Výsledky vyhledávání - (( (statne OR stat) python code analysis ) OR ( (statnych OR stateee) python code analysis ))*
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Zdroj: BMC health services research [BMC Health Serv Res] 2026 Jan 07. Date of Electronic Publication: 2026 Jan 07.
Způsob vydávání: Journal Article
Informace o časopise: Publisher: BioMed Central Country of Publication: England NLM ID: 101088677 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1472-6963 (Electronic) Linking ISSN: 14726963 NLM ISO Abbreviation: BMC Health Serv Res Subsets: MEDLINE
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Zdroj: 54es Journées de Statistique de la SFdS
JDS 2023 - 54e Journées de Statistique de la SFds
https://hal.science/hal-04384220
JDS 2023 - 54e Journées de Statistique de la SFds, SFdS, Jul 2023, Bruxelles (BEL), Belgique. ⟨10.48550/arXiv.2303.13590⟩Témata: Machine Learning (stat.ML), Machine Learning (cs.LG), Machine learning, neural networks, survival data, simulated data, missing data, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.AP]Statistics [stat]/Applications [stat.AP]
Geografické téma: Bruxelles (BEL), Belgium
Relation: hal-04384220; https://hal.science/hal-04384220; https://hal.science/hal-04384220/document; https://hal.science/hal-04384220/file/preprint_arxiv.pdf
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Zdroj: Stats, Vol 7, Iss 1, Pp 220-234 (2024)
Témata: scale-free networks, assortative networks, network rewiring, agent-based models, Bass innovation diffusion model, Statistics, HA1-4737
Popis souboru: electronic resource
Přístupová URL adresa: https://doaj.org/article/1ed58599ad2b446e8fba7bbd67fd64e7
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Zdroj: Clinical Nursing Research. Jun2024, Vol. 33 Issue 5, p355-369. 15p.
Témata: *MENTAL depression risk factors, *RISK assessment, *PREDICTION models, *SOCIAL determinants of health, *RECEIVER operating characteristic curves, *QUESTIONNAIRES, *PRIMARY health care, *RETROSPECTIVE studies, *QUANTITATIVE research, *DESCRIPTIVE statistics, *NURSING interventions, *SURVEYS, *HEALTH behavior, *MEDICAL records, *ACQUISITION of data, *SOCIODEMOGRAPHIC factors, *MACHINE learning, *DATA analysis software, *ACCURACY, *COMPARATIVE studies, *SOCIAL classes, *SENSITIVITY & specificity (Statistics), *SOCIAL isolation, *ALGORITHMS, *REGRESSION analysis, *MENTAL depression, *EVALUATION
Geografický termín: FLORIDA
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Zdroj: Future Internet; Sep2025, Vol. 17 Issue 9, p412, 28p
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Zdroj: instname:Universidad de Bogotá Jorge Tadeo Lozano ; reponame:Expeditio Repositorio Institucional UJTL
Témata: Ingeniería, Máquinas, Automatización, Auditorías, Seguridad, Security auditing
Geografické téma: Colombia
Popis souboru: 58 páginas; application/pdf
Relation: Alon, U., Zilberstein, M., Levy, O., and Yahav, E. (2019). code2vec: learning distributed representations of code. Proc. ACM Program. Lang., 3(POPL):1– 29.; Antunes, N. and Vieira, M. (2009). Comparing the Effectiveness of Penetration Testing and Static Code Analysis on the Detection of SQL Injection Vulnerabilities in Web Services. In 2009 15th IEEE Pacific Rim International Symposium on Dependable Computing, pages 301–306, Shanghai, China. IEEE.; Chang, Y., Liu, B., Cong, L., Deng, H., Li, J., and Chen, Y. (2019). Vulnerability Parser: A Static Vulnerability Analysis System for Android Applications. J. Phys.: Conf. Ser., 1288:012053.; Chong, S., Guttman, J., Datta, A., Myers, A., Pierce, B., Schaumont, P., Sherwood, T., and Zeldovich, N. (2016). Report on the NSF Workshop on Formal Methods for Security. arXiv:1608.00678 [cs]. arXiv: 1608.00678.; Dauber, E., Caliskan, A., Harang, R., Shearer, G., Weisman, M., Nelson, F., and Greenstadt, R. (2019). Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments. Proceedings on Privacy Enhancing Technologies, 2019(3):389–408. arXiv: 1701.05681; Ferreira, A. M. and Kleppe, H. (2011). Effectiveness of Automated Application Penetration Testing Tools. Technical report, OS3 University of Amsterdam.; FluidAttacks (2020). Integrates.; Free Software Foundation (2020). GNU diffutils; Ghaffarian, S. M. and Shahriari, H. R. (2017). Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey. ACM Computing Surveys, 50(4):1–36.; Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., and Chen, Z. (2018a). SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities. arXiv:1807.06756 [cs, stat]. arXiv: 1807.06756.; Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., and Zhong, Y. (2018b). VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. Proceedings 2018 Network and Distributed System Security Symposium. arXiv: 1801.01681.; Moor, O. d., Verbaere, M., Hajiyev, E., Avgustinov, P., Ekman, T., Ongkingco, N., Sereni, D., and Tibble, J. (2007). Keynote Address: .QL for Source Code Analysis. In Seventh IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2007), pages 3–16, Paris, France. IEEE; Ng, A. (2016). What Artificial Intelligence Can and Can’t Do Right Now. Harvard Business Review. Section: Analytics.; 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., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.; Rice, H. G. (1953). Classes of recursively enumerable sets and their decision problems. Trans. Amer. Math. Soc., 74(2):358–366.; Schwartz, E. J., Avgerinos, T., and Brumley, D. (2010). All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask). In 2010 IEEE Symposium on Security and Privacy, pages 317–331, Oakland, CA, USA. IEEE.; Sommer, R. and Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. In 2010 IEEE Symposium on Security and Privacy, pages 305–316, Oakland, CA, USA. IEEE.; Stefinko, Y., Piskozub, A., and Banakh, R. (2016). Manual and automated penetration testing. Benefits and drawbacks. Modern tendency. In 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), pages 488–491, Lviv, Ukraine. IEEE.; Yamaguchi, F., Wressnegger, C., Gascon, H., and Rieck, K. (2013). Chucky: exposing missing checks in source code for vulnerability discovery. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS ’13, pages 499–510, Berlin, Germany. ACM Press.; http://hdl.handle.net/20.500.12010/17241; http://expeditio.utadeo.edu.co
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Zdroj: Mathematics (2227-7390); Dec2025, Vol. 13 Issue 23, p3764, 29p
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Zdroj: Veterinary Sciences. Jan2026, Vol. 13 Issue 1, p88. 20p.
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Zdroj: Nutrients. Jan2026, Vol. 18 Issue 2, p232. 19p.
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Zdroj: Frontiers in Veterinary Science. 2026, p1-15. 15p.
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Zdroj: Veterinary World. Jan2026, Vol. 19 Issue 1, p1-14. 14p.
Témata: Multidrug resistance, Poultry farming, Holism, Enterococcus, Drug resistance in microorganisms, Escherichia coli, Anti-infective agents, Kenyans
Geografický termín: Kenya
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Zdroj: Frontiers in Mammal Science. 2025, p1-27. 27p.
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Zdroj: Veterinary Sciences. Oct2025, Vol. 12 Issue 10, p935. 17p.
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Zdroj: Nutrients. Oct2025, Vol. 17 Issue 19, p3119. 12p.
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Zdroj: Journal of Applied Clinical Medical Physics; Jul2025, Vol. 26 Issue 7, p1-9, 9p
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Zdroj: BMC Veterinary Research. 10/6/2025, Vol. 21 Issue 1, p1-11. 11p.
Témata: Peste des petits ruminants, Microbial virulence, Symptoms, Goats, Comparative studies, Seroconversion, Communicable disease control
Geografický termín: Ethiopia
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Zdroj: Nutrients. Oct2025, Vol. 17 Issue 19, p3075. 16p.
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Zdroj: Applied and Computational Harmonic Analysis. 48:445-481
Témata: FOS: Computer and information sciences, [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], Computer Science - Information Theory, Gaussian processes, super-resolution, Mathematics - Statistics Theory, Kac-Rice formula, Statistics Theory (math.ST), 01 natural sciences, Parametric hypothesis testing, 94A08, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 62J05, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], hypothesis testing, FOS: Mathematics, 62F03, 0101 mathematics, Gaussian process, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], 62H15, secondary 60E05, Super-Resolution, Information Theory (cs.IT), Probability (math.PR), MSC 2010 subject classifications: Primary 62E15, Hypothesis Testing, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], 60G15, 62E15, 62F03, 60G15, 62H10, 62H15 (Primary) 60E05, 60G10, 62J05, 94A08 (secondary), [INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT], Gaussian Process, Image processing (compression, reconstruction, etc.) in information and communication theory, 62H10, 60G10, Mathematics - Probability
Popis souboru: application/xml; application/pdf
Přístupová URL adresa: https://www.sciencedirect.com/science/article/am/pii/S1063520318301556
http://arxiv.org/abs/1706.00679
https://zbmath.org/7140143
https://doi.org/10.1016/j.acha.2018.07.001
https://inria.hal.science/hal-01686434v1/document
https://inria.hal.science/hal-01686434v1
https://hal.science/hal-04955851v1
https://doi.org/10.1016/j.acha.2018.07.001 -
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Zdroj: Frontiers in Veterinary Science. 2025, p1-13. 13p.
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Zdroj: Journal of Animal Science & Biotechnology. 8/11/2025, Vol. 16 Issue 1, p1-21. 21p.
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