• The Korean Journal of Health Service Management
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• v.14 no.1
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• pp.195-209
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• 2020

Objectives: This study aimed to analyze the papers in the Korean Journal of Health Service Management (KJHSM) (2007-2018) in order to identify the research trends and aid the future development of healthcare-related research. Methods: Data collection was conducted from September 1-30, 2019. The KSHSM website and Lorea Citatin Index (KCI) electronic database provided 605 copies of original text. Results: Of these, 538 studies are original articles and 7 studies are review articles; 23.7% of the studies presented conceptual framework, 58.4% implemented convenience extraction, and 64.7% collected data using questionnaires. 29.3% of key words were included in the healthcare service, and 48.5% were excluded from the submission field. Conclusions: For the qualitative improvement and development of the journal, it is necessary to consider the relevance of refinement of the methodological approach, segmentation in the field of submission, and selection of keywords.

• Journal of Ginseng Research
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• v.13 no.1
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• pp.56-59
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• 1989

The changes in free sugar composition were investigated with respect to the kinds of dried ginseng for extraction, the various ethanol concentrations used for ginseng extract manufacture and the conditions of heating temperature and time under which the ginseng extract was stored . The results are as follows: 1) The free sugar content of dried ginseng was 6.02-8.02% and the sucrose and maltose content in the free sugar was 70-80%. 2) The free sugar content was 13.82-26.29% in the Sanggunsam (dried ginseng of whole root) extract and it had a tendency to increase with increase in ethanol concentration. In addition, when a higher ethanol concentration was used, the sucrose content was in- creased but the maltose content was decreased. 3) The glucose, sucrose and maltose content in ginseng extract, decreased, in the order, as heating temperature and time were increased. On the other hand the opposite results were neted for xylose and fructose. Keywords Panax ginseng, ginseng extract, Sanggunsam.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.463-465
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• 2005

문서분류 시 문서의 내용을 표현하기 위한 자질로서 사용되는 단어의 출현빈도정보는 해당 문서의 주제어를 표현하기에 취약한 점을 갖고 있다. 즉, 키워드가 문장에서 어떠한 목적(의미)으로 사용되었는지에 대한 정보를 표현할 수가 없고, 문장 간의 응집도가 강한 문장에서 추출되었는지 아닌지에 대한 정보를 표현할 수가 없다. 따라서, 이 정보로부터 문서분류를 하는 것은 그 정확도에 있어서 한계를 갖게 된다. 본 논문에서는 이러한 문서표현의 문제를 해결하기위해, 키워드를 선택할 때, 자질로서 문장의 역할(주어)정보를 추출하여 가중치 부여방식을 통하여 주어주도정보량을 추출하였다. 또한, 자질로서 문장 내 키워드들의 동시출현빈도 정보를 추출하여 문장 간 키워드들의 연관성정도를 시소러스에 담아내었다. 그리고, 이로부터 응집도 정보를 추출하였다. 이 두 정보의 통합으로부터 문서 주제어를 결정함으로서, 문서분류를 위한 주제어 추출 시 불필요한 키워드의 삽입을 줄이고, 동시 출현하는 키워드들에 대한 선택 기준을 제공하고자 하였다. 실험을 통해 한번 출현한 키워드라도, 문장을 주도하는 주어로서 사용될 경우와 응집도 가중치가 높을 경우에 주제어로서의 선택될 가능성이 향상되고, 문서분류를 위해 좀 더 세분화된 키워드 점수화가 가능함을 확인하였다. 따라서, 선택된 주제어가 문서분류의 정확도에 있어서 향상을 가져올 수 있을 것으로 기대한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.675-676
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• 2010

As the number of documents rapidly increases in the web environment, the efficient document classification approaches have been required to retrieve the desired information from too many documents. In this paper, we propose a corpus construction tool to annotate document classification information such as category, keywords, and usage to each product description document. The proposed tool can help a human annotator to correctly identify this information by providing the verification step to check the input results of other human annotators. Also, the human annotator can construct the corpus anytime anywhere by using the web-based proposed system.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• The Journal of Information Systems
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• v.28 no.3
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• pp.105-121
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• 2019

Purpose One of the reasons for the expansion of information systems in the enterprise is the increased efficiency of data analysis. In particular, the rapidly increasing data types which are complex and unstructured such as video, voice, images, and conversations in and out of social networks. The purpose of this study is the customer needs analysis from customer voices, ie, text data, in the web environment.. Design/methodology/approach As previous study results, the word frequency of the sentence is extracted as a word that interprets the sentence has better affects than frequency analysis. In this study, we applied the TF-IDF method, which extracts important keywords in real sentences, not the TF method, which is a word extraction technique that expresses sentences with simple frequency only, in Korean language research. We visualized the two techniques by cluster analysis and describe the difference. Findings TF technique and TF-IDF technique are applied for Korean natural language processing, the research showed the value from frequency analysis technique to semantic analysis and it is expected to change the technique by Korean language processing researcher.

• Archives of Plastic Surgery
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• v.49 no.4
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• pp.543-548
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• 2022

Microvascular reconstruction frequently requires anastomosis outside of the zone of injury for successful reconstruction. Multiple options exist for pedicle lengthening including vein grafts, arteriovenous loops, and arteriovenous bundle interposition grafts. The authors performed a systematic review of arteriovenous bundle interposition grafts to elucidate indications and outcomes of arteriovenous grafts in microvascular reconstruction. A systematic review of the literature was performed using targeted keywords. Data extraction was performed by two independent authors, and descriptive statistics were used to analyze pooled data. Forty-four patients underwent pedicle lengthening with an arteriovenous graft from the descending branch of the lateral circumflex femoral artery. Most common indications for flap reconstruction were malignancy (n = 12), trauma (n = 7), and diabetic ulceration (n = 4). The most commonly used free flap was the anterolateral thigh flap (n = 18). There were five complications, with one resulting in flap loss. Arteriovenous bundle interposition grafts are a viable option for pedicle lengthening when free flap distant anastomosis is required. The descending branch of the lateral circumflex femoral artery may be used for a variety of defects and can be used in conjunction with fasciocutaneous, osteocutaneous, muscle, and chimeric free flaps.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.2
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• pp.29-42
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• 2024

As a global response to climate and biodiversity challenges, there is an emphasis on the conservation and restoration of ecosystems that can simultaneously reduce carbon emissions and enhance biodiversity. This study comprised a text analysis and keyword extraction of 1,100 research papers addressing nature restoration in Korea, aiming to provide a quantative and systematic evaluation of domestic research trends in this field. To discern the major research topics of these papers, topic modeling was applied and correlations were established through network analysis. Research on nature restoration exhibited a mainly upward trend in 2002-2022 but with a slight recent decline. The most common keywords were "species," "forest," and "water". Research topics were broadly classified into (1) predictions of habitat size and species distribution, (2) the conservation and utilization of natural resources in urban areas, (3) ecosystems and landscape managements in protected areas, (4) the planting and growth of vegetation, and (5) habitat formation methods. The number of studies on nature restoration are increasing across various domains in Korea, with each domain experiencing professional development.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.97-107
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• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.177-195
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• 2017

This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.