Suchergebnisse - (( (statu OR stat) python code analysis ) OR ( (staten OR staten) python code analysis ))~

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

Autoren: National Cancer Institute (NCI)

Quelle: 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

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

Yusuf Suresinin Anlam İlişkileri ve Hikâye Birleşenlerinin Pyhton Kodlu Matematiksel Görselleştirmelerle Tahlili. (Turkish)

Alternate Title: Analysis of the Meaning Relations and Story Components of Surah Yusuf Via Python Coded Mathematical Visualizations. (English)

Autoren: Yücel, Doğan Er, Ülkü Aysel, Metin et al.

Quelle: International Journal of Social Sciences (IJSS) / Uluslararası Sosyal Bilimler Dergisi; 2025, Vol. 9 Issue 41, p1-42, 42p

Schlagwörter: TIME series analysis, THEMATIC maps, GRAPH theory, PYTHON programming language, QUR'ANIC criticism

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

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

Autoren: Zheng, Tianming Meng, Fanchao Yi, Ping et al.

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

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

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

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

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

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

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

Autoren: Astvansh, Vivek Simpson, Joseph J.

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

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

Geografische Kategorien: UNITED States

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

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

Autoren: Wang, Le Chen, Chan Zhu, Junyi et al.

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

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

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

The Computational Content Analyst : Using Machine Learning to Classify Media Messages

Autoren: Chris J. Vargo

Resource Type: eBook.

Schlagworte: Machine learning

Categories: LANGUAGE ARTS & DISCIPLINES / Communication Studies, SOCIAL SCIENCE / Methodology, SOCIAL SCIENCE / Media Studies

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.

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

Understanding AI Agents and Agentic AI: Concepts, Tools, and Implementation with SmolAgents.

Autoren: Lee, Wei-Meng

Quelle: CODE Magazine; Jan/Feb2026, Vol. 27 Issue 1, p28-39, 12p, 3 Color Photographs

Schlagwörter: INTELLIGENT agents, AUTONOMOUS robots

Mathematical Model of the Software Development Process with Hybrid Management Elements.

Autoren: Semenov, Serhii Tsukur, Volodymyr Molokanova, Valentina et al.

Quelle: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 21, p11667, 44p

Schlagwörter: COMPUTER software development, AGILE software development, TELEMETRY, PREDICTION models, INTERDISCIPLINARY research, EPISTEMIC uncertainty, RISK assessment

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

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

Autoren: Cordier, Chiara Jézéquel, Pascal Campone, Mario et al.

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

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

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

Autoren: Whittle, Rebecca Ensor, Joie Archer, Lucinda et al.

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

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

JDroid: Android malware detection using hybrid opcode feature vector.

Autoren: Arslan, Recep Sinan

Quelle: PeerJ Computer Science; Jul2025, p1-30, 30p

Schlagwörter: MALWARE, MACHINE learning, DATA analytics, INFORMATION technology security, ENSEMBLE learning

Reviews & Products: ANDROID (Operating system)