• Herbal Formula Science
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• v.28 no.1
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• pp.15-27
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• 2020

Objectives : We selected three herb-combined remedies, Bigihwan (BGH), Daechilgitang (DCGT) and Mokwhyangbinranghwan (MHBRH), through Donguibogam text-mining analysis, and evaluated their anti-cancer effects on human hepatocellular carcinoma Hep3B cells. Methods : Cytotoxicity was assessed by an MTT assay. Apoptosis rate, autophagy and ROS level were detected by flow cytometry. The autophagy was also observed by Cyto-ID staining fluorescence microscopy. The expression of autophagy, mitophagy and pexophagy regulatory proteins was detected by Western blot analysis. Results : BGH showed the strongest effect among the three prescriptions in inhibiting Hep3B cell viability, which was associated with the induction of apoptosis and autophagy. Autophagy blockers improved cell viability and reduced apoptosis after BGH and DCGT treatments, indicating that autophagy by these prescriptions enhanced Hep3B cells against their cytotoxicity. However, MHBRH enhanced the reduction of cell viability and apoptosis by autophagy blockers. Induction of autophagy by BGH treatment was associated with mitophagy due to mitochondrial dysfunction than DCGT and MHBRH-treated cells. In addition, induction of apoptosis by BGH treatment was ROS-dependent and showed the possibility of pexophagy involvement. Conclusion : Although further studies need to be conducted to study the efficacy and mechanism of accurate anticancer activity, the present results will serve as important sources of understanding the mechanism of action of herbal remedies prescribed for liver disease as documented in Donguibogam.

• The Research Journal of the Costume Culture
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• v.28 no.3
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• pp.285-298
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• 2020

This study aimed at examining fashion consumers' awareness during the COVID-19 pandemic. Big data analysis methods, such as text mining, social network analysis, and regression analysis, were applied to user posts about fashion on Korean portal websites and social media during COVID-19. R 3.4.4, UCINET 6, and SPSS 25.0 software were used to analyze the data. The results were as follows. In researching the popular fashion-related topics during COVID-19, the prevention of infection and prophylaxis were significant concerns in the early stage (Jan 1 to Jan 31, 2020), and changed to online channels and online fashion platforms. Then, various topics and fashion keywords appeared with COVID-19-related keywords afterwards. Fashion-related subjects concerned prophylaxis, home life, digital and beauty products, online channels, and fashion consumption. In comparing fashion consumers' awareness during COVID-19 with SARS and MERS, "face masks" was the common keyword for all three illnesses; yet, the prevention of infection was a major consumer concern in fashion-related subjects during COVD-19 only. As COVD-19 cases increased, the search volume for face masks, shoes, and home clothes also increased. Consumer awareness about face masks shifted from blocking yellow dust and micro-dust to the sociocultural significance and short supply. Keywords related to performance turned out to be the major awareness as to shoes, and home clothes were repurposed with an expanded range of use.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.83-91
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• 2020

This study aims to identify research topics and examine the trend of Covid19-related papers on DBpia. Applying latent Dirichlet allocation (LDA), we have extracted seven research topics, each of which concerns "International Dynamics", "Technology & Security", "Psychological Impact", "Biomedical-Related", "Economic Impact", "Online Education", and "Religion-Related". In addition, we used the multinomial logistic model to examine the trend of research topics. We found that the papers mainly cover topics related to "International Dynamics" and "Biomedical-Related" before June 2020, but the topics have become diverse since then. In particular, topics regarding "Economic Impact", "Online Education" and "Psychological Impact" has drawn increased attention of researchers. The findings would provide a guideline for collaboration in Covid19-related research, and could serve as a reference work for active research.

• 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.

• Korea Science and Art Forum
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• v.34
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• pp.133-148
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• 2018

Korea's housing structure is predicted that one-person housing will be the most common type of housing in Korea. Therefore, this study intends to derive contents for designing a one-person housing space considering the life of a rapidly increasing one-person householder. For this purpose, this study objectively derives the social, economic and cultural influencing factors of one-person households through big data analysis, and analyzed the correlation between contents using social network analysis methodology. In this paper, 60 core contents related to one person housing space were derived by applying big data analysis methodology. And through social network analysis, the most influential contents were derived from the space editing and space composition categories. This means that the residential space is an important part of the design idea that can flexibly respond to changes in the user's life. Based on this study, future research will focus on the concept and design methodology of one-person housing space.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.197-209
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• 2024

This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.

