يعرض 1 - 20 نتائج من 27 نتيجة بحث عن '(( python _ implementing ) OR ( python model representing ))~', وقت الاستعلام: 0.37s تنقيح النتائج
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    Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions حسب Zahir Barahmand (18008947)

    منشور في 2025
    "…</p><p dir="ltr">This Python code was optimized and debugged using ChatGPT-4o to ensure implementation efficiency, accuracy, and clarity.…"
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    Cost functions implemented in Neuroptimus. حسب Máté Mohácsi (20469514)

    منشور في 2024
    "…We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. …"
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    PTPC-UHT bounce حسب David Parry (22169299)

    منشور في 2025
    "…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …"
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    Performance Benchmark: SBMLNetwork vs. SBMLDiagrams Auto-layout. حسب Adel Heydarabadipour (22290905)

    منشور في 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. …"
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    Advancing Solar Magnetic Field Modeling حسب Carlos António (21257432)

    منشور في 2025
    "…<br><br>We developed a significantly faster Python code built upon a functional optimization framework previously proposed and implemented by our team. …"
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    Overview of deep learning terminology. حسب Aaron E. Maxwell (8840882)

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

    منشور في 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.…"
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    <b>Altered cognitive processes shape tactile perception in autism.</b> (data) حسب Ourania Semelidou (19178362)

    منشور في 2025
    "…The perceptual decision-making setup was controlled by Bpod (Sanworks) through scripts in Python (PyBpod, https://pybpod.readthedocs.io/en/latest/). …"
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    Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 حسب Robert Zomer (12796235)

    منشور في 2025
    "…The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:</p><p>**************************************************************************************************************************</p><p dir="ltr">The Global Aridity Index (Global-AI) and Global Reference Evapo-Transpiration (Global-ET0) datasets provided in Version 3.1 of the Global Aridity Index and Potential Evapo-Transpiration (ET0) Database (Global-AI_PET_v3.x1) provide high-resolution (30 arc-seconds) global raster data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon implementation of the FAO-56 Penman-Monteith Reference Evapotranspiration (ET<sub>0</sub>) equation.…"
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    Overview of generalized weighted averages. حسب Nobuhito Manome (8882084)

    منشور في 2025
    "…<div><p>The multi-armed bandit (MAB) problem is a classical problem that models sequential decision-making under uncertainty in reinforcement learning. …"
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    Summary of Tourism Dataset. حسب Jing Zhang (23775)

    منشور في 2025
    "…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"
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    Segment-wise Spending Analysis. حسب Jing Zhang (23775)

    منشور في 2025
    "…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"