Výsledky vyhľadávania - (( (statne OR stat) python code analysis ) OR ( (statnych OR stateeeee) python code analysis ))*

Prediction of Positive Patient Health Questionnaire-2 Screening Using Area Deprivation Index in Primary Care.

Autori: Duarte, Martha Salamanca, Mayra Gonzalez, Juan M. a ďalší

Zdroj: Clinical Nursing Research. Jun2024, Vol. 33 Issue 5, p355-369. 15p.

Predmety: *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

A comparison of machine learning algorithms for survival with missing data ; Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes

Autori: Dufossé, Paul Benzekry, Sébastien Méthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique (COMPO) a ďalší

Prispievatelia: Dufossé, Paul Benzekry, Sébastien Méthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique (COMPO) a ďalší

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⟩

Predmety: 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

Dostupnosť: https://hal.science/hal-04384220
https://hal.science/hal-04384220/document
https://hal.science/hal-04384220/file/preprint_arxiv.pdf
https://doi.org/10.48550/arXiv.2303.13590

Machine learning for prioritizing security code-based Vulnerability discovery

Autori: Ballestas, Rafael García Bedoya, Olmer

Prispievatelia: Ballestas, Rafael García Bedoya, Olmer

Zdroj: instname:Universidad de Bogotá Jorge Tadeo Lozano ; reponame:Expeditio Repositorio Institucional UJTL

Predmety: Ingeniería, Máquinas, Automatización, Auditorías, Seguridad, Security auditing

Geografické téma: Colombia

Popis súboru: 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

Dostupnosť: https://hdl.handle.net/20.500.12010/17241
http://expeditio.utadeo.edu.co

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries.

Autori: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. a ďalší

Zdroj: Future Internet; Sep2025, Vol. 17 Issue 9, p412, 28p

Predmety: LANGUAGE models, EXPERIMENTAL design, PYTHON programming language, GENERATIVE pre-trained transformers, QUANTITATIVE research, BENCHMARK problems (Computer science), CODE generators

The state of the late Quaternary megafauna extinction debate: a systematic review and analysis.

Autori: Stewart, Mathew Peters, Carli Ziegler, Michael J. a ďalší

Zdroj: Frontiers in Mammal Science. 2025, p1-27. 27p.

Portable Electronic Olfactometer for Non-Invasive Screening of Canine Ehrlichiosis: A Proof-of-Concept Study Using Machine Learning.

Autori: Durán Cotrina, Silvana Valentina Acevedo, Cristhian Manuel Durán Carrillo Gómez, Jeniffer Katerine

Zdroj: Veterinary Sciences. Jan2026, Vol. 13 Issue 1, p88. 20p.

A statistical approach to automated analysis of the low‐contrast object detectability test for the large ACR MRI phantom.

Autori: Golestani, Ali M. Gee, Julia M.

Zdroj: Journal of Applied Clinical Medical Physics; Jul2025, Vol. 26 Issue 7, p1-9, 9p

Predmety: LINEAR statistical models, IMAGING phantoms, QUALITY control, PYTHON programming language, MAGNETIC resonance imaging

Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python.

Autori: Dunker, Fabian Mendoza, Emil Reale, Marco

Zdroj: Mathematics (2227-7390); Dec2025, Vol. 13 Issue 23, p3764, 29p

Predmety: PYTHON programming language, MAXIMUM likelihood statistics, CONSUMPTION (Economics), MATHEMATICAL programming, MULTILEVEL models, SCIENTIFIC computing

Zinc as a Biomarker of Nutritional Status and Clinical Burden in Recessive Dystrophic Epidermolysis Bullosa: Implications for Preventive Monitoring.

Autori: Quintana-Castanedo, Lucía Maseda, Rocío Sánchez-Ramón, Silvia a ďalší

Zdroj: Nutrients. Jan2026, Vol. 18 Issue 2, p232. 19p.

Strong correlation of gene counts and differentially expressed genes between a 3′ RNA-Seq and an RNA hybridization platform in transcriptome analyses from canine archival tissues.

Autori: Haake, Alexander F. H. Loriani Fard, Alina K. Jovanovic, Vladimir M. a ďalší

Zdroj: Frontiers in Veterinary Science. 2025, p1-13. 13p.

Genome-wide analyses reveal intricate genetic mechanisms underlying egg production efficiency in chickens.

Autori: Tan, Lizhi Cai, Xinyu Kong, Yuan a ďalší

Zdroj: Journal of Animal Science & Biotechnology. 8/11/2025, Vol. 16 Issue 1, p1-21. 21p.

Data-Driven Early Warning Approach for Antimicrobial Resistance Prediction–Anomaly Detection Based on High-Level Indicators.

Autori: Csorba, Szilveszter Vribék, Krisztián Farkas, Máté a ďalší

Zdroj: Veterinary Sciences. Oct2025, Vol. 12 Issue 10, p935. 17p.

Enhanced cattle identification using Siamese network and MobileViT with EMA attention.

Autori: Han, Mingshuo Li, Baoshan Li, Qi a ďalší

Zdroj: Frontiers in Veterinary Science. 2026, p1-15. 15p.

Cross-domain antimicrobial resistance in poultry farming: A One Health assessment of antimicrobial use and multidrug resistance in Kiambu County, Kenya.

Autori: Munene, Ann Kangai Mwangi, Peter Muiruri Bebora, Lilly Caroline a ďalší

Zdroj: Veterinary World. Jan2026, Vol. 19 Issue 1, p1-14. 14p.

Predmet: Multidrug resistance, Poultry farming, Holism, Enterococcus, Drug resistance in microorganisms, Escherichia coli, Anti-infective agents, Kenyans

Geografický termín: Kenya

Sex-Specific Dietary Predictors of Blood Glucose Identified Through Decision Tree Modeling in Adults.

Autori: Gautney, Joanna Aguilar, Christina Chan, Julian a ďalší

Zdroj: Nutrients. Oct2025, Vol. 17 Issue 19, p3119. 12p.

Comparative virulence study of locally circulating peste des petits ruminants virus (PPRV) strains in indigenous Ethiopian goats.

Autori: Aklilu, Fasil Ashenafi, Hagos Kassa, Tesfu a ďalší

Zdroj: BMC Veterinary Research. 10/6/2025, Vol. 21 Issue 1, p1-11. 11p.

Predmet: Peste des petits ruminants, Microbial virulence, Symptoms, Goats, Comparative studies, Seroconversion, Communicable disease control

Geografický termín: Ethiopia

ASPA 25th Congress Book of Abstract.

Autori: Renna, Manuela (AUTHOR) manuela.renna@unito.it

Zdroj: Italian Journal of Animal Science. 2025 Suppl 1, Vol. 24 Issue 1, p1-463. 463p.

Predmet: Conferences & conventions

Sweet Taste Adaptation to Sugars, Sucralose, and Their Blends: A Human and Rodent Perspective.

Autori: Okoye, Stephanie I. Kim, Minjae Petty, Sara a ďalší

Zdroj: Nutrients. Oct2025, Vol. 17 Issue 19, p3075. 16p.

System and Method for Code and Data Versioning in Computerized Data Modeling and Analysis

GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model.

Autori: Li, Jingmao Xu, Yaqing Ma, Shuangge a ďalší

Zdroj: Bioinformatics; Sep2025, Vol. 41 Issue 9, p1-9, 9p

Predmety: CANCER research, GENOTYPE-environment interaction, DIAGNOSTIC imaging, DEEP learning, EMPIRICAL research, COMPUTER simulation