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Showing 121 - 140 results of 286 for search '(( python model implementation ) OR ( ((python model) OR (python code)) implementation ))', query time: 0.30s Refine Results
  1. 121
    Descriptive measures of the dataset.

    Descriptive measures of the dataset. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  2. 122
    Corpora from the articles in order of size.

    Corpora from the articles in order of size. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  3. 123
    Media information.

    Media information. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  4. 124
    Information from interactions.

    Information from interactions. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  5. 125
    Table of the database statistical measures.

    Table of the database statistical measures. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  6. 126
    Tweets information.

    Tweets information. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  7. 127
    Examples of tweets texts (Portuguese).

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

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  8. 128
    Methodological flowchart.

    Methodological flowchart. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  9. 129
    Number of tweets collected per query and type.

    Number of tweets collected per query and type. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  10. 130
    Examples of tweets texts (English).

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

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  11. 131
    Users information.

    Users information. by Sylvia Iasulaitis (8301189)

    Published 2025
    “…Python algorithms were developed to model each primary collection type. …”
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  12. 132
    The Improved Hydro-Sediment Numerical Model and Machine Learning Models

    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.…”
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  13. 133
    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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  14. 134
    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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  15. 135
    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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  16. 136
    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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  17. 137
    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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  18. 138
    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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  19. 139
    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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  20. 140
    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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