• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.162-171
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• 2020

The purpose of this study is to examine the social perception of disaster safety education for young children based on Textom big data and to explore the direction of young children's disaster safety education. Researchers collected and analyzed online text data using the keywords 'young children+disaster+safety education' from portal websites from 2014 to 2017. The raw data were then subjected to first and second data refinement process. Based on the frequency analysis results, 50 keywords were selected, and the selected keywords were converted into matrix data for network analysis. The results of the study are: first, the most frequently appeared keyword together with young children's disaster safety education was 'education', followed by 'experience', 'kindergarten', 'prevention', and 'school.' Second, keywords with high centrality in the analysis of centrality also were 'education', 'experience', and 'prevention'. In addition, keywords like 'prevention', 'life', and 'evacuation' appear higher in connection-centricity than frequency ranking, which means that the degree of connection between the words is high. These results suggest that young children need education in during early childhood in order to improve their disaster safety skills, and disaster safety education should be accomplished through 'prevention' and 'experience' in early childhood education institutions.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.125-135
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• 2022

This study aims to explore variables using machine learning and provide analysis techniques suitable for predicting pharmacy sales whether government statistical indicators built to create an industrial ecosystem based on data, network, and artificial intelligence affect pharmacy sales. Therefore, this study explored predictive variables and performance through machine learning techniques such as Random Forest, XGBoost, LightGBM, and CatBoost using analysis data from January 2016 to December 2021 for 28 government statistical indicators and pharmacies in the retail sector. As a result of the analysis, economic sentiment index, economic accompanying index circulation change, and consumer sentiment index, which are economic indicators, were found to be important variables affecting pharmacy sales. As a result of examining the indicators MAE, MSE, and RMSE for regression performance, random forests showed the best performance than XGBoost, LightGBM, and CatBoost. Therefore, this study presented variables and optimal machine learning techniques that affect pharmacy sales based on machine learning results, and proposed several implications and follow-up studies.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.755-758
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• 2001

본 논문에서는 서로 다른 크기의 클러스터에 대해서 효과적으로 데이터를 분류할 수 있는 내부클러스터를 이용한 개선된 FCM 알고리즘을 제안하였다. 내부클러스터는 평균내부거리 안쪽에 속하는 데이터 집합으로 클러스터의 크기와 밀도에 비례한다. 그러므로 이를 이용한 개선된 FCM 알고리즘은 기존의 FCM 알고리즘이 클러스터 크기가 다를 경우 퍼지분할과 중심탐색을 제대로 하지 못하는 문제점을 개선한 수 있다. 실험을 통하여 개선된 FCM 알고리즘이 분류 엔트로피에 의해 기존의 FCM 알고리즘 보다 더 좋은 결과를 나타냄을 알 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.171-174
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• 2019

본 논문에서는 사회연결망 분석을 이용하여 군 장비정비정보체계의 정비데이터를 분석하고 이를 통해 정비 분야 전문가를 파악하고자 하였다. 장비정비정보체계는 군에서 장비를 효율적으로 정비하고 관리하기 위해 2009년부터 운용하고 있는 체계로 해군한 정비부대에 대한 2017년 정비데이터(00,000건)의 일부(0,000건)를 페이지랭크 중심성 분석을 통해 정비 분업화 수준과 참여도를 확인함으로써 전문분야를 확인하였다.

• Journal of Information Technology Services
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• v.21 no.1
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• pp.145-161
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• 2022

The data is a key catalyst for the development of the fourth industry, and has been viewed as an essential element of the new industry, with technology convergence such as artificial intelligence, augmented/virtual reality, self-driving and 5 G. This will determine the price and value of the data as the user uses data in which the data is based on the context of the situation, rather than the data itself of the past supplier-centric data. This study began with, what factors will increase the value of data from a user perspective not a supplier perspective The study was limited to public data and users conducted research on users using data, such as analysis or development based on data. The study was designed to gauge the value of data that was not studied in the user's perspective, and was instrumental in raising the value of data in the jurisdiction of supplying and managing data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.93-96
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• 2021

