• International Journal of Advanced Culture Technology
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• 제9권2호
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• pp.47-55
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• 2021

The coronavirus disease 2019 (COVID-19) pandemic continuously influences on the health and well-being of people and communities worldwide. The purpose of this study was to explore the published research articles in the fields of nursing and health during the COVID-19 pandemic. The research design is an exploratory study. Samples are the abstracts of the articles from a literature database, PubMed, that were published from January 1st, 2020 to April 15th, 2021. We used a MeSH term, 'nursing' and "COVID-19" to retrieve articles that are related to COVID-19. Five hundred and fifty-five articles were retrieved. Two hundred and sixteen articles were excluded as they were not met the inclusion criteria. The total number of articles used in this study was three hundred and thirty-nine. The average number of articles published during the data collection period was 21.9. Seven topics were discovered from the abstracts of the articles: COVID-19 management and guidance; Perinatal COVID-19 and breastfeeding; Nurse; Health service and support; Patient care; Research; and Education and experience. This study revealed some interesting topics from the articles related to COVID-19 and provided some sense of research areas that may be interesting and allow us to develop important research questions about nursing science and nursing practice.

• 인간식물환경학회지
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• 제24권4호
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• pp.415-427
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• 2021

Background and objective: This study examined the changes in the cultural trend of use for green infrastructure in Seoul due to COVID-19 pandemic. Methods: The subjects of this study are 8 sites of green infrastructure selected by type: Forested green infrastructure, Watershed green infrastructure, Park green infrastructure, Walkway green infrastructure. The data used for analysis was blog posts for a total of four years from August 1, 2016 to July 31, 2020. The analysis method was conducted keyword frequency analysis, topic modeling, and related keyword analysis. Results: The results of this study are as follows. First, the number of posts on green infrastructure has increased since COVID-19, especially forested green infrastructure and watershed green infrastructure with abundant naturalness and high openness. Second, the cultural trend keywords before and after COVID-19 changed from large-scale to small-scale, community-based to individual-based activities, and nondaily to daily culture. Third, after COVID-19, topics and keywords related to coronavirus showed that the cultural trends were reflected on appreciation, activities, and dailiness based on natural resources. In sum, the interest in green infrastructure in Seoul has increased after COVID-19. Also, the change of green infrastructure represents the increased demand for experience that reflects the need and expectation for nature. Conclusion: The new trend of green Infrastructure in the pandemic era should be considered in the the individual relaxations & activities.

• Steel and Composite Structures
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• 제44권3호
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• pp.389-406
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• 2022

Recycling concrete construction waste is an encouraging step toward green and sustainable building. A lot of research has been done on recycled aggregate concretes (RACs), but not nearly as much has been done on concrete made with recycled aggregate. Recycled aggregate concrete, on the other hand, has been found to have a lower mechanical productivity compared to conventional one. Accurately estimating the mechanical behavior of the concrete samples is a most important scientific topic in civil, structural, and construction engineering. This may prevent the need for excess time and effort and lead to economic considerations because experimental studies are often time-consuming, costly, and troublous. This study presents a comprehensive data-mining-based model for predicting the splitting tensile strength of recycled aggregate concrete modified with glass fiber and silica fume. For this purpose, first, 168 splitting tensile strength tests under different conditions have been performed in the laboratory, then based on the different conditions of each experiment, some variables are considered as input parameters to predict the splitting tensile strength. Then, three hybrid models as GWO-RF, GWO-MLP, and GWO-SVR, were utilized for this purpose. The results showed that all developed GWO-based hybrid predicting models have good agreement with measured experimental results. Significantly, the GWO-RF model has the best accuracy based on the model performance assessment criteria for training and testing data.

• 미래기술융합논문지
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• 제2권3호
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• pp.1-7
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• 2023

전세계적으로 기후 변화 문제가 심화되고 있으며, 화석연료의 사용 증가로 인한 다양한 문제들이 야기되고 있다. 특히 탄소중립 정책이 가속화됨으로 재생에너지에 대한 관심이 증가하고 있는 추세이다. 이에 본 연구에서는 SCOPUS DB를 활용하여 신재생에너지 연구 동향을 파악하였다. 초록 제공이 가능한 1,353개의 데이터를 확보하고 이를 분석할 수 잇도록 데이터 전처리를 수행하고 분석을 수행하였다. 토픽모델링 분석 결과 중요한 키워드로 renewable와 enegy로 나타났다. 이 외에도 electricity, solar, wind, 등이 중요한 키워드로 분석이 되었다. 본 연구 결과를 통해 신재생에너지 관련 기업의 실무자들이 신재생에너지 관련 연구동향을 실무에 활용할 수 있을 것으로 기대한다.

• 4차산업연구
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• 제3권2호
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• pp.11-17
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• 2023

Purpose - Recently, in the field of language education, interest in edutech has increased due to difficulties in classroom teaching due to COVID-19. Accordingly, we would like to analyze research topics related to e-learning before and after COVID-19 and examine the implications for the future Korean language education field. Research design, data, and methodology - This study organized a list of papers to be analyzed by searching for e-learning terms applicable to Korean language education in RISS. The collected data was electronically documented, keywords were extracted using text mining techniques, and word frequencies were checked, and then viewed through cloud visualization. Result - It was confirmed that research on e-learning in the field of Korean language education has increased rapidly in 2021 and 2022. In particular, extensive research on online learning methods has been actively conducted due to the difficulties of face-to-face learning in the COVID-19 era. There have been many studies on teaching and learning methods, such as flipped learning, hybrid learning, blended learning, mobile learning, and smart learning. Conclusion - Since the research so far has mainly focused on online class management methods. Therefore, future research suggests that efforts should be made to develop educational contents and teaching methods using specific ICT technologies. These efforts will contribute to advancing smart education that future education aims for.

