• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.25-36
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• 2020

Topics calculated using LDA topic modeling have to be labeled separately. When labeling a topic, we look at the words that represent the topic, and label the topic. Therefore, it is important to first make a good set of words that represent the topic. This paper proposes a method of calculating a set of words representing a topic using TextRank, which extracts the keywords of a document. The proposed method uses Relevance to select words related to the topic with discrimination. It extracts topic keywords using the TextRank algorithm and connects keywords with a high frequency of simultaneous occurrence to express the topic with a higher coverage.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.4
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• pp.91-110
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• 2018

We proposed a video summarization algorithm based on an ERP (Event Related Potentials)-based topic relevance model, a MMR (Maximal Marginal Relevance), and discriminant analysis to generate a semantically meaningful video skim. We then conducted implicit and explicit evaluations to evaluate our proposed ERP/MMR-based method. The results showed that in the implicit and explicit evaluations, the average scores of the ERP / MMR methods were statistically higher than the average score of the SBD (Shot Boundary Detection) method used as a competitive baseline, respectively. However, there was no statistically significant difference between the average score of ERP/MMR (${\lambda}=0.6$) method and that of ERP/MMR (${\lambda}=1.0$) method in both assessments.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.739-754
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• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• Human Ecology Research
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• v.60 no.3
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• pp.359-376
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• 2022

Based on an ecological model of teacher agency, the purpose of this study was to examine the relevance of HE (home economics) subjects and CSE (comprehensive sexuality education) for HE teachers, and their implementation in HE classes. To achieve this, a survey was conducted with HE teachers nationwide for which 243 responses were collected, and interviews were conducted with five HE teachers who were actively practicing CSE. The results of the survey and interview were as follows. First, HE teachers strongly recognized the relevance of HE subjects and the topic of CSE with an average score of 4.63 (out of 5 points), and practiced CSE at an average of 72.23% (97.12%~43.21%) in their class. Second, based on the ecological approach model of teacher agency, the factors facilitating the CSE practice of HE teachers included: childbirth and parenting experienced as parents, experiences of students encountering sexual problems in school, the philosophy and content of HE subjects, positive feedback from students and support from fellow teachers, and intention to help students in their lives. Conversely, HE teachers cited a lack of sexual education experience as learners, complaints from parents, weakness of HE teacher networks, lack of specific statements in curriculum and textbooks, insufficient class content and teacher training, and lack of absolute class time. This study is significant in revealing that CSE is highly relevant to the contents of HE subjects and is already being practiced in HE classes.

• The Korean Journal of Archival Studies
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• no.19
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• pp.57-102
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• 2009

Recently, distribution of record via web and coefficient of utilization are increase. so, Archival information service using website becomes essential part of record center. The main point of archival information service by website is making record information retrieval easy. It has need of matching user's request and representation of record resources correctly to making archival information retrieval easy. Archivist and record manager have used various information representation tools from taxonomy to recent thesaurus, still, the accuracy of information retrieval has not solved. This study constructed record retrieval system based on Topic Map by modeling record resources which focusing on description metadata of the records to improve this problem. The target user of the system is general web users and its range is limited to the president related sources in the National Archives Portal Service. The procedure is as follows; 1) Design an ontology model for archival information service based on topic map which focusing on description metadata of the records. 2) Buildpractical record retrieval system with topic map that received information source list, which extracted from the National Archives Portal Service, by editor. 3) Check and assess features of record retrieval system based on topic map through user interface. Through the practice, relevance navigation to other record sources by semantic inference of description metadata is confirmed. And also, records could be built up as knowledge with result of scattered archival sources.

• Korean Journal of Medicinal Crop Science
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• v.25 no.3
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• pp.165-174
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• 2017

Background: Traditional plant drugs, are less toxic and free from side effects compared to general synthetic drugs. They have been used for the treatment of diabetes and associated renal damage. In this study, we evaluated effect of Hachimi-jio-gan against diabetic renal damage in a rat model of type 1 diabetic nephropathy induced by subtotal nephrectomy plus streptozotocin (STZ) injection, and in Otsuka Long-Evans Tokushima Fatty (OLETF) rats and db/db mice as a model of human type 2 diabetes, and its associated complications. To explore the active components of Hachimi-jio-gan, the antidiabetic effect of corni fructus, a consituent of Hachimi-jio-gan, and 7-O-galloyl-${{\small}D}$-sedoheptulose, a phenolic compound isolated from corni fructus, were investigated. Methods and Results: We conducted an extensive literature search, and all required data were collected and systematically organized. The findings were reviewed and categorized based on relevance to the topic. A summary of all the therapeutic effects were reported as figures and tables. Conclusions: Hachimi-jio-gan serves as a potential therapeutic agent to against the development of type 1 and type 2 diabetic nephropathy. From the results of characterization active components of corni fructus, 7-O-galloyl-${\small}D$-sedoheptulose is considered to play an important role in preventing and/or delaying the onset of diabetic renal damage. 7-O-Galloyl-${\small}D$-sedoheptulose is expected to serve as a novel therapeutic agent against the development of diabetic nephropathy.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.65-76
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• 2023

In the field of technology entrepreneurship and startups, the development of Artificial Intelligence(AI) has emerged as a key topic for business model innovation. As a result, venture firms are making various efforts centered on AI to secure competitiveness(Kim & Geum, 2023). The purpose of this study is to analyze the relationship between the development of GenAI technology and the startup ecosystem by analyzing domestic news articles to identify trends in the technology startup field. Using BIG Kinds, this study examined the changes in GenAI-related news articles, major issues, and trends in Korean news articles from 1990 to August 10, 2023, focusing on the emergence of ChatGPT before and after, and visualized the relevance through network analysis and keyword visualization. The results of the study showed that the mention of GenAI gradually increased in the articles from 2017 to 2023. In particular, OpenAI's ChatGPT service based on GPT-3.5 was highlighted as a major issue, indicating the popularization of language model-based GenAI technologies such as OpenAI's DALL-E, Google's MusicLM, and VoyagerX's Vrew. This proves the usefulness of GenAI in various fields, and since the launch of ChatGPT, Korean companies have been actively developing Korean language models. Startups such as Ritten Technologies are also utilizing GenAI to expand their scope in the technology startup field. This study confirms the connection between GenAI technology and startup entrepreneurship activities, which suggests that it can support the construction of innovative business strategies, and is expected to continue to shape the development of GenAI technology and the growth of the startup ecosystem. Further research is needed to explore international trends, the utilization of various analysis methods, and the possibility of applying GenAI in the real world. These efforts are expected to contribute to the development of GenAI technology and the growth of the startup ecosystem.