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Showing 61 - 80 results of 388 for search '(( ((python model) OR (python code)) implementing ) OR ( python model represent ))', query time: 0.50s Refine Results
  1. 61
    The format of the weights file

    The format of the weights file by Joseph James Tharayil (21416715)

    Published 2025
    “…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”
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  2. 62
    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’), followed by a word frequency count (‘Counter’).

    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... by Enock Niyonkuru (21246879)

    Published 2025
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  3. 63
    Schematic of the approach: This schematic illustrates the entire workflow of the project.

    Schematic of the approach: This schematic illustrates the entire workflow of the project. by Enock Niyonkuru (21246879)

    Published 2025
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  4. 64
    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739).

    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

    Published 2025
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  5. 65
    Comparative analysis of WordNet replacement impact on data distribution.

    Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

    Published 2025
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  6. 66
    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10, this Table shows the results for three different window sizes at a =. Abbreviations are the same as for Table 1.

    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... by Enock Niyonkuru (21246879)

    Published 2025
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  7. 67
    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet.

    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

    Published 2025
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  8. 68
    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,918. The Table summarizes results for different thresholds () and categories of concept/gene sets (M,B,K,G,P). Columns: : Replacement threshold; replaced: Unique Replaced Concepts; Category: M = MeSH, B = Biocarta, K = KEGG, G = GP(bp), P = PID; # sets: Number of concept/gene sets in the categories; #Concepts: number of concept vectors in the category; <i>WN better</i><i>: The count and percentage of concept/gene sets for which the mean interconcept distance was smaller for WN than for PM. “Winners” are shown in bold.;</i> <i>PM better</i><i>: Analogous to “WN better” but for PM.</i>

    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

    Published 2025
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  9. 69
    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of concept sets where the cluster mean distance is significantly lower, indicative of superior embeddings.

    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

    Published 2025
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  10. 70
    Segmentation module training details.

    Segmentation module training details. by Khelina Fedorchuk (19130089)

    Published 2025
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  11. 71
    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission

    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission by Monika Salesh (15306281)

    Published 2025
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  12. 72
    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission

    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission by Monika Salesh (15306281)

    Published 2025
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  13. 73
    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission

    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission by Monika Salesh (15306281)

    Published 2025
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  14. 74
    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission

    Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission by Monika Salesh (15306281)

    Published 2025
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  15. 75
    Resolving Harvesting Errors in Institutional Repository Migration : Using Python Scripts with VS Code and LLM Integration.

    Resolving Harvesting Errors in Institutional Repository Migration : Using Python Scripts with VS Code and LLM Integration. by satoshi hashimoto(橋本 郷史) (18851272)

    Published 2025
    “…Therefore, we decided to create a dedicated Python program using Large Language Model (LLM)-assisted coding.…”
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  16. 76
    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, the temporal molecular states <i>n</i> at specific time <i>t</i> will be generated for further visualization.

    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

    Published 2025
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  17. 77
    Data&Codes.zip

    Data&Codes.zip by Zer0 Star (20545655)

    Published 2025
    “…</p><p dir="ltr">To facilitate the widespread use of the proposed framework, we have implemented it as the <b><i>ESLocalIndi</i></b> open-source package in Python, making it easily accessible to geographers. …”
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  18. 78
    BSTPP: a python package for Bayesian spatiotemporal point processes

    BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

    Published 2025
    “…However, they are sometimes neglected due to the difficulty of implementing them. There is a lack of packages with the ability to perform inference for these models, particularly in python. …”
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  19. 79
    City-level GDP estimates for China under alternative pathways from 2020 to 2100-python code

    City-level GDP estimates for China under alternative pathways from 2020 to 2100-python code by Jinjie Sun (11791715)

    Published 2025
    “…The dataset is complemented by processing code and raw input data in the "Python_Code" directory to ensure full reproducibility. …”
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  20. 80
    Configurations used in ALAAMs for undirected networks.

    Configurations used in ALAAMs for undirected networks. by Alex Stivala (8356257)

    Published 2024
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