Výsledky vyhledávání - acm: d.: software/d.4: operating system/d.4.8: performance/d.4.8.3: operational analysis

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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

    Autoři: Okunola, Abiodun

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    Zdroj: Séminaire SRCO - CERMID ; https://hal.inrae.fr/hal-05138693 ; Séminaire SRCO - CERMID, Société canadienne de recherche opérationnelle SCRO -Québec avec la collaboration du département Opérations et Systèmes de Décision et le CERMID, Apr 2025, Québec (Canada), Université Laval, Canada

    Témata: Sustainibility transitions, Agricultural Stakeholders, Agroecological Performance, Agricultural Public Policies, Agroforestry systems, Agroforestery, Territorial Decision-Making, Multicriteria Evaluation ou Multicriteria Decision Aiding (MCDA), Decision support, Parties prenantes agricoles, Evaluation de la performance, Modélisation multicritère pour l'aide à la décision, Transition agroécologique agroécosystèmes, Agroforesterie, Poltiques publiques, Transition agroécologique, Systèmes agroforestiers, JEL: C - Mathematical and Quantitative Methods/C.C4 - Econometric and Statistical Methods: Special Topics/C.C4.C44 - Operations Research • Statistical Decision Theory, JEL: D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D81 - Criteria for Decision-Making under Risk and Uncertainty, JEL: O - Economic Development, Innovation, Technological Change, and Growth/O.O1 - Economic Development/O.O1.O13 - Agriculture • Natural Resources • Energy • Environment • Other Primary Products, JEL: Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q5 - Environmental Economics/Q.Q5.Q57 - Ecological Economics: Ecosystem Services • Biodiversity Conservation • Bioeconomics • Industrial Ecology, ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.8: Performance/D.4.8.3: Operational analysis, ACM: F.: Theory of Computation/F.4: MATHEMATICAL LOGIC AND FORMAL LANGUAGES/F.4.2: Grammars and Other Rewriting Systems/F.4.2.0: Decision problems, ACM: F.: Theory of Computation/F.4: MATHEMATICAL LOGIC AND FORMAL LANGUAGES/F.4.3: Formal Languages/F.4.3.3: Decision problems, ACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS/H.4.2: Types of Systems/H.4.2.0: Decision support (e.g.

    Geografické téma: Université Laval

    Time: Québec (Canada), Université Laval, Canada

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    Zdroj: ASIAN'06: 11th Annual Asian Computing Science Conference ; https://inria.hal.science/inria-00130210 ; ASIAN'06: 11th Annual Asian Computing Science Conference, National Institute of Informatics, Dec 2006, Tokyo/Japan

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    Zdroj: The International Conference on Stabilization, Safety, and Security in Distributed Systems (SSS 2015) ; https://hal.science/hal-01213273 ; The International Conference on Stabilization, Safety, and Security in Distributed Systems (SSS 2015), Aug 2015, Edmonton, Canada. pp.156-170, ⟨10.1007/978-3-319-21741-3_11⟩

    Geografické téma: Edmonton

    Time: Edmonton, Canada

    Relation: hal-01213273; https://hal.science/hal-01213273

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    Zdroj: 2013 IEEE 12th International Symposium on Network Computing & Applications; 2013, p211-218, 8p

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    Zdroj: 2016 IEEE 24th International Symposium on Modeling, Analysis & Simulation of Computer & Telecommunication Systems (MASCOTS); 2016, p159-168, 10p

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    Zdroj: 2019 IEEE/ACM 14th International Workshop on Automation of Software Test (AST), pp. 21–27, Montreal, QC, Canada, 27/5/2019
    info:cnr-pdr/source/autori:Pietrantuono R.; Bertolino A.; De Angelis G.; Miranda B.; Russo S./congresso_nome:2019 IEEE%2FACM 14th International Workshop on Automation of Software Test (AST)/congresso_luogo:Montreal, QC, Canada/congresso_data:27%2F5%2F2019/anno:2019/pagina_da:21/pagina_a:27/intervallo_pagine:21–27
    2019 IEEE/ACM 14th International Workshop on Automation of Software Test (AST)

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