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Showing 181 - 200 results of 297 for search '((((python model) OR (python code))) OR (python tool)) implementing', query time: 0.25s Refine Results
  1. 181

    Examples of tweets texts (English). by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
  2. 182

    Users information. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
  3. 183

    The Improved Hydro-Sediment Numerical Model and Machine Learning Models by Yuning Tan (20580932)

    Published 2025
    “…The hydro-sediment model was implemented in the C# programming language using Visual Studio, while the machine learning models were developed in Python.…”
  4. 184

    Data Sheet 1_COCαDA - a fast and scalable algorithm for interatomic contact detection in proteins using Cα distance matrices.pdf by Rafael Pereira Lemos (9104911)

    Published 2025
    “…Here, we introduce COCαDA (COntact search pruning by Cα Distance Analysis), a Python-based command-line tool for improving search pruning in large-scale interatomic protein contact analysis using alpha-carbon (Cα) distance matrices. …”
  5. 185

    Advancing Solar Magnetic Field Modeling by Carlos António (21257432)

    Published 2025
    “…<br><br>We developed a significantly faster Python code built upon a functional optimization framework previously proposed and implemented by our team. …”
  6. 186
  7. 187

    Performance Benchmark: SBMLNetwork vs. SBMLDiagrams Auto-layout. by Adel Heydarabadipour (22290905)

    Published 2025
    “…<p>Log–log plot of median wall-clock time for SBMLNetwork’s C++-based auto-layout engine (blue circles, solid fit) and SBMLDiagrams’ implementation of the pure-Python NetworkX spring_layout algorithm (red squares, dashed fit), applied to synthetic SBML models containing 20–2,000 species, with a fixed 4:1 species-to-reaction ratio. …”
  8. 188

    Linking Thermal Conductivity to Equations of State Using the Residual Entropy Scaling Theory by Zhuo Li (165589)

    Published 2024
    “…Besides, a detailed examination of the impact of the critical enhancement term on mixture calculations was conducted. To use our model easily, a software package written in Python is provided in the Supporting Information.…”
  9. 189

    HCC Evaluation Dataset and Results by Jens-Rene Giesen (18461928)

    Published 2024
    “…The only requirement for running this script is a Python 3.6+ interpreter as well as an installation of the <code>numpy</code> package. …”
  10. 190

    Overview of deep learning terminology. by Aaron E. Maxwell (8840882)

    Published 2024
    “…This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. …”
  11. 191
  12. 192

    Compiled Global Dataset on Digital Business Model Research by Dimas Fauzan Aryadefa (22123186)

    Published 2025
    “…</p><p dir="ltr">For the modeling component, annual publication growth is projected from 2025–2034 using a logistic growth model (S-curve) implemented in Python. …”
  13. 193

    Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model by Marina Diachenko (19739092)

    Published 2025
    “…<p dir="ltr">The <i>zip</i> file contains the code for the functional excitation-inhibition ratio (fE/I) and theta-gamma (θ-γ) phase-amplitude coupling (PAC) analyses described in the paper titled "<b>Hippocampal and cortical activity reflect early </b><b>hyperexcitability</b><b> in an Alzheimer's mouse model</b>" submitted to <i>Brain Communications</i> in April 2025.…”
  14. 194

    Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models by Pascal Kündig (19824557)

    Published 2024
    “…In particular, we obtain a speed-up of an order of magnitude compared to Cholesky-based calculations and a 3-fold increase in prediction accuracy in terms of the continuous ranked probability score compared to a state-of-the-art method on a large satellite dataset. All methods are implemented in a free C++ software library with high-level Python and R packages. …”
  15. 195

    Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil) by Amanda Bueno de Moraes (22559249)

    Published 2025
    “…The code implements a statistical–computational workflow for parameter selection (VIF, Bartlett and KMO tests, PCA and FA with <i>varimax</i>) and then trains and evaluates machine-learning models to predict three key physico-chemical indicators: turbidity, true color, and total iron. …”
  16. 196

    Neural-Signal Tokenization and Real-Time Contextual Foundation Modelling for Sovereign-Scale AGI Systems by Lakshit Mathur (20894549)

    Published 2025
    “…The work advances national AI autonomy, real-time cognitive context modeling, and ethical human-AI integration.</p><p dir="ltr"><b>Availability</b> — The repository includes LaTeX sources, trained model checkpoints, Python/PyTorch code, and synthetic datasets. …”
  17. 197

    face recognation with Flask by Muammar, SST, M.Kom (21435692)

    Published 2025
    “…</li><li><b>Face Recognition Engine:</b> Compares detected faces to known faces using deep learning models (e.g., <code>face_recognition</code>, based on dlib’s ResNet).…”
  18. 198
  19. 199

    Supplementary file 1_ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.docx by Piyachat Udomwong (22563212)

    Published 2025
    “…The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.…”
  20. 200

    Image 1_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif by Xiaobing Li (291454)

    Published 2025
    “…LR, SVM, and AdaBoost algorithms were implemented using Python to build the models. In the training set, the accuracies of the LR, SVM, and AdaBoost models were 79.4, 84.0, and 80.9%, respectively; the sensitivities were 74.1, 74.1, and 81.0%, respectively; the specificities were 83.6, 91.8, and 80.8%, respectively; and the AUC values were 0.837, 0.886, and 0.900, respectively. …”