• Title/Summary/Keyword: keyword network

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Analysis of Students Experience related of Nursing Management Clinical Practice: Text Network Analysis Method (Text Network Analysis를 이용한 간호관리학 실습경험 분석)

  • Kang, Kyeong Hwa;Yu, Soyoung
    • Journal of Korean Academy of Nursing Administration
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    • v.22 no.1
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    • pp.80-90
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    • 2016
  • Purpose: The purpose of this study was to analyze students experiences during clinical practice in nursing management. Methods: Assessing through computerized databases, self-reflection reports of 57 students were analyzed. Text network analysis was applied to examine the research. The keywords from each student's reports were extracted by using the programs, KrKwic and NetMiner. Results: The results of the keyword network analysis of what students learned in the nursing process included 27 words. The keyword network analysis of what students learned from the problem solving process included 23 words and the keyword network analysis of improvements in Clinical Practice of Nursing included 31 words. Conclusion: Studies related to clinical practice have been increasing, and themes of the studies have also become broader. Further research is required to investigate factors affecting clinical practice specifically in nursing management. Further comparative studies are necessary to define differences in clinical practice systems related to improving nursing students competency.

A Study on the Research Trend in the Dyslexia and Learning Disability Trough a Keyword Network Analysis (키워드 네트워크 분석을 통한 난독증과 학습장애 관련 연구 동향 분석)

  • Lee, Woo-Jin;Kim, Tae-Gang
    • Journal of Digital Convergence
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    • v.17 no.1
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    • pp.91-98
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    • 2019
  • The present study was performed to investigate the general research trends of dyslexia and learning disability to explore the centrality of related variables though analysis of keyword networks. Data were collected from ten years articles research information sharing service(RISS) which is provided by korea education and research information service(KERIS). The research subjects selected for the analysis were keyword cleansing work, extraction major keyword using KrKwic program and using NodeXL program to Visualize the center of connection between keyword. The results of this were as follows. First, totally 72 of keyword were extracted from keyword cleansing process and among those keyword. major keywords included learning disability, dyslexia, RTI. Second, analysis of the betweenness centrality of dyslexia and learing disabilities shows that learning disabilities are a key word that has been addressed in the study of dyslexia and learning disabilities in korea. The results of these studies suggest a method of analyzing trends in qualitative and qualitative analysis in relation to dyslexia and learning disorder.

Keyword Selection for Visual Search based on Wikipedia (비주얼 검색을 위한 위키피디아 기반의 질의어 추출)

  • Kim, Jongwoo;Cho, Soosun
    • Journal of Korea Multimedia Society
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    • v.21 no.8
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    • pp.960-968
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    • 2018
  • The mobile visual search service uses a query image to acquire linkage information through pre-constructed DB search. From the standpoint of this purpose, it would be more useful if you could perform a search on a web-based keyword search system instead of a pre-built DB search. In this paper, we propose a representative query extraction algorithm to be used as a keyword on a web-based search system. To do this, we use image classification labels generated by the CNN (Convolutional Neural Network) algorithm based on Deep Learning, which has a remarkable performance in image recognition. In the query extraction algorithm, dictionary meaningful words are extracted using Wikipedia, and hierarchical categories are constructed using WordNet. The performance of the proposed algorithm is evaluated by measuring the system response time.

Reactive Routing Keyword based Routing Procedure in MANET (MANET에서의 Reactive Routing Keyword 기반 라우팅 프로시듀어)

  • Park Soo-Hyun;Shin Soo-Young
    • Journal of the Korea Society for Simulation
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    • v.13 no.4
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    • pp.55-69
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    • 2004
  • In MANET(Mobile Ad-hoc Network), unlike in wired networks, a path configuration should be in advance of data transmission along a routing path. Frequent movement of mobile nodes, however, makes it difficult to maintain the configured path and requires re-configuration of the path very often. It may also leads to serious problems such as deterioration of QoS in mobile ad-hoc networks. In this paper, we proposed a Reactive Routing Keyword (RRK) routing procedure to solve those problems. Firstly, we noticed it is possible in RRK routing to assign multiple routing paths to the destination node. We applied this feature into active networks and SNMP information based routing by storing unique keywords in cache of mobile nodes corresponding to present and candidate routings in a path configuration procedure. It was shown that the deterioration of QoS which may observed in Dynamic Source Routing(DSR) protocol was greatly mitigated by using the proposed routing technique.

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Analyzing Knowledge Structure of Defense Area using Keyword Network Analysis

  • Lee, Yong-Kyu;Yoon, Soung-Woong;Lee, Sang-Hoon
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.10
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    • pp.173-180
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    • 2018
  • In this paper, we analyzed key keywords and research themes in the field of defense research using keyword network analysis and tried to grasp the whole knowledge structure. To do this, we extracted data from 2,165 research data from defense related research institutes from 2010 to 2017 and applied the Pareto rule to the number of abstracts of words and the number of links between words, We extracted a total of 2,303 words based on the criterion and extracted 204 final key words through component analysis. By analyzing the centrality and cohesiveness through these key words, we confirmed the concept of core research in the defense field and derived a total of 7 large groups and 16 small groups of each group in the knowledge structure of the defense area.

