[휴실] 새벽벌도서관 2층 열람실(새벽별당 제외) 및 4층 쉼터 리모델링 안내 (공사기간: ~ 3. 31.)
[휴관] 천장 배관 교체 공사 기간 중앙도서관 '휴관' 실시 및 방문대출 서비스 안내 ( ~ 3. 2.)
[휴관] RFID 기반 도서관리시스템 전환 작업 기간 도서관 휴관·휴실 안내 (시범운영: 2. 2. ~ 3. 2.)
2026년 2월 학위논문(원문파일/인쇄본) 제출 안내_원문파일 제출 기한 연장 : ~2.27.(금)

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프랑스어
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스페인어
à á Á é É í Í Ó ó Ú ú Ñ ñ ä Ä
Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ  
Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω  
α β γ δ ε ζ η θ ι κ λ μ  
ν ξ ο π ρ ς σ τ υ φ χ ψ ω
À Á Â Ã Ä Å Æ Ç È É Ê Ë Ì Í Î Ï
Ð Ñ Ò Ó Ô Õ Ö Ø Ù Ú Û Ü Ý Þ ß  
à á â ã ä å æ ç è é ê ë ì í î ï
ð ñ ò ó ô õ ö ø ù ú û ü ý þ ÿ  
А Б В Г Д Е Ё Ж З И Й К Л М Н О П
Р С Т У Ф Х Ц Ч Ш Щ Ъ Ы Ь Э Ю Я  
а б в г д е ё ж з и й к л м н о п
р с т у ф х ц ч ш щ ъ ы ь э ю я  
ⅰ ⅱ ⅲ ⅳ ⅴ ⅵ ⅶ ⅷ ⅸ ⅹ Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ
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ㆇ ㆈ ㆉ ㆊ ㆋ ㆌ ㆍ ㆎ                  
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± × ÷ ≠ ≤ ≥ ∞ ∴ ∠ ⊥ ⌒ ∂ ∇ ≡ ≒
≪ ≫ √ ∽ ∝ ∵ ∫ ∬ ∈ ∋ ⊆ ⊇ ⊂ ⊃ ∪ ∩ ∧
∨ ￢ ⇒ ⇔ ∀ ∃ ∮ ∑ ∏ ＋ － ＜ ＝ ＞      
  ก ข ฃ ค ฅ ฆ ง จ ฉ ช ซ ฌ ญ ฎ ฏ
ฐ ฑ ฒ ณ ด ต ถ ท ธ น บ ป ผ ฝ พ ฟ
ภ ม ย ร ฤ ล ฦ ว ศ ษ ส ห ฬ อ ฮ ฯ
ะ ั า ำ ิ ี ึ ื ุ ู ฺ         ฿
เ แ โ ใ ไ ๅ ๆ ็ ่ ้ ๊ ๋ ์ ํ ๎ ๏
᠀ ᠁ ᠂ ᠃ ᠄ ᠅ ᠆ ᠇ ᠈ ᠉ ᠊          
᠐ ᠑ ᠒ ᠓ ᠔ ᠕ ᠖ ᠗ ᠘ ᠙            
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ᡀ ᡁ ᡂ ᡃ ᡄ ᡅ ᡆ ᡇ ᡈ ᡉ ᡊ ᡋ ᡌ ᡍ ᡎ ᡏ
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'학술논문' 에서 검색결과 1,324건 | 목록 1~20
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1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras
Okunola, Abiodun
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Okunola, Abiodun
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Billy, Elly
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THE MANY LIVES OF H. RAP BROWN.
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TIME Magazine. 11/8/2021, Vol. 198 Issue 17/18, p52-59. 8p. 2 Color Photographs, 6 Black and White Photographs.
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