Suchergebnisse - (( (stateneen OR stat) python code analysis ) OR ( stateee python code analysis ))~

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

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

Autoren: Dunker, Fabian Mendoza, Emil Reale, Marco

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

Schlagwörter: 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.

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

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

Autoren: Griffiths, JR Lee, Shen-Han McIntyre, Dominick John et al.

Index Begriffe: 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

Python Programming: Numerical Analysis/Machine Learning Series.

Autoren: Doskas, Constantinos

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

Schlagwörter: PYTHON programming language, NUMERICAL analysis, MACHINE learning

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

Autoren: Brooke, Siân

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

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

Automation of Software Code Analysis Using Machine Learning Methods.

Autoren: Moshkin, V. S. Dyrnochkin, A. A. Yarushkina, N. G.

Quelle: Pattern Recognition & Image Analysis; Sep2023, Vol. 33 Issue 3, p417-424, 8p

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

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

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

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

Firma/Körperschaft: INTERNATIONAL Business Machines Corp.

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions.

Autoren: Stoica, Alexandru Stefan Babiceanu, Daniel Mihaescu, Marian Cristian et al.

Quelle: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3589, 19p

Schlagwörter: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code

An extensive study of the effects of different deep learning models on code vulnerability detection in Python code.

Autoren: Wang, Rongcun Xu, Senlei Ji, Xingyu et al.

Quelle: Automated Software Engineering; Jun2024, Vol. 31 Issue 1, p1-35, 35p

Time Series Analysis with Python Cookbook : Practical Recipes for the Complete Time Series Workflow, From Modern Data Engineering to Advanced Forecasting and Anomaly Detection

Autoren: Tarek A. Atwan

Resource Type: eBook.

Categories: COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / General, COMPUTERS / Data Science / Data Analytics

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

Autoren: Li, Jingmao Xu, Yaqing Ma, Shuangge et al.

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

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

An optimization method for klystron magnetic focusing system.

Autoren: Wang, Yiao Zhou, Zusheng Xiao, Ouzheng et al.

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

DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 Generate Correct Code for LoRaWAN-Related Engineering Tasks.

Autoren: Fernandes, Daniel Matos-Carvalho, João P. Fernandes, Carlos M. et al.

Quelle: Electronics (2079-9292); Apr2025, Vol. 14 Issue 7, p1428, 24p

Schlagwörter: LANGUAGE models, GENERATIVE pre-trained transformers, GENERATING functions, RESEARCH questions, NATURAL languages

Python Programming Log Analysis Series.

Autoren: Doskas, Constantinos

Quelle: ISSA Journal; Oct2021, Vol. 19 Issue 10, p20-23, 4p

Schlagwörter: PYTHON programming language, SAWLOGS, RECORDS management, INFORMATION assurance, DATABASES, TEXT files

A comparison of machine learning algorithms for survival with missing data

Autoren: Dufossé, Paul Benzekry, Sébastien Benzekry, Sebastien

Weitere Verfasser: Dufossé, Paul Benzekry, Sébastien Benzekry, Sebastien

Schlagwörter: survival data, missing data, [STAT.AP] Statistics [stat]/Applications [stat.AP], Machine learning, Machine Learning (stat.ML), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], simulated data, neural networks, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04384220v1
https://doi.org/10.48550/arxiv.2303.13590
https://hal.science/hal-04384220v1/document

Compound-SNE: comparative alignment of t-SNEs for multiple single-cell omics data visualization.

Autoren: Cess, Colin G Haghverdi, Laleh

Quelle: Bioinformatics; Jul2024, Vol. 40 Issue 7, p1-5, 5p

Schlagwörter: RESEARCH personnel, DATA visualization, GENE expression, DATA analysis, PYTHON programming language

Towards the genome-scale discovery of bivariate monotonic classifiers.

Autoren: Fourquet, Océane Krejca, Martin S. Doerr, Carola et al.

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

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

Evaluating Large Language Models in Code Generation: INFINITE Methodology for Defining the Inference Index.

Autoren: Christakis, Nicholas Drikakis, Dimitris

Quelle: Applied Sciences (2076-3417); Apr2025, Vol. 15 Issue 7, p3784, 24p

Schlagwörter: LANGUAGE models, LONG short-term memory, ARTIFICIAL intelligence, GENERATIVE pre-trained transformers, WIND speed

ICAROGW: A python package for inference of astrophysical population properties of noisy, heterogeneous, and incomplete observations.

Autoren: Mastrogiovanni, Simone Pierra, Grégoire Perriès, Stéphane et al.

Quelle: Astronomy & Astrophysics / Astronomie et Astrophysique; 1/27/2024, Vol. 682, p1-16, 16p

Schlagwörter: PYTHON programming language, GENERAL relativity (Physics), GRAVITATIONAL waves, REDSHIFT