Výsledky vyhľadávania - (( (statu OR stat) python code analysis ) OR ( (stateen OR stateei) python code analysis ))

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

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

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

Testing an Educational Program to Improve Goals of Care Conversations Between Patients and Their Care Teams

Autori: National Cancer Institute (NCI)

Zdroj: A Randomized Controlled Trial of an Intervention Called "Algorithm-Enabled Patients Activated in Cancer Care Through Teams" (A-PACT) to Improve Goals of Care Communication for People With Cancer

An Empirical Evaluation of Ensemble Models for Python Code Smell Detection.

Autori: Rao, Rajwant Singh Dewangan, Seema Mishra, Alok

Zdroj: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 13, p7472, 27p

Predmety: ENSEMBLE learning, FEATURE selection, BOOSTING algorithms, PYTHON programming language, COMPUTER software quality control, DESIGN failures, DATA augmentation

Recurrence Quantification Analysis Based Methodology in Automatic Aerobic Threshold Detection: Applicability and Accuracy across Age Groups, Exercise Protocols and Health Conditions

Autori: Serantoni C. De Spirito M. Maulucci G. a ďalší

Prispievatelia: Serantoni C. De Spirito M. Maulucci G. a ďalší

Predmety: aerobic transition, athletes, cardiac signal processing, cardiorespiratory monitoring, heart rate variability, nonlinear time series analysis, obesity, python code, wearable and portable sensors, Settore PHYS-06/A - Fisica per le scienze della vita, l'ambiente e i beni culturali

Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001342706200001; volume:14; issue:20; firstpage:N/A; lastpage:N/A; issueyear:2024; journal:APPLIED SCIENCES; https://hdl.handle.net/10807/311687

Dostupnosť: https://hdl.handle.net/10807/311687
https://doi.org/10.3390/app14209216

Primary data for BJC Carbonic anhydrase IX is a pH-stat that sets an acidic tumour extracellular pH in vivo

Autori: Griffiths, JR Lee, Shen-Han McIntyre, Dominick John a ďalší

Témy: Carbonic anhydrase IX, Extracellular pH, Intracellular pH, ISUCA, Magnetic Resonance Spectroscopy, MRS, pH-stat, Tumour pH, Dataset

URL: https://www.repository.cam.ac.uk/handle/1810/361916
https://doi.org/10.17863/CAM.26029
https://doi.org/10.1038/s41416-018-0216-5
https://www.repository.cam.ac.uk/handle/1810/287141
https://doi.org/10.1038/s41416-018-0216-5
https://www.repository.cam.ac.uk/handle/1810/287141

Programmed differently? Testing for gender differences in Python programming style and quality on GitHub.

Autori: Brooke, Siân

Zdroj: Journal of Computer-Mediated Communication; Jan2024, Vol. 29 Issue 1, p1-13, 13p

Predmety: PYTHON programming language, PYTHONS, RANDOM forest algorithms, GENDER

Towards the genome-scale discovery of bivariate monotonic classifiers.

Autori: Fourquet, Océane Krejca, Martin S. Doerr, Carola a ďalší

Zdroj: BMC Bioinformatics; 9/2/2025, Vol. 26 Issue 1, p1-32, 32p

Predmety: CLASSIFICATION, BIOMARKERS, ALGORITHMS, MACHINE learning, BIOINFORMATICS

Python Programming: Numerical Analysis/Machine Learning Series.

Autori: Doskas, Constantinos

Zdroj: ISSA Journal; Oct2023, Vol. 21 Issue 10, p34-38, 5p

Predmety: PYTHON programming language, NUMERICAL analysis, MACHINE learning

Automating fuzz driver generation for deep learning libraries with large language models.

Autori: Zheng, Tianming Meng, Fanchao Yi, Ping a ďalší

Zdroj: Cybersecurity (2523-3246); 1/4/2026, Vol. 9 Issue 1, p1-21, 21p

Predmety: LANGUAGE models, COMPUTER software testing, TEST systems, STATISTICAL reliability, MECHANIZATION

Insights and Analysis of Open-source License Violation Risks in LLMs Generated Code.

