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Study on Development of Graphic User Interface for TensorFlow Based on Artificial Intelligence

인공지능 기반의 TensorFlow 그래픽 사용자 인터페이스 개발에 관한 연구

  • Song, Sang Gun (Department of Health Policy and Management, College of Social science, Inje University) ;
  • Kang, Sung Hong (Department of Health Policy and Management, College of Social science, Inje University) ;
  • Choi, Youn Hee (Department of Medical Administration, College of Health, Dong-Eui Institute of Technology) ;
  • Sim, Eun Kyung (Department of Beauty Care, College of Health, Social Welfare & Education, Tongmyong University) ;
  • Lee, Jeong- Wook (Department of Health public Administration, College of Health and Social Welfare, Silla university) ;
  • Park, Jong-Ho (Dongsan Medical Center, Kyeimyoung University) ;
  • Jung, Yeong In (College of Medicine, Pusan National University) ;
  • Choi, Byung Kwan (College of Medicine, Pusan National University)
  • 송상근 (인제대학교 사회과학대학 보건행정학과) ;
  • 강성홍 (인제대학교 사회과학대학 보건행정학과) ;
  • 최연희 (동의과학대학교 보건대학 의무행정과) ;
  • 심은경 (동명대학교 보건복지교육대학 뷰티케어학과) ;
  • 이정욱 (신라대학교 보건복지대학 보건행정학과) ;
  • 박종호 (계명대학교 동산의료원) ;
  • 정영인 (부산대학교 의과대학 의학과) ;
  • 최병관 (부산대학교 의과대학 의학과)
  • Received : 2018.02.26
  • Accepted : 2018.05.20
  • Published : 2018.05.28

Abstract

Machine learning and artificial intelligence are core technologies for the 4th industrial revolution. However, it is difficult for the general public to get familiar with those technologies because most people lack programming ability. Thus, we developed a Graphic User Interface(GUI) to overcome this obstacle. We adopted TensorFlow and used .Net of Microsoft for the develop. With this new GUI, users can manage data, apply algorithms, and run machine learning without coding ability. We hope that this development will be used as a basis for developing artificial intelligence in various fields.

기계 학습 및 인공지능은 제 4차 산업혁명의 핵심 기술이다. 하지만 프로그래밍 능력을 요구하는 기계 학습 플랫폼의 특성 상 일반 사용자들의 접근이 힘들기 때문에 인공지능이나 기계학습의 대중화는 제한을 받고 있다. 본 연구에서는 그래픽 사용자 인터페이스(Graphic User Interface, GUI)를 도입하여 이러한 한계를 극복하고 인공지능 활용에 대한 일반인의 접근성을 향상시키고자 하였다. 기본 기계 학습 플랫폼으로는 Tensorflow를 채택하였고 GUI는 마이크로 소프트 사의 .Net 환경을 활용하여 작성하였다. 새로운 사용자 인터페이스를 이용하면 일반 사용자도 파이썬 프로그래밍에 대한 부담없이 직관적으로 데이터를 관리하고, 알고리즘을 적용하고, 기계 학습을 실행할 수 있다. 우리는 이 개발이 다양한 분야에서의 인공지능 개발에 기초가 되는 자료로 활용되었으면 한다.

Keywords

References

  1. J. W. Kim, B. K. Park, Y. W. Noh & S. H. Lim (2016). 2016 Dabos Report; 4th industrial revolution spurred by artificial intelligence. Seoul : Maeil Business Newspaper.
  2. O. Cann. (2016). These are the top 10 emerging technologies of 2016. World economic forum. https://www.weforum.org/agenda/2016/06/top-10-emerging-technologies-2016. accessed on 23 January 2018.
  3. H. S. Cho. (2016). Artificial Intelligence Plantform Trend. Software Policy Research Center.
  4. D. J. Kim, H. Y. Kwon & J. I. Im. (2017). Measures to minimize the side effects of the increased use of Artificial Intelligence Robo-Advisor. Joornal of the Korea Convergence Society, 8(10), 67-73. https://doi.org/10.15207/JKCS.2017.8.4.067
  5. S. W. Eom.. (2016. 8. 8). 'Doctor AI' detects tuberculosis which was missed by human doctor. HanKyoReh News Paper.
  6. Y. C. Choi. (2017). Payment Signature Identification Technique using Tensorflow. Kyungpook National University, Kyungpook., p.6.
  7. S. Degroeve, De Baets, B., Van de Peer, Y. & Rouze, P. (2002). Feature subset selection for splice site prediction. Bioinformatics. 18(2), S75-S83. https://doi.org/10.1093/bioinformatics/18.suppl_2.S75
  8. P. Bucher. (1990). Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. Journal of molecular biology, 212(4), 563-578. https://doi.org/10.1016/0022-2836(90)90223-9
  9. N. D. Heintzman et al. (2007). Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nature genetics, 39(3), 311-318. https://doi.org/10.1038/ng1966
  10. B. K. Choi et al. (2017). Tensorflow Programming Basics. Seoul: Cheong-Gu cultual conpany.
  11. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, & M. Kudlur (2016). TensorFlow: A System for Large-Scale Machine Learning. In OSDI. 16, 265-283.
  12. Tom M. Mitchel.l (1997). Machine Learning, McGraw-Hill Science.
  13. J. H. Ku. (2018). A study on Adaptive Learning Model for P erformance Improvement of Stream Analytics. Journal of Convergence for Information Technology, 8(1), 201-206. https://doi.org/10.22156/CS4SMB.2018.8.1.201
  14. L. Rampasek & A. Goldenber. (2016). Tensorflow: Biology's gateway to deep learning?, Cell systems, 2(1), 12-14. https://doi.org/10.1016/j.cels.2016.01.009
  15. IBM. (2017). Watson for Oncology. https://www.ibm.com/watson/health/oncology-and-genomics/oncology/, accessed on 15 September 2017.
  16. J. O. Park & D. H. Choi. (2015). Security tendency analysis techniques through machine learning algorithms applications in big da\ta environments. Journal of Digital Convergence, 13(9), 269-276. https://doi.org/10.14400/JDC.2015.13.9.269