• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.224-226
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• 2021

최근 코로나바이러스감염증-19(COVID-19)가 확산됨에 따라 화상회의, 온라인 게임, 스트리밍 등과 같은 다양한 온라인 서비스들의 트래픽이 크게 증가하면서 원활한 서비스 제공을 위한 서버 자원 관리의 중요성이 강조되고 있다. 이에 따라 서버 자원을 전문적으로 관리해주는 클라우드 서비스의 수요도 증가하는 추세이다. 하지만 대다수의 국내 기업들은 성능의 불확실성, 보안, 정서적 이질감 등을 이유로 클라우드 서비스 도입에 어려움을 겪고 있다. 따라서 본 논문에서는 클라우드 서비스의 성능의 불확실성을 해소하기 위해 클라우드 시장 BIG3 기업(아마존, 마이크로소프트, 구글)의 클라우드 서비스의 성능을 비교하였다.

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.221-232
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• 2021

The purpose of this study is to explore the design direction of artificial intelligence convergence liberal arts subjects based on flip learning and maker education through analysis of learner needs in a non-face-to-face classroom environment caused by COVID-19. To this end, we analyzed the priorities of subject content elements by using the Borich needs assessment and The Locus for Focus model along with students' perceptions of flip learning for students who took and did not take maker education-based liberal arts courses. Based on this, it was used as basic data for designing the curriculum. The study results are as follows. First, the content elements of the artificial intelligence liberal arts curriculum based on maker education consisted of a total of 9 areas and were designed as a class using flip learning. Second, the areas with the highest demand for education are 'Artificial Intelligence Theory', 'Artificial Intelligence Programming Practice', 'Physical Computing Theory', 'Physical Computing Practice', followed by 'Convergence Project', '3D Printing Theory', '3D Printing practice' was decided. Third, most of the questionnaires regarding the application of flip learning in maker education-based artificial intelligence liberal arts subjects showed positive responses regardless of whether they took the course, and the satisfaction of the students was very high. Based on this, an artificial intelligence-based convergence liberal arts curriculum using flip learning and maker education was designed. This is meaningful in that it provides an opportunity to cultivate artificial intelligence literacy for college students by preparing the foundation for artificial intelligence convergence education in liberal arts education by reflecting the needs of students.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.490-501
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• 2018

This study was conducted to develop and test effects of E-book based Flipped Learning education for nursing students. This study was one-group pretest-posttest design. The data were collected from 54 second-year nursing students in the located Y city and August 28 2017 to October 16 2017. The data were analyzed by descriptive statistics, paired t-test, Pearson's correlation coefficient, and stepwise multiple regression with SPSS 20.0 program. The results showed that the program was effective in increasing the critical thinking disposition (t=-8.62, p<.001), academic self-efficacy (t=-9.62, p<.001) and major satisfaction (t=-8.11, p<.001). The result of the stepwise multiple regression indicates the critical thinking disposition predict 13.4% (F=9.22, p<.001) of major satisfaction. The result of the stepwise multiple regression indicates the critical thinking disposition predict 13.4% (F=9.22, p<.001) of major satisfaction. Therefore, strategies for enhancing critical thinking disposition is needed by applying various teaching and learning strategies for nursing students.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.11-21
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• 2020

This study is a phenomenological study that aimed to understand the meaning of nursing students' experience of class using flipped learning method. The participants are 8 senior nursing students. The data collected by individual in-depth interviews and analyzed by Colaizzi's method. As a result of this study, 35 key themes and 11 clusters of themes were derived. The 11 thematic categories are classified in pre-education, in-classroom, and post-education. At the pre-education, the theme clusters are 'lack of information', 'psychological burden', 'different teaching methods', 'improvement of self-directed learning ability', and 'different learner's achievement'. At the in-class, the theme clusters are 'efficient teaching direction' and 'confidence improvement'. At the post-education stage, the theme clusters are 'positive influence on class', 'strengthening self-pay', 'not preferred', and 'lecture preference'. Therefore, a more diversified and in-depth repetitive study is suggested in order to apply the flipped learning method to the nursing major.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.234-234
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• 2023