Autori: Yibo Wang Ying Wang Yue Yu a ďalší

Zdroj: International Journal of Software & Informatics; 2025, Vol. 15 Issue 2, p145-173, 29p

Predmety: OPEN source software, COPYRIGHT infringement, LANGUAGE models, DATA analytics, CODE generators, INTELLECTUAL property infringement, DETECTION algorithms

Entropy, Fidelity, and Entanglement During Digitized Adiabatic Quantum Computing to Form a Greenberger–Horne–Zeilinger (GHZ) State.

Autori: Jansen, Nathan D. Hunt, Katharine L. C.

Zdroj: Entropy; Sep2025, Vol. 27 Issue 9, p891, 21p

Predmety: ENTROPY, QUANTUM entanglement, STATISTICAL accuracy, HAMILTONIAN mechanics, ISING model, QUANTUM computing, QUANTUM computers

Korporácia: INTERNATIONAL Business Machines Corp.

A Firm's Operational Risk: Data Set and Empirical Evidence.

Autori: Astvansh, Vivek Simpson, Joseph J.

Zdroj: Manufacturing & Service Operations Management (M&SOM); Jan/Feb2026, Vol. 28 Issue 1, p326-341, 16p

Predmety: OPERATIONAL risk, TRANSFORMER models, STAKEHOLDER theory, LEGAL liability, FINANCIAL disclosure, EMPIRICAL research, NUMERICAL analysis

Geografický termín: UNITED States

An optimization method for klystron magnetic focusing system.

Autori: Wang, Yiao Zhou, Zusheng Xiao, Ouzheng a ďalší

Zdroj: Radiation Detection Technology & Methods; Dec2025, Vol. 9 Issue 4, p662-671, 10p

HABiC: an algorithm based on the exact computation of the Kantorovich-Rubinstein optimizer for binary classification in transcriptomics.

Autori: Cordier, Chiara Jézéquel, Pascal Campone, Mario a ďalší

Zdroj: Bioinformatics; Jun2025, Vol. 41 Issue 6, p1-13, 13p

Predmety: PREDICTION algorithms, TRANSCRIPTOMES, DEGREES of freedom, INDIVIDUALIZED medicine, MACHINE learning

Uncertainty quantification for severe-accident reactor modelling: Results and conclusions of the MUSA reactor applications work package

Autori: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. a ďalší

Prispievatelia: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. a ďalší

Zdroj: ISSN: 0306-4549 ; Annals of Nuclear Energy ; https://hal.science/hal-04777159 ; Annals of Nuclear Energy, 2025, 211, pp.110962. ⟨10.1016/j.anucene.2024.110962⟩.

Predmety: Nuclear reactor, Severe accident, Modelling, Uncertainty quantification, MUSA, Source Term, [PHYS]Physics [physics], [SPI]Engineering Sciences [physics], [STAT]Statistics [stat]

Relation: info:eu-repo/grantAgreement//847441/EU/Euratom research and training programme 2014 – 2018/Euratom

Dostupnosť: https://hal.science/hal-04777159
https://hal.science/hal-04777159v1/document
https://hal.science/hal-04777159v1/file/Final_version_ANE20225.pdf
https://doi.org/10.1016/j.anucene.2024.110962

Extended sample size calculations for evaluation of prediction models using a threshold for classification.

Autori: Whittle, Rebecca Ensor, Joie Archer, Lucinda a ďalší

Zdroj: BMC Medical Research Methodology; 7/1/2025, Vol. 25 Issue 1, p1-12, 12p

Predmety: SAMPLE size (Statistics), PREDICTION models, RESEARCH personnel, MODEL validation, CALIBRATION

CQLLM: A Framework for Generating CodeQL Security Vulnerability Detection Code Based on Large Language Model.

Autori: Wang, Le Chen, Chan Zhu, Junyi a ďalší

Zdroj: Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 1, p517, 17p

Predmety: COMPUTER security vulnerabilities, LANGUAGE models, COMPUTER software correctness, SOFTWARE frameworks, PENETRATION testing (Computer security), CODE generators

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