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머신러닝 및 딥러닝 연구동향 분석: 토픽모델링을 중심으로

Research Trends Analysis of Machine Learning and Deep Learning: Focused on the Topic Modeling

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초록

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', ‘deep learning’, and ‘artificial neural network’ in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

Abstract

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', ‘deep learning’, and ‘artificial neural network’ in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

참고문헌 (20)

  1. "itechkorea" (2018)[URL]
  2. Silver, D., "Mastering the game of Go without human knowledge", Nature, 550, pp.354~359 (2017)
  3. 조성준, "머신러닝(인공지능)의 산업 응용", IE매거진, 23(2), pp.34~38 (2016)
  4. Samuel, A. L., "Some studies in machine learning using the game of checkers", IBM Journal of research and development, 44, pp.206~226 (1959)
  5. Mitchell, T. M., "Evaluating hypotheses", Machine Learning, pp.128~153 (1997)
  6. 최영상, "Deep learning 및 지능 기술의 현황과 미래", IE 매거진, 22(2), pp.31~35 (2015)
  7. "Wikipeida" (2019)[URL]
  8. 곽기영, "SPSS를 이용한 통계데이터분석", pp.1~767 (2019), 청람
  9. 김남규, "텍스트 분석 기술 및 활용 동향", 한국통신학회논문지, 42(2), pp.471~492 (2017), 한국통신학회[DOI]
  10. 박자현, "토픽모델링을 활용한 국내 문헌정보학 연구동향 분석", 정보관리학회지, 30(1), pp.7~32 (2013), 한국정보관리학회[DOI]
  11. 임명수, "기간별 이슈 매핑을 통한 이슈 생명주기 분석 방법론", 지능정보연구, 20(4), pp.25~41 (2014), 한국지능정보시스템학회
  12. 류신, "거시적 이슈 트래킹의 한계 극복을 위한 개인 관심 트래킹 방법론", 한국IT서비스학회지, 13(4), pp.275~287 (2014), 한국IT서비스학회[DOI]
  13. 박준석, "텍스트마이닝과 소셜네트워크분석 기법을 활용한 호텔분야 연구동향 분석", 관광레저연구, 28(9), pp.209~226 (2016), (사)한국관광레저학회
  14. 김창식, "토픽모델링과 시계열회귀분석을 활용한 정보시스템분야 연구동향 분석", 디지털콘텐츠학회논문지, 18(6), pp.1143~1150 (2017), 한국디지털콘텐츠학회[DOI]
  15. 김창식, "관광분야 연구동향 분석: 토픽모델링과 시계열분석을 중심으로", 관광레저연구, 29(12), pp.25~39 (2017), (사)한국관광레저학회
  16. 김태경, "텍스트마이닝을 이용한 정보보호 연구동향 분석", (사)디지털산업정보학회 논문지, 14(2), pp.19~25 (2018), (사)디지털산업정보학회[DOI]
  17. 윤혜진, "Research Trends Investigation Using Text Mining Techniques: Focusing on Social Network Services", 디지털콘텐츠학회논문지, 19(3), pp.513~519 (2018), 한국디지털콘텐츠학회[DOI]
  18. 박종순, "빅데이터 연구동향 분석: 토픽 모델링을 중심으로", (사)디지털산업정보학회 논문지, 15(1), pp.1~7 (2019), (사)디지털산업정보학회
  19. 현윤진, "텍스트 분석의 신뢰성 확보를 위한 스팸 데이터 식별 방안", 한국통신학회논문지, 42(2), pp.493~504 (2017), 한국통신학회[DOI]
  20. Griffiths, T. L., "Finding scientific topics", Proceedings of the National academy of Sciences, 101(suppl 1), pp.5228~5235 (2004)

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