 368 girls; 686 medical, 186 surgical) from 23 hospitals in South Korea. Malnutrition risk was screened using the Pediatric Yorkhill Malnutrition Score (PYMS) and the Screening Tool Risk on Nutritional status and Growth. Nutritional status was assessed by z-scores of weight-for-age for underweight, weight-for-height for wasting, and height-for-age for stunting as well as laboratory tests. RESULTS: At admission, of the 872 hospitalized children, 17.2% were underweight, and the prevalence of wasting and stunting was 20.2% and 17.3%, respectively. During hospitalization till pNday, 10.8% and 19.6% experienced weight loss and decreased oral intake, respectively. During the aforementioned period, fasting was more prevalent in surgical patients (7.5%) than in medical patients (1.6%) (P < 0.001). According to the PYMS, 34.3% and 30% of the children at admission and on pNday, respectively, had a high-risk of malnutrition, requiring consultation with the nutritional support team (NST). However, only 4% were actually referred to the NST during hospitalization. CONCLUSIONS: Malnutrition was prevalent at admission and during hospitalization in pediatric patients, with many children experiencing weight loss and poor oral intake. To improve the nutritional status of hospitalized children, it is important to screen and identify all children at risk of malnutrition and refer malnourished patients to the multidisciplinary NST for proper nutritional interventions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.135-154
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• 2020

This paper analyzes the 5,521 samples of the small and medium-sized businesses(SMBs) obtained from the Korea Credit Guarantee Fund. From January 2014 to September 2019, 85% of the SMBs have 5 or fewer full-time employees. The proportion of SMBs is overwhelmed by the elderly men, and most founders are the CEO. Also, about 87% of the workplace types are rented, while 64% of the CEO's residence types are owner-occupation. 47% of the financial grade score is less than 10 points out of 100 and 80% of SMBs have less than 200 million won of the loan guarantee. In particular, the total guarantee loan amount or the days of net guarantee have significantly positive relations with the working period of the CEO in the same industry, the number of employees, the operation period of SMBs, and the corporate business type. In the case of the financial grading score which has the highest weight in overall credit rating gets higher with the higher number of employees, the longer the operation period, and the corporate business type. However, the quantified non-financial grading score has no significant relationship with other explanatory variables, except for the corporate business type. This implies that a non-financial grade score is measured by other determinants that are not observed by the Korea credit guarantee fund. The pure non-financial grade score has positive relations with the working period of the CEO. Overall, this paper would help Korean SMBs upgrade their credit ratings and expand the money supply when there is no standardized credit rating model or no publicly available evaluation criteria for SMBs. We expect this paper provides important insights for further research and policy-makers for SMBs. In particular, to address the financial needs of thin-filers such as SMBs, technology-based financial services (TechFin) would use alternative data to evaluate the financial capabilities of thin-filers and to develop new financial services.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Korean Journal of Heritage: History & Science
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• v.54 no.1
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• pp.52-69
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• 2021

The transcriptions of Buddhist sutra in the Goryeo Dynasty are more elaborate and splendid than those of any other period and occupy a very important position in Korean bibliography. Among them, the transcriptions made on indigo paper show decorative features that represent the dignity and quality that nobles would have preferred. Particularly, during the Goryeo Dynasty, a large number of transcriptions were made on indigo paper, often in hand-scrolled and folded forms. If flexibility was not guaranteed, the hand-scrolled form caused inconvenience and damage when handling the transcription because of the structural limitations of the material that is rolled up and opened. It was possible to overcome these shortcomings by changing from the hand-scrolled to the folded form to obtain convenience and structural stability. The folded form of the transcription utilizes the same principle as the folding screen, so it is a structure that can be folded and unfolded, and it is made by connecting parts at regularly spaced intervals. No matter how small the transcription is, if it is made of thin paper, it is difficult to handle it and to maintain its shape and structure. For this reason, the folded transcription was usually made of thick paper to support the structure, and the cover was made thicker than the inner part to protect the contents. In other words, the forded form was generally manufactured to suit the characteristics of maintaining strength by making the paper thick. Because a large amount of indigo paper was needed to make this type of transcription, it is assumed that there were craftsmen who were in charge only of dark dyeing the papers. Usually, paper dyeing requires much more dye than silk dyeing, and dyeing dozens of times would be required to obtain the deep indigo color of the base of the transcription of Buddhist sutra in the Goryeo Dynasty. Unfortunately, there is no record of the Goryeo Dynasty's indigo blue paper manufacturing technique, and the craftsmen who made indigo paper no longer remain, so no one knows the exact method of making indigo paper. Recently, Hanji artisans, natural dyers, and conservators attempted to restore the Goryeo Dynasty's indigo paper, but the texture and deep colors found in the relics could not be reproduced. This study introduces the process of restoring indigo paper in the Goryeo Dynasty through collaboration between dyeing artisans, Hanji artisans, and conservators for conservation of the transcription of Buddhist sutra in the late Goryeo dynasty, yielding a suggested method of making indigo paper.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.99-114
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The analysis items of the 3rd question were and the 4th question were the motivation for applying to college, the academic plan and the career plan. The text mining to the 3rd question showed that the frequency of 'friends' was overwhelmingly high, followed by keywords such as 'thought', 'time', 'opinion', 'activity', and 'club'. In the 4th question, keyword frequency such as 'thought', 'agriculture', 'KNCAF', 'farm', 'father' was high. The result of association rules analysis for each question showed that the relationship with the highest support level, which means the frequency and importance of the rule, was the {friend} <=> {thought}, {thought} <=> {KNCAF}. The confidence level of a correlation between keywords was the highest in the rules of {teacher}=>{friend}, {agriculture, KNCAF}=>{thought}. Also the lift level that indicates the closeness of two words was the highest in the rules of {friend} <=> {teacher}, {knowledge} <=> {professional}. These keywords are found to play a very important roles in analyzing betweenness centrality and analyzing degree centrality between keywords. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.27-40
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• 2022

Building self-sustainable rural infrastructure and environment through smart digital agriculture technology innovation is one of the major goals of the Korean agricultural administration as a part of the nation's 4^th industry revolution. To identify areas for improving and effectively investing in the acceleration of rural development, 207 experts in the areas of crop science and smart digital agriculture technology were interviewed for their opinions and suggestions on 22 questions designed to recognize fundamental agricultural issues to be addressed and solutions to advance technology innovation and rural development. Majority of the participants expected smart digital agriculture technologies to resolve major agricultural issues and help build a better rural environment. To overcome technology gaps and resolve issues more effectively, further investment in training new technology experts and building stronger agricultural technology infrastructure is urgent, and persistent and systematic support from agricultural administration appears to be the key for accelerating the process. While the leading global groups of both public and private sectors have advanced their technologies beyond the field application stage, most of the Korean technologies remain at the early pilot stage. Aging population and lack of labor in rural areas, unknown future climate change, and challenges in sustainable rural development are expected to be resolved by smart digital agriculture technologies. Technological innovations by research institutes should be promptly deployed in the crop production field, and farm training systemically organized by local technology centers can accelerate farming revolution. Standardization of equipment and data systems is another key to the success of digitalization of food crop production and food supply chains nationwide.