• Hwankyungkyoyuk
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• v.21 no.2
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• pp.51-67
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• 2008

The purpose of this study is to develop and applicate the elementary environmental textbook in order to solve its problem and to improve attitude related to the Asian dust. The results in this research are as follows. First, it was showed that three groups who composed of teachers, parents and students in the elementary school had recognized the serosities and problems caused by the Asian dust form TV, and that such problems was associated with increase of the desertification and the global warming. Especially the student group insist that the cause in Asian dust is due to the natural phenomena or industrialization. Second, as a result in analysis on the Asian dust through both textbooks on the 7th elementary curriculum and subsidiary textbooks, contents concerning Asian dusts was little or noting. In addition, in the subjects of Science, Society and Health for the 5th and 6th grade students in the elementary school, they were explained partially as one of the air pollutants. Third, the elementary environmental textbook on the Asian dust was developed for the 5th and 6th grade students. The textbook is composed of four contents on the material which is harmful of the human health and life in Asian dust, the special news of Asian dust, and the best answer to solve Asian dust as well as the cause and the source of Asian dust. Forth, as a result in classes using the environmental textbook developed by four themes about the Asian dust, its application is meaningful in the level of p value in the view of knowledge, awareness and attitude of the experiment group. They was more improved in 37%, 14%, and 15%, respectively, than the comparative group. In conclusion, the environmental textbook related to Asian dust will play an important role in useful tool to understand the right knowledge, awareness, and attitude which makes an effort on its effective management in the elementary school.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.12
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• pp.463-478
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• 2014

Portal site is not only providing search engine and e-mail service but also various services including blog, news, shopping, and others. The fact that average number of daily login for Korean portal site Naver is reaching 300 million suggests that many people are using portal sites. With the increase in number of users followed by the diversity in types of services provided by portal sites, the attack is also increasing. Most of studies of password authentication is focused on threat and countermeasures, however, in this study, we analyse the security threats and security requirement of membership, login, password reset first phase, password reset second phase. Also, we measure security score with common criteria of attack potential. As a result, we compare password authentication system of domestic and abroad portal sites.

• The Journal of Information Systems
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• v.30 no.3
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• pp.21-45
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• 2021

Purpose This paper analyzed the impacts of domestic stock market by a global pandemic such as COVID-19. We investigated how the overall pattern of the stock market changed due to the impact of the COVID-19 pandemic. In particular, we analyzed in depth the pattern of stock price, as well, tried to find what factors affect on stock market index(KOSPI) in the healthcare industry due to the COVID-19 pandemic. Design/methodology/approach We built a data warehouse from the databases in various industrial and economic fields to analyze the changes in the KOSPI due to COVID-19, particularly, the changes in the healthcare industry centered on bio-medicine. We collected daily stock price data of the KOSPI centered on the KOSPI-200 about two years before and one year after the outbreak of COVID-19. In addition, we also collected various news related to COVID-19 from the stock market by applying text mining techniques. We designed four experimental data sets to develop decision tree-based prediction models. Findings All prediction models from the four data sets showed the significant predictive power with explainable decision tree models. In addition, we derived significant 10 to 14 decision rules for each prediction model. The experimental results showed that the decision rules were enough to explain the domestic healthcare stock market patterns for before and after COVID-19.

• Fashion & Textile Research Journal
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• v.21 no.2
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• pp.179-188
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• 2019

Domestic apparel products labeled as 'Made in Korea' in the Chinese market are recognized as a high quality products due to the influence of the Korean Wave (Intergen Consulting Group, 2007). This study analyzes the patterns and order arrangement types of a fashion brand company commissioned to produce apparel in Seoul, Korea in order to rebuild a network of small sewing factories scattered in Korea, reorganize operations, and to find the possibility of regenerating the Korean sewing industry by establishing contact points with domestic sewing factories. We surveyed 100 apparel brand companies in Seoul listed in the 2014/2015 Korea Fashion Brand Annual (Apparel News, 2014) and conducted a questionnaire survey on the company's general management status, type of fabric materials dealt with, and major contact points and methods of production handling. The frequency analysis indicated that the main production material with cloth type was woven fabric with ladies' clothes. The Planning MD team has the highest rate of ordering production with delivery method to the production factory after purchasing fabric and trims. Most respondents answered that they would select a production factory based on recommendations from acquaintances. This was due to a lack of no objective indicator provided by the sewing factory at present and the absence of objectively proceeded communication with brand companies. In this study, we analyze various conditions and measurements for production arrangements from a fashion brand company to revitalize sewing factories in Korea.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.45-50
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• 2019

This research analyzed that what factors affect to the change of social positioning of 'multi-cultural family(MCF)' centered on 'multi-cultural family in Jecheon City using Social Construction. The purpose of this research analyze the social positioning of MCF in Jecheon City, policy design depending on that social positioning and the effect of social perception. Therefore, this research carried out qualitative analysis method that analyzed news articles, legislations and interviews from 1990 to 2013 based on social construction theory. For the purpose, first, the time scope could be divided into four periods such as 'the quickening period in 1990s', 'quantitative growth period from 2000 to 2005', 'qualitative growth period from 2006 to 2011', 'the period of antagonism after 2012' of MCF.

• Database Research
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• v.34 no.3
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• pp.3-13
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• 2018

Text classification is one of the text mining technologies that classifies a given textual document into its appropriate categories and is used in various fields such as spam email detection, news classification, question answering, emotional analysis, and chat bot. In general, the text classification system utilizes machine learning algorithms, and among a number of algorithms, naïve Bayes and support vector machine, which are suitable for text data, are known to have reasonable performance. Recently, with the development of deep learning technology, several researches on applying deep neural networks such as recurrent neural networks (RNN) and convolutional neural networks (CNN) have been introduced to improve the performance of text classification system. However, the current text classification techniques have not yet reached the perfect level of text classification. This paper focuses on the fact that the text data is expressed as a vector only with the word dimensions, which impairs the semantic information inherent in the text, and proposes a neural network architecture based upon the semantic tensor space model.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.494-504
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• 2021

The spread of Over-The-Top (OTT) service, which represents Netflix, and the social distancing caused by COVID-19, acted as an overall bad news for domestic multiplex movie theaters. In addition to this, the phenomenon of digital shifting was added, and the need for domestic offline movie theaters to seek a new market for growth emerged. This study focused on the concept of consumer value perception amid this problem consciousness, and attempted to investigate the relationship between the brand image of multiplex movie theaters and purchase satisfaction according to the type of consumer value perception. After data was sampled through a questionnaire survey to a total of 350 subjects, the results of empirical analysis according to the study model are as follows. Among the types of value perception of offline movie viewers, practicality had the strongest influence on brand image construction, and self-faithfulness had the strongest influence on purchase satisfaction of offline movie watching. In addition, the brand image of offline movie theaters had a positive(+) effect on the purchase satisfaction of moviegoers. Based on this, this study suggested a new survival strategy in the new normal era of offline Multiplex Cinemas.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.35-44
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• 2020

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.1-13
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• 2020

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation's voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as "other". Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.