Suchergebnisse - (( (stateni OR stat) python code analysis ) OR ( (statnych OR statenee) python code analysis ))

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

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

Weitere Verfasser: Dufossé, Paul Benzekry, Sébastien Méthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique (COMPO) et al.

Quelle: 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⟩

Schlagwörter: 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]

Geographisches Schlagwort: 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

Verfügbarkeit: 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

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

Autoren: Duarte, Martha Salamanca, Mayra Gonzalez, Juan M. et al.

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

Schlagwörter: *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

Geografische Kategorien: FLORIDA

Statistics for Data Science and Analytics

Autoren: Peter C. Bruce Peter Gedeck Janet Dobbins

Resource Type: eBook.

Schlagworte: Statistics, Python (Computer program language)

Categories: COMPUTERS / Languages / Python, COMPUTERS / Data Science / Data Analytics, MATHEMATICS / Probability & Statistics / General

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

Autoren: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. et al.

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

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

TRIQS/SOM 2.0: Implementation of the stochastic optimization with consistent constraints for analytic continuation.

Autoren: Krivenko, Igor Mishchenko, Andrey S.

Quelle: Computer Physics Communications. Nov2022, Vol. 280, pN.PAG-N.PAG. 1p.

Schlagwörter: *PYTHON programming language, *GREEN'S functions, *MAXIMUM entropy method, *COVARIANCE matrices, *STATISTICAL ensembles, *PROGRAMMING languages, *REGULARIZATION parameter

Generation of Scale-Free Assortative Networks via Newman Rewiring for Simulation of Diffusion Phenomena

Autoren: Laura Di Lucchio Giovanni Modanese

Quelle: Stats, Vol 7, Iss 1, Pp 220-234 (2024)

Schlagwörter: assortative networks, Physics - General Physics, General Physics (physics.gen-ph), network rewiring, Statistics, 0103 physical sciences, FOS: Physical sciences, scale-free networks, agent-based models, Bass innovation diffusion model, 01 natural sciences, HA1-4737

Zugangs-URL: http://arxiv.org/abs/2310.13716
https://doaj.org/article/1ed58599ad2b446e8fba7bbd67fd64e7

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

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

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

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

Python for Data Science For Dummies

Autoren: John Paul Mueller Luca Massaron

Resource Type: eBook.

Schlagworte: Data structures (Computer science), Data mining, Python (Computer program language), Programming languages (Electronic computers)

Categories: COMPUTERS / Programming / General

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

Autoren: Stewart, Mathew Peters, Carli Ziegler, Michael J. et al.

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

Machine learning for prioritizing security code-based Vulnerability discovery

Autoren: Ballestas, Rafael García Bedoya, Olmer

Weitere Verfasser: Ballestas, Rafael García Bedoya, Olmer

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

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

Geographisches Schlagwort: Colombia

Dateibeschreibung: 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

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

Eating Disorders in People Who Identify as Gender-Diverse: Associations Between Gender Diversity, Eating Disorder Diagnosis, Minority Stress Experiences and Mental Health Comorbidity.

Autoren: Murphy, Rebecca Sharpe, Helen

Quelle: Nutrients. Feb2026, Vol. 18 Issue 3, p458. 24p.

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

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

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

Varietal Differences in Kidney Beans Modulate Gut Microbiota and Inflammation During High-Fat Diet-Induced Obesity in Male Mice.

Autoren: Rodrigue, Alexane F. Pereira, Bruna B. Freije, Giorgio et al.

Quelle: Nutrients. Feb2026, Vol. 18 Issue 3, p461. 34p.

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

Autoren: Quintana-Castanedo, Lucía Maseda, Rocío Sánchez-Ramón, Silvia et al.

Quelle: 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.

Autoren: Haake, Alexander F. H. Loriani Fard, Alina K. Jovanovic, Vladimir M. et al.

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

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

Autoren: Tan, Lizhi Cai, Xinyu Kong, Yuan et al.

Quelle: 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.

Autoren: Csorba, Szilveszter Vribék, Krisztián Farkas, Máté et al.

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

Feather forensics: tracing the origins of parrots in wildlife trade with stable isotopes and citizen science.

Autoren: Hill, K. G. W. Delean, S. Hall, T. et al.

Quelle: Animal Conservation. Aug2025, Vol. 28 Issue 4, p582-591. 10p.

Schlagworte: Stable isotopes, Parrots, Wild animal trade, Citizen science, Isotopic analysis

Geografische Kategorien: Australia

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

Autoren: Gautney, Joanna Aguilar, Christina Chan, Julian et al.

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

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

Autoren: Han, Mingshuo Li, Baoshan Li, Qi et al.

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