• Journal of Korea Trade
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• 제26권8호
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• pp.37-57
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• 2022

Purpose -International diplomacy is key for the cohesive economic growth of countries around the world. This study aims to identify the major topics discussed and make sense of word pairs used in sentences by Chinese senior leaders during their diplomatic visits. It also compares the differences between key topics addressed during diplomatic visits to developed and developing countries. Design/methodology - We employed three methods: word frequency, co-word, and semantic network analysis. Text data are crawling state and official visit news released by the Ministry of Foreign Affairs of the People's Republic of China regarding diplomatic visits undertaken from 2015-2019. Findings - The results show economic and diplomatic relations most prominently during state and official visits. The discussion topics were classified according to nine centrality keywords most central to the structure and had the maximum influence in China. Moreover, the results showed that China's diplomatic issues and strategies differ between developed and developing countries. The topics mentioned in developing countries were more diverse. Originality/value - Our study proposes an effective approach to identify key topics in Chinese diplomatic talks with other countries. Moreover, it shows that discussion topics differ for developed and developing countries. The findings of this research can help researchers conduct empirical studies on diplomacy relationships and extend our method to other countries. Additionally, it can significantly help key policymakers gain insights into negotiations and establish a good diplomatic relationship with China.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.341-343
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• 2022

온실가스 배출 문제가 세계적인 현안으로 부각되면서 수소를 에너지원으로 사용하는 수소경제가 주목받고 있다. 수소연료전지는 수소경제의 구성요소 중 하나로, 수소를 활용해 열과 전기를 생산하며 에너지 변환 효율이 높이는데 장점이 있다. 본 연구는 세계적인 온라인 커뮤니티인 레딧(Reddit)에서 수집한 수소연료전지와 관련된 소셜 데이터를 텍스트마이닝과 감성분석 기법으로 분석하였다. 분석 결과 9,211건의 댓글을 LDA(Latent Dirichlet Allocation)을 이용해 4개의 토픽 그룹으로 분류할 수 있었다. 이 중 수소연료전지와 관련이 높은 그룹을 선정해 STM(Structural Topic Model) 분석으로 10개 토픽을 추출하였고, 기후 환경, 수소 산업, 수소 차와 관련 있는 토픽 3개를 발견할 수 있었다. 이 연구 결과를 통해 수소연료전지의 세계적으로 실제적인 내용을 빠르고 효과적으로 파악하여 수소연료전지에 대한 예측하고, 우리나라의 수소연료전지 관련 국가R&D의 정책적 방향을 제시하고자 한다.

• 스마트미디어저널
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• 제12권10호
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• pp.38-46
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• 2023

Farming activities are transforming from traditional skill-based agriculture into knowledge-based and technology-driven digital agriculture. The use of intelligent information and communication technology introduces the idea of smart farming that enables farmers to collect weather data, monitor crop growth remotely and detect crop diseases easily. The introduction of Plantix, a pest and disease management tool in the form of a mobile application has allowed farmers to identify pests and diseases of the crop using their mobile devices. Hence, this study collected the reviews of Plantix to explore the response of the users on the Google Play Store towards the application through Latent Dirichlet Allocation (LDA) topic modeling. Results indicate four latent topics in the reviews: two positive evaluations (compliments, appreciation) and two suggestions (plant options, recommendations). We found the users suggested the application to additional plant options and additional features that might help the farmers with their difficulties. In addition, the application is expected to benefit the farmer more by having an early alert of diseases to farmers and providing various substitutes and a list of components for the remedial measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.591-609
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• 2024

In this study, preprocessings with all combinations were examined in terms of the effects on decreasing word number, shortening the duration of the process and the classification success in balanced and imbalanced datasets which were unbalanced in different ratios. The decreases in the word number and the processing time provided by preprocessings were interrelated. It was seen that more successful classifications were made with Turkish datasets and English datasets were affected more from the situation of whether the dataset is balanced or not. It was found out that the incorrect classifications, which are in the classes having few documents in highly imbalanced datasets, were made by assigning to the class close to the related class in terms of topic in Turkish datasets and to the class which have many documents in English datasets. In terms of average scores, the highest classification was obtained in Turkish datasets as follows: with not applying lowercase, applying stemming and removing stop words, and in English datasets as follows: with applying lowercase and stemming, removing stop words. Applying stemming was the most important preprocessing method which increases the success in Turkish datasets, whereas removing stop words in English datasets. The maximum scores revealed that feature selection, feature size and classifier are more effective than preprocessing in classification success. It was concluded that preprocessing is necessary for text classification because it shortens the processing time and can achieve high classification success, a preprocessing method does not have the same effect in all languages, and different preprocessing methods are more successful for different languages.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.