• Journal of Korean Library and Information Science Society
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• v.36 no.1
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• pp.47-69
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• 2005

The aim of this paper is to evaluate the text of six alternative versions of Korean fairy tale The Sun and the Moon. So the author took a textual bibliographical study of those versions in a comparative analysis of titles, backgrounds, characters. styles and functions of them. On the basis of this comparative analysis, the version of Hyo-Sun Eu was selected as a good text. In the version of Hyo-Sun Eu. if the helper In the story, King of Heaven represented in the form of Taoism would change into Heaven represented in the form of God, and the literary style change into the colloquial style, and the last scene that the Sun Brother turns to the Sun Sister and the Moon Sister to the Moon Brother would be removed, this version will be the best one.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.30 no.1
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• pp.17-28
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• 2017

Objectives : The aim of this study is reviewing the literature to extracting the key parameter and finding the calibration parameter for the clinical study with economical assessment protocol on atopic dermatitis. Methods : Literature search is performed using PUBMED for literature published from Janurary 2000 to December 2016. We included randomized controlled trials(RCTs) with economic assessment in which human participants. Results : Among the articles published from January 2000 to December 2016, The 1464 articles were found. After reviewing the title, abstract and full text, the five articles were selected. Selected articles are classified 3 CEA(cost effective analysis)study, 1 CMA(cost minimizing analysis)study and 1 cost analysis study. Conclusions : We found highly reliable key parameters and calibration parameters, which might be necessary factors for developing research protocol of economic evaluation alongside clinical trial about atopic dermatitis patients.

• Journal of Korean Elementary Science Education
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• v.36 no.2
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• pp.163-179
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• 2017

This study is to examine whether elementary science textbooks in Korea and Singapore are applicable to flipped learning. By comparative study we sought to identifying appropriate features of science textbooks for learner-centered teaching. We analyzed text pages on the unit of 'Working of electricity' in Korean elementary science textbook for sixth grade and three chapters of 'Electric circuits, Using electricity, Conductors of electricity' in Singaporean elementary textbook, 'Science : My pals are here!'. We designed evaluating frameworks for science textbooks based on the four pillars of flipped learning. and applied it to 10 elementary teachers evaluate two textbooks. They evaluated textbooks with Likert Scale items and wrote detailed statements and exemplars about their choices. We analyzed the teachers' evaluative descriptions inductively and chose commonly mentioned characteristics. Based on the analysis, we got to the conclusion about specific features of two elementary science textbooks in terms of flexible environment, learning culture, intentional contents, and teachers' expertises. Implications for improving science textbooks towards flipped learning and learner-centered teaching through comparative study were discussed.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.174-180
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• 2024

In this paper, we explore an emotion classification method through multimodal learning utilizing wav2vec 2.0 and KcELECTRA models. It is known that multimodal learning, which leverages both speech and text data, can significantly enhance emotion classification performance compared to methods that solely rely on speech data. Our study conducts a comparative analysis of BERT and its derivative models, known for their superior performance in the field of natural language processing, to select the optimal model for effective feature extraction from text data for use as the text processing model. The results confirm that the KcELECTRA model exhibits outstanding performance in emotion classification tasks. Furthermore, experiments using datasets made available by AI-Hub demonstrate that the inclusion of text data enables achieving superior performance with less data than when using speech data alone. The experiments show that the use of the KcELECTRA model achieved the highest accuracy of 96.57%. This indicates that multimodal learning can offer meaningful performance improvements in complex natural language processing tasks such as emotion classification.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1065-1073
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• 2016

In this paper, we examined informatics textbooks for middle school students in accordance with 2009 revised curriculum through conducting both content analysis and surveys. The content analysis was analyzed the composition and contents of textbooks. Survey questionnaires were based on the authorization criteria and selection standards of informatics textbooks. The content analysis demonstrated that there were discrepancies among textbooks in areas such as the total number of pages, related material, the number of core concepts introduced in each chapter, and the application of software. The survey results showed that two sections - 'Expression and Modification' and 'Text' exhibited the most positive results, and the 'Creativity' section the least. Using the findings above, we present alternative ways to improve qualities of these textbooks.

• Journal of The Korean Association For Science Education
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• v.12 no.2
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• pp.97-107
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• 1992

ln this study, we analyzed 5 science textbooks currently used for the 7th graders quantitatively by using the science textbook rating system of Collette and Chiappetta(1986), making meta-analysis of the results of 17 graduate school students of Seoul National University. The rating system consists of 11 categories with detailed items respectively : content, organization, reading level, instruction approach, illustrations, end-chapter teaching aids, laboratory activities in text and/or accompanying manual, teacher aids, indices and glossaries and mechanical makeup of text. Each item in the checklist is to be given between one and five points and the total number of possible points in this rating system is 290. It was shown that 5 science textbooks currently used for 7th-year-students were all "poor" in terms of total points and had, at large, uniformed results especially in 10 items; 7 items concerning moral and ethical implications of science, vocabulary lists, accompanying laboratory manual, annotated editions for test, supply list for laboratory program, student workbook and glossary with low points, while 3 items concerning facilities needed for laboratory activities, activities relevant to the content and textbook size with high points. A Science teachers could get a broad view with a correct impression of the books usefulness in making an evaluation of available textbooks.

• Human Ecology Research
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• v.60 no.1
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• pp.21-38
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• 2022

This study uses text mining compares and contrasts consumers' social media discourses on dietary related zero-waste movement before and after COVID-19. The results indicate that the amount of buzz on social networks for the zero- waste movement has been increasing after COVID-19. Additionally, the results of frequency analysis and topic modeling revealed that subjects associated with zero-waste movement were more diversified after COVID-19. Although the results of a sentiment analysis and word cloud visualization confirmed that consumers' positive responses toward the zero-waste have been increasing, they also revealed a need to educate and encourage those who are still not aware of the need for zero-waste. Finally, consumers mentioned only a small number of companies participating in zero-waste movement on SNS, indicating that the level of active involvement by such companies is much lower than that of consumers. Theoretical and educational implications as well as those for government policy-making are considered.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Asia pacific journal of information systems
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• v.29 no.1
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• pp.35-49
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• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.