유해 남조 대발생(Harmful Algal blooms, HABs)이 담수호에 발생하면 마이크로시스틴과 같은 독성물질과 맛·냄새 물질을 생성하여 상수원이용과 친수활동을 방해한다. 그래서 유해 남조 대발생 전 유해남조 세포수를 예측하여 선제적 대응하는 것은 중요하다. 따라서 본 연구는 머신러닝기반 Random Forest(RF)를 활용하여 팔당댐 앞의 유해남조 세포수를 예측하는 모델을 개발하고 성능을 평가하고자 한다. 모델 구축을 위해 2012년 4월부터 2021년 12월까지의 팔당호(삼봉리, 경안천) 및 남북한강(의암댐~이포보)권역의 조류, 수질, 수리/수문, 기상 자료를 수집하여 입력 및 출력 자료로 이용하였다. 수집된 데이터에는 다양한 입력변수들이 있어 남조 세포수 예측 성능 비교를 위한 전체 26개 변수 적용과 통계학적으로 상관관계가 높은 12개 변수 적용을 통해 모델을 구축하였다. 입력, 출력 자료로 이용한 유해남조 세포수는 로그변환된 값으로 사용하였으며 일반적인 조류 시료 채취기간이 7일이므로 7일 후를 예측하기 위한 모델을 구축하였다. 구축한 모델의 성능은 실측데이터와 예측데이터의 R²로 산출하여 평가하였다. 전체 26개 입력변수로 모델 구축 후 학습 및 검증 수행 결과 R²의 학습 0.803, 검증 0.729로 나타났고, 유해남조 세포수와 유의미한 상관관계를 보이는 12개 입력변수로 모델 구축 후 학습 및 검증 수행 R²은 학습 0.784, 검증 0.731로 나타났다. 두 모델의 성능을 살펴본 결과 입력변수 개수의 변화에 따른 성능차이는 크지 않은 것으로 나타났으며, 남조세포수 예측을 위한 모델로서 활용가능함을 알 수 있었다. 향후 연구에서는 Random Forest 외 다른 기계학습 모델들과 딥러닝 모델을 통해 남조세포수 예측 성능이 높은 모델을 구축해볼 필요성이 있다.

• Information Systems Review
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• v.25 no.2
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• pp.57-76
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• 2023

This study classifies the success of creators and YouTubers who have created channels on YouTube recently, which is the most influential digital platform. Based on the actual information disclosure of YouTubers who are in the field of science and technology category, video upload cycle, video length, number of selectable multilingual subtitles, and information from other social network channels that are being operated, the success of YouTubers using machine learning was classified and analyzed, which is the closest to the YouTube revenue structure. Our findings showed that neural network algorithm provided the best performance to predict the success or failure of YouTubers. In addition, our five factors contributed to improve the performance of the classification. This study has implications in suggesting various approaches to new individual entrepreneurs who want to start YouTube, influencers who are currently operating YouTube, and companies who want to utilize these digital platforms. We discuss the future direction of utilizing digital platforms.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.73-83
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• 2023

This study is a descriptive survey to develop a gamification e-learning class based on self-determination theory and to check its effectiveness. The data collection period was from March 1 to June 15, 2023, and 59 students at G University in G Metropolitan City were surveyed on class participation, learning immersion, and teaching presence before and after the course. IBM SPSS/Win 26.0 was used to analyze the collected data, and descriptive statistics, analysis of variance (ANOVA), and analysis of covariance (ANCOVA) were conducted. The results showed that the self-determination-based gamification class significantly improved students' class participation, learning engagement, and teaching presence (p<.05). An analysis of covariance (ANCOVA) was conducted to determine whether the general characteristics of the participants affected the results of the post-test, and gender affected the post-test results of learning engagement, with an effect of 7.9%. Based on the results of this study, it can be seen that self-determination-based gamification e-learning class is effective in improving learners' class participation, learning engagement, and teaching presence. As the demand for e-learning in universities is expanding, self-determination-based gamification e-learning classes should be developed in various fields of liberal arts and majors.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.499-500
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• 2022

차량용 블랙박스의 대중화와 '스마트 국민 제보' 애플리케이션 도입에 따른 영향으로 교통법규 위반 공익신고 건수가 급증하면서 대응해야 할 담당 경찰 인력이 부족한 상황이다. 이러한 인력 부족 문제를 해결하기 위해서 인공지능(AI) 알고리즘을 활용하여 신고된 영상의 위법 여부를 자동으로 분석할 필요가 있다. 본 논문에서는 공익신고의 대부분을 차지하고 있는 진로변경 위반 영상 분석을 위한 객체 인식 방법에 대한 연구 내용을 기술한다. 이 연구에서는 딥러닝 알고리즘과 컴퓨터 비전 알고리즘을 통해 진로변경 위반 분석에 필요한 차량과 실선 객체를 인식하여 진로변경 위반 영상 분석에 활용할 수 있도록 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.814-817
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• 2024

특허의 가치를 평가하기 위해서는 특허 데이터에 포함된 다양한 지표가 활용될 수 있으며, 최근 다양한 지표를 머신 러닝 기법으로 분석하여 특허의 가치를 평가하는 연구가 증가하고 있다. 특허의 가치를 올바르게 평가하기 위해서는 여러 지표 중에서 어떤 지표가 특허의 가치에 크게 기여 하는지 판단할 수 있어야 하며, 이에 따라 지표별로 적절한 가중치를 설정할 수 있어야 한다. 제안된 방법은 회귀 모델 기반으로 다양한 지표에 가중치를 적용하여 특허 피인용수를 예측하였으며, 특허 패밀리 수에 적용되는 가중치를 변경하면서 특허 패밀리 수가 특허의 가치에 미치는 영향을 검증하였고, 특허 가치 평가 과정에서 특허 패밀리 수의 중요성에 대해 확인하였다.