• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.49-65
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• 2021

Although the Environmental Impact Assessment (EIA) is continuously being advanced, the number of environmental disputes regarding it is still on the rise. In order to supplement this, it is necessary to analyze the accumulated complaint cases. In this study, through the analysis of complaint cases, it is possible to identify matters that need to be improved in the existing EIA stages as well as various damages and conflicts that were not previously considered or predicted. In the process, we dervied 'complaint-based data demands' that should be additionally examined to improve the EIA. To this end, a total of 348 news articles were collected by searching with combinations of 'environmental impact assessment' and a keyword for each of the six assessment groups. As a result of analysis of collected data, a total of 54 complaint-based data demands were suggested. Among those were 15 items including 'impact of changes in seawater flow on water quality' in the category of water environment; 13 items including 'area of green buffer zone' in atmospheric environment; 10 items including 'impact of soundproof wall on wind corridor' in living environment; 8 items including 'expected number of users' in socioeconomic environment, 4 items including 'feasibility assessment of development site in terms of environmental and ecological aspects' in natural ecological environment; and 4 items including 'prediction of sediment runoff and damaged areas according to the increase in intensity and frequency of torrential rain' in land environment. In future research, more systematic complaint collection and analysis as well as specific provision methods regarding stages, subjects, and forms of use should be sought to apply the derived data demands in the actual EIA process. It is expected that this study can serve to advance the prediction and assessment of EIA in the future and to minimize environmental impact as well as social conflict in advance.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.313-320
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• 2022

Recently, the keyword 'convergence' has emerged in the education field. The voice of demand for the humanities is also increasing. The range of convergence of the humanities is gradually spreading to various fields such as science, technology, engineering, and the arts field. And also, the trend is to nurture the future creative convergence talent with logical, comprehensive, and creative thinking through the fusion of humanities, scientific, and empirical theories. Myths and legends contain the content of humanity's culture creation and deal with matters such as religion, philosophy, art, and science. Therefore, through the consciousness of the ancients who lived in the so-called convergence era when academic differentiation did not occur, it will be possible to reflect on the appearance of sages. In this paper, we propose a method for educating Chinese culture through the analysis of by Wen Yi-Duo, a famous Chinese scholar. He sought to find the origin of Chinese culture through myths and legends and to find national identity by restoring the concept of national culture in the period of origin. The myths and legends of China are closely related to the cultural phenomena of modern China, which will further enhance our understanding of China.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.1
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• pp.145-166
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• 2023

This study aims to analyze the research trends of English-language journals in Korean studies published in Korea. Data were collected from four English journals in Korean studies indexed in A&HCI and SCOPUS. A total of 1,840 were selected, including 768 articles of the Korea Journal, 466 articles of The Review of Korean Studies, 285 articles of the Seoul Journal of Korean Studies, and 321 articles of the Acta Koreana, in connection with content analysis, author analysis, author keyword frequency analysis, and topic modeling. In results, the domain research of Korean studies is Humanities, followed by Social Science, and Arts and Kinesiology. These three sectors have grown significantly in publishing numbers since 2000. The subject period of the study is in the order of the modern period, late Joseon, and Japanese colonial period. Authors from domestic affiliations made up 73.34% of the total, but the proportion of authors belonging to foreign institutions continued to increase. As for author keywords, 'Korea'(41), 'Buddhism'(20), 'Koreanwar'(18), and 'Joseon'(18) were derived as top keywords. In topic modeling, six topics were identified; 'Korean culture, cultural transmission,' 'Korean modern political history,' 'Korean social democratization process,' 'Japanese colonial period,' 'Korean religious philosophy,' and 'Korean ancient history.' Through this study, it was possible to identify the interests in and research areas of the recent international academic community of Korean studies.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.227-253
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• 2023

