Výsledky vyhledávání - ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.1: Process Management~

Upřesnit hledání
  1. 1
  2. 2
  3. 3
  4. 4

    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

    Autoři: Okunola, Abiodun

  5. 5

    Zdroj: Congrès JRES : Les Journées Réseaux de l'Enseignement et de la Recherche ; https://hal.science/hal-01639362 ; Congrès JRES : Les Journées Réseaux de l'Enseignement et de la Recherche, Nov 2017, Nantes, France

    Geografické téma: Nantes, France

  6. 6
  7. 7

    Zdroj: IEEE Transactions on Parallel and Distributed Systems. 33:1464-1477

    Popis souboru: application/pdf

  8. 8
  9. 9
  10. 10

    Přispěvatelé: Von Wissel, Dirk Jordan, Yohan Von, Dirk a další

    Zdroj: 9th European Congress on Embedded Real Time Software and Systems (ERTS 2018
    https://hal.archives-ouvertes.fr/hal-01710774
    9th European Congress on Embedded Real Time Software and Systems (ERTS 2018, Jan 2018, Toulouse, France

    Geografické téma: Toulouse, France

  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18

    Zdroj: 16th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE’17)
    https://hal.inria.fr/hal-01580582
    16th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE’17), Oct 2017, Vancouver, Canada. ⟨10.1145/3136040.3136045⟩
    http://conf.researchr.org/home/gpce-2017

    Geografické téma: Vancouver

    Time: Vancouver, Canada

    Relation: info:eu-repo/semantics/altIdentifier/arxiv/1709.00833; hal-01580582; https://hal.inria.fr/hal-01580582; https://hal.inria.fr/hal-01580582/document; https://hal.inria.fr/hal-01580582/file/gpce.pdf; ARXIV: 1709.00833

  19. 19
  20. 20