본 논문에서는 공공데이터를 이용한 개인 맞춤형 부동산 추천 방식을 제안한다. 이 추천 서비스는 기존의 가격 중심의 부동산 추천 방식이 아닌 개인이 원하는 요소 통해 부동산을 추천함으로써 사용자의 만족도를 높인다. 이 모델은 사용자가 실거주를 목적으로 하는 부동산 매물을 탐색하고자 할 때 거래 유형, 매물 유형, 가격 정보 뿐만 아니라 사용자가 자신의 주거지 근처에 형성되어 있길 원하는 편의 시설이나 기반시설, 치안 등의 환경 요소를 선택할 수 있도록 하고 선택된 요소들을 통합적으로 분석하여 주거지를 추천한다. 본 논문에서는 직접 구현한 서비스를 통해서 제안하는 새로운 맞춤형 부동산 추천 모델이 기존의 가격 중심의 부동산 추천 서비스보다 편의성 면에서 우수함을 보인다.

• The Journal of Bigdata
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• v.3 no.1
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• pp.21-32
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• 2018

Currently, Webtoon Industry is promising as high potential market from it's high growth trend. The best advantage webtoon propose is that webtoon can provide appropriate service to customers with various needs. For this feature, webtoon industry is expanding throughout the world. This situation may give a great chance for authors and webtoon service corporation to export webtoon contents. Also, this situation could be an opportunity for webtoon to become a new "Korean Wave" contents. For successful advance to market, a close analysis for customers of exporting countries. In this research, we collected the data from Naver Webtoon and analyzed the features of webtoons and webtoon subscribers according to countries. With this research, it would be possible to find out specific methods and variables which affect the preference of webtoon subscribers.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.537-547
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• 2017

현재 우리나라의 GDP 대비 R&D 투자 규모는 세계최고의 수준에 이르렀다. 이러한 연구개발 예산의 양적인 확대 및 성장과 함께 상대적으로 연구개발 예산의 효율적 활용이 중요한 과학기술정책 이슈로 부각되고 있다. 본 연구는 정부 R&D사업 유사영역의 효율성 제고를 위한 정책, 전략의 수립 및 실행의 의사결정을 돕는 데이터 기반의 객관적인 지표들을 제시하였다. 그 후 본 연구에서 제시한 효율성 지표들을 NTIS에서 추출한 2015년 정부출연연구기관 R&D 사업 데이터와 연계하여 실질적으로 측정과 사용이 가능한 정량적 지표들만을 따로 선별하였다. 또한 정부 R&D사업 효율성 지표들의 가중치를 측정하기 위하여 계층분석기법(analytic hierarchy process)을 수행하였으며 계층분석기법의 결과로 나온 가중치를 효율성 지표들에 적용하여 과제 우선순위를 도출하였다. 이를 통해 정책의 수립, 실행 및 조정 시 고려해야 할 지표의 우선순위를 설정하여 유사연구영역 관련 정부 R&D 정책수립에서 실행까지의 연계를 강화시키고 국가적으로 한정된 자원의 효율적 사용을 위한 방안을 제시하였다.

• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.53-64
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• 2020

As online learning forms centered on video lectures become more common and constantly increasing, the video-based learning environment applying various educational methods is also changing and developing to enhance learning effectiveness. Learner's log data has emerged for measuring the effectiveness of education in the online learning environment, and various analysis methods of log data are important for learner's customized learning prescriptions. To this end, the study analyzed learner behavior data and predictions of achievement by machine learning in video-based learning environments. As a result, interactive behaviors such as video navigation and comment writing, and learner-led learning behaviors predicted achievement in common in each model. Based on the results, the study provided implications for the design of the video learning environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.411-425
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• 2023

Today, AI (Artificial Intelligence) technology is widely used in various fields, performing classification and regression tasks according to the purpose of use, and research is also actively progressing. Especially in the field of security, unexpected threats need to be detected, and unsupervised learning-based anomaly detection techniques that can detect threats without adding known threat information to the model training process are promising methods. However, most of the preceding studies that provide interpretability for AI judgments are designed for supervised learning, so it is difficult to apply them to unsupervised learning models with fundamentally different learning methods. In addition, previously researched vision-centered AI mechanism interpretation studies are not suitable for application to the security field that is not expressed in images. Therefore, In this paper, we use a technique that provides interpretability for detected anomalies by searching for and comparing optimization references, which are the source of intrusion attacks. In this paper, based on reference, we propose additional logic to search for data closest to real data. Based on real data, it aims to provide a more intuitive interpretation of anomalies and to promote effective use of an anomaly detection model in the security field.