The study is a preliminary study to conceptualize memes as information resources for literacy education in information environment changed with digital revolution. The study is to explain the context and reality of memes in order to promote the utilization of memes as information resources. The research questions are as follows: First, what topics are 'memes' studied with? Second, what things are captured and studied as 'memes'? The study conducted frequency and co-occurrence network analysis on 145 domestic studies and contents analysis on 73 domestic studies. The results are as follows: First, memes were mainly studied in the fields of 'humanities', 'social sciences', 'interdiciplinary studies', and 'arts and kinesiology'. Studies based on Dawkins' concept of memes (around 2012), studies on introducing the concept of memes to explain the spread of Korean Wave content (around 2015), and independent studies of memes as a major research topic in cultural sociology (around 2019) were performed. Second, memes are linguistic. Language memes (L-memes) are 102 (37%), language-visual memes (LV-memes) are 23 (8%), language-visual-musical memes (LVM-memes) are 21 (8%). Keyword 'language meme' ranked high in frequency, degree centrality and betweenness centrality of co-occurrence network. In other words, memes are expanding as a unique information phenomenon of cultural sociology based on linguistic characteristics. It is necessary to conceptualize meme literacy in terms of information literacy.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.2
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• pp.1-25
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• 2022

Today, most archives provide recording services through social media; however, their effectiveness is very low. This study aimed to analyze the causes of insufficient social media recording service, focusing on Gyeongnam Archives and Seoul Metropolitan Archives, which are permanent records management institutions and local government archives, and design ways to create synergy by mutual growth with classical recording service. Through literature research, the characteristics and mechanisms of each social medium were identified, and the institutions' current status of social media operations and internal documents were reviewed to analyze the common problems. An in-depth analysis was conducted by interviewing the person in charge of recording services at each institution. In addition, a plan that can be applied to archives was proposed by reviewing the cases of social media operations of domestic-related institutions and overseas archives. Based on this, a new recording service process was established, strategic operation plans for each social medium were proposed, and a plan to mutually grow with the existing recording service was designed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.843-856
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• 2023

The global surge in maritime traffic has resulted in an increased number of ship collisions, leading to significant economic, environmental, physical, and human damage. The causes of these maritime accidents are multifaceted, often arising from a combination of crew judgment errors, negligence, complexity of navigation routes, weather conditions, and technical deficiencies in the vessels. Given the intricate nuances and contextual information inherent in each incident, a methodology capable of deeply understanding the semantics and context of sentences is imperative. Accordingly, this study utilized the SentenceBERT model to analyze maritime safety tribunal decisions over the last 20 years in the Busan Sea area, which encapsulated data on ship collision incidents. The analysis revealed important keywords potentially responsible for these incidents. Cluster analysis based on the frequency of specific keyword appearances was conducted and visualized. This information can serve as foundational data for the preemptive identification of accident causes and the development of strategies for collision prevention and response.

• 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.

• Smart Media Journal
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• v.12 no.11
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• pp.185-197
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• 2023

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.

• Journal of Korean Home Economics Education Association
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• v.36 no.2
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• pp.1-13
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• 2024

This study used TEXTOM for a total of 4 years, 2 years before and after, as of January 19, 2020, when the domestic confirmed cases of COVID-19 were officially announced, targeting Naver, Daum, Google, YouTube, and Twitter. By analyzing changes in adolescents' interest and awareness of their dietary habits, we aimed to create an opportunity to develop a dietary education program to provide proper dietary education. The results obtained through this study are as follows. First, the keywords with the highest co-occurrence before COVID-19 were 'nutrition' and 'counseling', and the next keywords were 'nutrition' and 'education'. After COVID-19, the order was 'nutrition', 'education', 'food' and 'safety'. Second, the results of co-occurrence frequency network analysis showed that there was high interest in nutrition and counseling regardless of COVID-19, and that interest in safety and health increased further after COVID-19. Third, through cluster formation through CONCOR analysis, before COVID-19, it was categorized into 'diet and physical activity', 'skin and disease', 'health and food', and 'nutrition and intake', and after COVID-19, it was categorized into 'nutrition, intake and COVID-19', 'diet and physical activity', 'skin and disease', and 'circadian rhythm imbalance and disease'. Fourth, as a result of the diet-related keyword cluster analysis network, before COVID-19, keywords in the 'eating and physical activity' group were strongly connected to keywords in the 'health and food' and 'nutrition and intake' groups, and after COVID-19, 'diet' Keywords in the 'and physical activity' group were strongly connected to keywords in the 'nutrition, intake, and COVID-19' group.