• 생태와환경
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• 제50권4호
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• pp.478-482
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• 2017

In order to identify key nations and bird species of conservation concern we described multinational collaborations as defined using network analysis linked by birds that are found in all nations in the network. We used network analysis to assess the patterns in bird occurrence for 10,422 bird inventories from 244 countries and territories. Nations that are important in multinational collaborations for bird conservation were assessed using the centrality measures, closeness and betweenness centrality. Countries important for the multinational collaboration of bird conservation were examined based on their centrality measures, which included closeness and betweenness centralities. Comparatively, the co-occurrence network was divided into four groups that reveal different biogeographical structures. A group with higher closeness centrality included countries in southern Africa and had the potential to affect species in many other countries. Birds in countries in Asia, Australia and the South Pacific that are important to the cohesiveness of the global network had a higher score of betweenness centrality. Countries that had higher numbers of bird species and more extensively distributed bird species had higher centrality scores; in these countries, birds may act as excellent indicators of trends in the co-occurrence bird network. For effective bird conservation in the world, much stronger coordination among countries is required. Bird co-occurrence patterns can provide a suitable and powerful framework for understanding the complexity of co-occurrence patterns and consequences for multinational collaborations on bird conservation.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.103-104
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• 2018

Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.424-425
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• 2018

Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2736-2754
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• 2015

Android dominates the mobile operating system market, which stimulates the rapid spread of mobile malware. It is quite challenging to detect mobile malware. System call sequence analysis is widely used to identify malware. However, the malware detection accuracy of existing approaches is not satisfactory since they do not consider correlation of system calls in the sequence. In this paper, we propose a new scheme called Artificial Neural Networks (ANNs) on Co-occurrence Matrices Droid (ANNCMDroid), using co-occurrence matrices to mine correlation of system calls. Our key observation is that correlation of system calls is significantly different between malware and benign software, which can be accurately expressed by co-occurrence matrices, and ANNs can effectively identify anomaly in the co-occurrence matrices. Thus at first we calculate co-occurrence matrices from the system call sequences and then convert them into vectors. Finally, these vectors are fed into ANN to detect malware. We demonstrate the effectiveness of ANNCMDroid by real experiments. Experimental results show that only 4 applications among 594 evaluated benign applications are falsely detected as malware, and only 18 applications among 614 evaluated malicious applications are not detected. As a result, ANNCMDroid achieved an F-Score of 0.981878, which is much higher than other methods.

• Journal of the Korean Wood Science and Technology
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• 제51권6호
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• pp.458-469
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• 2023

Text mining techniques provide valuable insights into research information across various fields. In this study, text mining was used to identify research trends in wood science from 2012 to 2022, with a focus on representative journals published in Korea and Japan. Abstracts from Journal of the Korean Wood Science and Technology (JKWST, 785 articles) and Journal of Wood Science (JWS, 812 articles) obtained from the SCOPUS database were analyzed in terms of the word frequency (specifically, term frequency-inverse document frequency) and co-occurrence network analysis. Both journals showed a significant occurrence of words related to the physical and mechanical properties of wood. Furthermore, words related to wood species native to each country and their respective timber industries frequently appeared in both journals. CLT was a common keyword in engineering wood materials in Korea and Japan. In addition, the keywords "MDF," "MUF," and "GFRP" were ranked in the top 50 in Korea. Research on wood anatomy was inferred to be more active in Japan than in Korea. Co-occurrence network analysis showed that words related to the physical and structural characteristics of wood were organically related to wood materials.

• Child Health Nursing Research
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• 제27권3호
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• pp.201-210
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• 2021

Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI). Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling. Results: For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life". Conclusion: The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.484-485
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• 2019

Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.565-566
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• 2021

Recently, interest in data analysis has increased as the importance of big data becomes more important. Particularly, as social media data and academic research communities become more active and important, analysis becomes more important. In this study, co-word analysis was conducted through altmetrics articles collected from 2012 to 2017. In this way, the co-occurrence network map is derived from the keyword and the emphasized keyword is extracted.

• Perspectives in Nursing Science
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• 제16권1호
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• pp.12-24
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• 2019

Purpose: This study examined the suitability of using text network analysis (TNA) methodology for topic analysis of academic papers related to nursing. Methods: TNA background theories, software programs, and research processes have been described in this paper. Additionally, the research methodology that applied TNA to the topic analysis of the academic nursing papers was analyzed. Results: As background theories for the study, we explained information theory, word co-occurrence analysis, graph theory, network theory, and social network analysis. The TNA procedure was described as follows: 1) collection of academic articles, 2) text extraction, 3) preprocessing, 4) generation of word co-occurrence matrices, 5) social network analysis, and 6) interpretation and discussion. Conclusion: TNA using author-keywords has several advantages. It can utilize recognized terms such as MeSH headings or terms chosen by professionals, and it saves time and effort. Additionally, the study emphasizes the necessity of developing a sophisticated research design that explores nursing research trends in a multidimensional method by applying TNA methodology.

• 정보관리학회지
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• 제31권4호
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• pp.49-68
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• 2014

본 연구의 목적은 국내 학술논문 데이터베이스에서 검색한 언어 네트워크 분석 관련 53편의 국내 학술논문들을 대상으로 하는 내용분석을 통해, 언어 네트워크 분석 방법의 기초적인 체계를 파악하기 위한 것이다. 내용분석의 범주는 분석대상의 언어 텍스트 유형, 키워드 선정 방법, 동시출현관계의 파악 방법, 네트워크의 구성 방법, 네트워크 분석도구와 분석지표의 유형이다. 분석결과로 나타난 주요 특성은 다음과 같다. 첫째, 학술논문과 인터뷰 자료를 분석대상의 언어 텍스트로 많이 사용하고 있다. 둘째, 키워드는 주로 텍스트의 본문에서 추출한 단어의 출현빈도를 사용하여 선정하고 있다. 셋째, 키워드 간 관계의 파악은 거의 동시출현빈도를 사용하고 있다. 넷째, 언어 네트워크는 단수의 네트워크보다 복수의 네트워크를 구성하고 있다. 다섯째, 네트워크 분석을 위해 NetMiner, UCINET/NetDraw, NodeXL, Pajek 등을 사용하고 있다. 여섯째, 밀도, 중심성, 하위 네트워크 등 다양한 분석지표들을 사용하고 있다. 이러한 특성들은 언어 네트워크 분석 방법의 기초적인 체계를 구성하는 데 활용할 수 있을 것이다.