Suchergebnisse - (( (statu OR stat) python code analysis ) OR ( (stavebnit OR stateeeen) 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

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

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

Autoren: Yibo Wang Ying Wang Yue Yu et al.

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

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

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

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

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

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

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

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

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

Autoren: Astvansh, Vivek Simpson, Joseph J.

Quelle: Manufacturing & Service Operations Management (M&SOM) (INFORMS); 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

Reviewing seas of data: Integrating image‐based bio‐logging and artificial intelligence to enhance marine conservation.

Autoren: Chimienti, Marianna Kato, Akiko Seydi, Vahid et al.

Quelle: Methods in Ecology & Evolution; Feb2026, Vol. 17 Issue 2, p272-290, 19p

Schlagwörter: ARTIFICIAL intelligence, COMPUTER vision, ANIMAL tagging, ENVIRONMENTAL sciences, COOPERATION, IMAGE processing, MARINE resources conservation, DATA integration

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)

Distinguishing LLM-Generated from Human-Written Code by Contrastive Learning.

Autoren: Xu, Xiaodan Ni, Chao Guo, Xinrong et al.

Quelle: ACM Transactions on Software Engineering & Methodology; May2025, Vol. 34 Issue 4, p1-31, 31p

Schlagwörter: CHATGPT, LANGUAGE models, CODE generators, MACHINE learning

RisingTides : An Analytic Modeling Code of Tidal Effects in Binary Neutron Star Mergers.

Autoren: O'Dell, Alexander Hamilton, Maria C. Babiuc

Quelle: Astronomy (2674-0346); Sep2024, Vol. 3 Issue 3, p167-188, 22p

Schlagwörter: NEUTRONS, GRAVITATIONAL waves, PYTHONS, WAVE analysis, EQUATIONS

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant.

Autoren: Demidova, Liliya A. Andrianova, Elena G. Sovietov, Peter N. et al.

Quelle: Data (2306-5729); Jun2023, Vol. 8 Issue 6, p109, 16p

Schlagwörter: TEACHERS' assistants, PYTHON programming language, CLASSIFICATION algorithms, HIERARCHICAL clustering (Cluster analysis), MARKOV processes, SOURCE code, ALGORITHMS

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

Autoren: Giovanna Zimatore Cassandra Serantoni Maria Chiara Gallotta et al.

Quelle: Applied Sciences, Vol 14, Iss 20, p 9216 (2024)

Schlagwörter: aerobic transition, heart rate variability, nonlinear time series analysis, python code, athletes, obesity, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/20/9216; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/f7b3f6a6e6a84bba9ef80e21fe47cacc

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

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

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