• Journal of KIISE
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• v.41 no.9
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• pp.652-665
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• 2014

Many important terminologies in biomedical text are expressed as abbreviations or acronyms. We newly suggest a semantic link topic model based on the concepts of topic and dependency link to disambiguate biomedical abbreviations and cluster long form variants of abbreviations which refer to the same senses. This model is a generative model inspired by the latent Dirichlet allocation (LDA) topic model, in which each document is viewed as a mixture of topics, with each topic characterized by a distribution over words. Thus, words of a document are generated from a hidden topic structure of a document and the topic structure is inferred from observable word sequences of document collections. In this study, we allow two distinct word generation to incorporate semantic dependencies between words, particularly between expansions (long forms) of abbreviations and their sentential co-occurring words. Besides topic information, the semantic dependency between words is defined as a link and a new random parameter for the link presence is assigned to each word. As a result, the most probable expansions with respect to abbreviations of a given abstract are decided by word-topic distribution, document-topic distribution, and word-link distribution estimated from document collection though the semantic dependency link topic model. The abstracts retrieved from the MEDLINE Entrez interface by the query relating 22 abbreviations and their 186 expansions were used as a data set. The link topic model correctly predicted expansions of abbreviations with the accuracy of 98.30%.

• Journal of Korean Society of Disaster and Security
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• v.12 no.4
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• pp.15-26
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• 2019

Anjunshinmungo (The safety e-report) has been in operation since 2014, and there are about 1 million cumulative reports by June 2019. This study analyzes the contents of more than 1 million safety newspapers reported at the present time of information age to determine how powerful and meaningful the people's voice and interest are. In particular, we are interested in forecasting ability. We wanted to check whether the report of the safety newspaper was related to possible disasters. To this end, the researchers received data reported in the safety newspaper as text and analyzed it by natural language analysis methodology. Based on this, the newspaper articles during the analysis of the safety newspaper were analyzed, and the correlation between the contents of the newspaper and the newspaper was analyzed. As a result, accidents occurred within a few months as the number of reports related to response and confirmation increased, and analyzing the contents of safety reports previously reported on social instability can be used to predict future disasters.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.321-333
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• 2003

Self-organizing map (SOM), a unsupervised learning neural network, has been developed by T. Kohonen since 1980's. Main application areas were pattern recognition and text retrieval. Because of that, it has not been spread to statisticians until late. Recently, SOM's are frequently drawn in data mining fields. Kohonen's SOM, however, needs improvements to become a statistician's standard tool. First, there should be a good guideline as for the size of map. Second, an enhanced visualization mode is wanted. In this study, principal components self-organizing map (PC-SOM), a modification of Kohonen's SOM, is proposed to meet such needs. PC-SOM performs one-dimensional SOM during the first stage to decompose input units into node weights and residuals. At the second stage, another one-dimensional SOM is applied to the residuals of the first stage. Finally, by putting together two stages, one obtains two-dimensional SOM. Such procedure can be easily expanded to construct three or more dimensional maps. The number of grid lines along the second axis is determined automatically, once that of the first axis is given by the data analyst. Furthermore, PC-SOM provides easily interpretable map axes. Such merits of PC-SOM are demonstrated with well-known Fisher's iris data and a simulated data set.

• Journal of Information Management
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• v.43 no.2
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• pp.43-66
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• 2012

The purpose of this study is to see the intellectual structure in the field of veterinary sciences in Korea, using author profiling analysis(APA), a bibliometric approach. Three journals are selected on the basis of citation data, exchanging most citations with Korean Journal of Veterinary. And then, 50 authors who published most articles at selected journals during the given period of time were chosen. The analysis of similarity and dissimilarity among authors by comparing co-word appearance patterns from article title, abstracts, and keywords was made. Authors can be grouped 11 minor clusters under 4 major clusters, depending on their interests in the area of veterinary sciences in Korea. The subjects for each cluster at the veterinary sciences are decided by the matching the keyword, representing author's research interest. As a result, it is possible to figure out the current research trends and the researcher network in the field of veterinary sciences.