Výsledky vyhledávání - acm: c.: computer systems organisation/c.4: performance of systems/c.4.2: measurement techniques

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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

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    Zdroj: ISSN: 1045-9219 ; IEEE Transactions on Parallel and Distributed Systems ; https://inria.hal.science/hal-03324177 ; IEEE Transactions on Parallel and Distributed Systems, 2022, 33 (6), pp.1464-1477. ⟨10.1109/TPDS.2021.3111159⟩.

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    Popis souboru: application/pdf

    Relation: Carrasquilla Marín, Shirly Marina, Ulloque Rodríguez, Edinson, Guerrero Santander, César Darío (2006). Evaluación de técnicas de medición de ancho de banda disponible. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB; BitTorrent, Inc. http://bittorrent.com/ Marzo de 2006; BRESLAU, L., KNIGHTLY, E., SHENKER, S. y STOIKA, I. “Endpoint admission control: Architectural issues and performance,” ACM SIGCOMM. 2000.; DOVROLIS, C.; RAMANATHAN, P. y MOORE, David. What do packet dispersion techniques measure? In Proc. of ACM INFOCOM’01. Anchorage. Alaska, USA. Abril, 2001.; Dummynet. http://info.iet.unipi.it/~luigi/ip_dummynet/. Agosto, 2005.; FALL, Kevin y FLOYD, Rally. Simulation-based Comparisons of Tahoe, Reno, and SACK TCP. Lawrence Berkeley National Laboratory.; Gnutella.Com. http://www.gnutella.com/ Marzo de 2006; GUERRERO, César. Test Bed for ABETs Evaluation. http://fis.unab.edu.co/docentes/cguerrer Marzo de 2006; HU, N. y STEENKISTE, P. Evaluation and Characterization of Available Bandwidth Techniques. IEEE JSAC Lanzamiento especial en Internet y WWW Measurement, Mapping, and Modeling. 2003.; HU, N. y STEENKISTE, Peter. Estimating Available Bandwidth Using Packet Pair Probing. Septiembre 9 de 2002; Internet World Stats. Usage and Population Statistics. http://www.internetworldstats.com/sa/co.htm Marzo de 2006; JAIN, M. y DOVROLIS, C. End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput. ACMSIGCOMM Simposio Communications Architectures Protocols. Pittsburgh. Agosto, 2002.; JAIN, M. y DOVROLIS, C. Pathload: A Measurement Tool for End-to-End Available Bandwidth. Passive and Active Measurements. Fort Collins. Colorado. Marzo, 2002.; MANISH, Jain and CONSTANTINO, Dovrolis. Ten Fallacies and Pitfalls on End-to-End Available Bandwidth Estimation. ACM Internet Measurements Conference (ICM). Sicilia, Italia. Octubre, 2004.; MASCOLO, Saberio, CASETTI, Claudio, GERLA, Mario, SANADIDI, M. Y. y WANG, Ren. TCP Westwood: Bandwidth Estimation for Enhanced Transport over Wireless Links.; PADHYE, Jitendra, FIROIU, Victor, TOWSLY, Donald F., y KUROSE, James F. Modeling TCP Reno Performance: A Simple Model and Its Empirical Validation. IEEE/ACM Transactions on Networking. Vol. 8. Nº 2. Abril, 2000. 133 p.; PRASAD, Ravi y DOVROLIS, Constantinos. MURRAY, Margareth y CLAFFY, Kc. Bandwidth Estimation: Metrics, Measurement Techniques, and Tools.; RIBEIRO, V., RIEDI, R., BARANJUK, R., NAVRATIL, J. y COTTRELL, L. “PathChirp: Efficient Available Bandwidth Estimation for Network Paths”. Department of Electrical and Computer Engineering, Rice University. 2004.; RIBEIRO, V.; COATES, M.; RIEDI, R.; SARVOTHAM, S. y BARANJUK, R. Multifractal cross traffic estimation. Septiembre, 2000.; STALLINGS, William. Comunicaciones y Redes de Computadores. 7ª ed.; STALLINGS, William. ISDN and Broadband ISDN with Frame Relay and ATM. 4ª ed.; STRAUSS, J., KATABI, D. y KAASHOEK, F. A Measurement Study of Available Bandwidth Estimation Tools. MIT Computer Science and Artificial Intelligence Laboratory.; TANENBAUM, Andrew S. Computer Networks. 4ª ed.; The FreeBSD proyect. http://www.freebsd.org/ Marzo de 2006; http://hdl.handle.net/20.500.12749/1352; reponame:Repositorio Institucional UNAB

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    Zdroj: IET Software (Institution of Engineering & Technology); Mar2010, Vol. 4 Issue 1, p79-90, 12p, 3 Black and White Photographs, 2 Diagrams, 3 Charts

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    Přispěvatelé: Nance, R. E.

    Zdroj: ACM SIGSIM Simulation Digest ; volume 21, issue 2, page 53 ; ISSN 0163-6103

    Dostupnost: https://doi.org/10.1145/382264.1108821
    https://dl.acm.org/doi/10.1145/382264.1108821
    https://dl.acm.org/doi/pdf/10.1145/382264.1108821

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