Analysis of Laughter Therapy Trend Using Text Network Analysis and Topic Modeling

  • LEE, Do-Young
    • Journal of Wellbeing Management and Applied Psychology
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    • v.5 no.4
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    • pp.33-37
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    • 2022
  • Purpose: This study aims to understand the trend and central concept of domestic researches on laughter therapy. For the analysis, this study used total 72 theses verified by inputting the keyword 'laughter therapy' from 2007 to 2021. Research design, data and methodology: This study performed the development and analysis of keyword co-occurrence network, analyzed the types of researches through topic modeling, and verified the visualized word cloud and sociogram. The keyword data that was cleaned through preprocessing, was analyzed in the method of centrality analysis and topic modeling through the 1-mode matrix conversion process by using the NetMiner (version 4.4) Program. Results: The keywords that most appeared for last 14 years were laughter therapy, depression, the elderly, and stress. The five topics analyzed in thesis data from 2007 to 2021 were therapy, cognitive behavior, quality of life, stress, and the elderly. Conclusions: This study understood the flow and trend of research topics of domestic laughter therapy for last 14 years, and there should be continuous researches on laughter therapy, which reflects the flow of time in the future.

Research Trends on Defects of Apartment Building by Keyword Network Analysis (키워드 네트워크 분석을 이용한 공동주택 하자 연구 동향 분석)

  • Jang, Ho-myun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.9
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    • pp.403-410
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    • 2017
  • Apartment housing has rapidly increased since the housing supply policy implemented in the late 1980s. However, various defects have occurred because the policy focused only on quantity supply, while neglected quality control. In addition, disputes related to various defects are increasing. ; accordingly, studies defects of apartment houses have been continuously conducted to solve various problems. In this study, I analyzed the research trends regarding long-term accumulated defects of apartment buildings by keyword network analysis, and suggest implications. As ananalysis method, I collected journal articles using the portal of the Korea Educational and Scientific Information Agency and constructed data analysis by filtering collected academic papers and keyword refinement. Ialso performed visualization modeling for keyword network relationships, connection degree centrality analysis, and mediation centrality analysis. The results revealed that Mortgage, Dispute, Repair, Case, Response, Condensation, Cost, Institution, Standard, and Valuation are the main keywords that characterize apartment housing defects.

Analysis of Structural Characteristics of the Discipline of Public Administration in Korea from the Viewpoint of Research Ecosystem: Focused on Co-author, Citation, and Keyword Network (연구 생태계 관점에서 본 국내 행정학 분야의 구조적 특성 분석 - 공저자, 인용, 키워드 네트워크 중심으로 -)

  • Park, Cho-Hee;Lee, Sung-Sook
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.31 no.1
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    • pp.213-235
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    • 2020
  • This study examined the process of production, utilization and extinction of researches through academic activities to identify the structural characteristics of the field of discipline of administration in Korea from the viewpoint of research ecosystem. To this end, statistical and network analyses were conducted, focusing on bibliographies, references, and keyword for papers published in 29 domestic journals in the field of public administration for the past five years. The results of the analysis, researchers in the field of public administration in Korea maintain a rather horizontal connection and are connected organically rather than separately. In addition, the core academic journals and keyword were extracted to present the connection, and the speed of knowledge transfer and deterioration was measured to identify the phenomenon of decreasing value in literature.

A Study on the User Experience at Unmanned Cafe Using Big Data Analsis: Focus on text mining and semantic network analysis (빅데이터를 활용한 무인카페 소비자 인식에 관한 연구: 텍스트 마이닝과 의미연결망 분석을 중심으로)

  • Seung-Yeop Lee;Byeong-Hyeon Park;Jang-Hyeon Nam
    • Asia-Pacific Journal of Business
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    • v.14 no.3
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    • pp.241-250
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    • 2023
  • Purpose - The purpose of this study was to investigate the perception of 'unmanned cafes' on the network through big data analysis, and to identify the latest trends in rapidly changing consumer perception. Based on this, I would like to suggest that it can be used as basic data for the revitalization of unmanned cafes and differentiated marketing strategies. Design/methodology/approach - This study collected documents containing unmanned cafe keywords for about three years, and the data collected using text mining techniques were analyzed using methods such as keyword frequency analysis, centrality analysis, and keyword network analysis. Findings - First, the top 10 words with a high frequency of appearance were identified in the order of unmanned cafes, unmanned cafes, start-up, operation, coffee, time, coffee machine, franchise, and robot cafes. Second, visualization of the semantic network confirmed that the key keyword "unmanned cafe" was at the center of the keyword cluster. Research implications or Originality - Using big data to collect and analyze keywords with high web visibility, we tried to identify new issues or trends in unmanned cafe recognition, which consists of keywords related to start-ups, mainly deals with topics related to start-ups when unmanned cafes are mentioned on the network.

A Study on Embedded DSP Implementation of Keyword-Spotting System using Call-Command (호출 명령어 방식 핵심어 검출 시스템의 임베디드 DSP 구현에 관한 연구)

  • Song, Ki-Chang;Kang, Chul-Ho
    • Journal of Korea Multimedia Society
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    • v.13 no.9
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    • pp.1322-1328
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    • 2010
  • Recently, keyword spotting system is greatly in the limelight as UI(User Interface) technology of ubiquitous home network system. Keyword spotting system is vulnerable to non-stationary noises such as TV, radio, dialogue. Especially, speech recognition rate goes down drastically under the embedded DSP(Digital Signal Processor) environments because it is relatively low in the computational capability to process input speech in real-time. In this paper, we propose a new keyword spotting system using the call-command method, which is consisted of small number of recognition networks. We select the call-command such as 'narae', 'home manager' and compose the small network as a token which is consisted of silence with the noise and call commands to carry the real-time recognition continuously for input speeches.