Search alternatives:
"python code implementing" » "python consider implementing" (Expand Search), "python after implementing" (Expand Search), "python _ implementing" (Expand Search)
"python study presented" » "python study presents" (Expand Search), "python study represents" (Expand Search), "method study presented" (Expand Search)
Showing 1 - 11 results of 11 for search '(("python study presented") OR ("python code implementing"))', query time: 0.27s Refine Results
  1. 1
    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
    Subjects:
    Save to List
    Saved in:
  2. 2
    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
    Subjects:
    Save to List
    Saved in:
  3. 3
    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
    Subjects:
    Save to List
    Saved in:
  4. 4
    Comparative analysis of WordNet replacement impact on data distribution.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  5. 5
    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
    Subjects:
    Save to List
    Saved in:
  6. 6
    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
    Subjects:
    Save to List
    Saved in:
  7. 7
    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
    Subjects:
    Save to List
    Saved in:
  8. 8
    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
    Subjects:
    Save to List
    Saved in:
  9. 9
    Human Mobility Pattern Mining

    Human Mobility Pattern Mining by Hao Yang (17966435)

    Published 2024
    “…</p><p dir="ltr">I_LCS python file: This Python code implements the I_LCS algorithm, designed to estimate the similarity between two sequences.…”
    Save to List
    Saved in:
  10. 10
    Connecting a T1DM closed-loop algorithm written in python code to the development platform though the nexus communicator plugin.

    Connecting a T1DM closed-loop algorithm written in python code to the development platform though the nexus communicator plugin. by Mohamed Raef Smaoui (9865830)

    Published 2020
    “…<p>The Python code implements an interface class to connect to the Platform. …”
    Save to List
    Saved in:
  11. 11
    Monte Carlo Simulation Code for Evaluating Cognitive Biases in Penalty Shootouts Using ABAB and ABBA Formats

    Monte Carlo Simulation Code for Evaluating Cognitive Biases in Penalty Shootouts Using ABAB and ABBA Formats by Raul MATSUSHITA (10276562)

    Published 2024
    “…<p dir="ltr">This Python code implements a Monte Carlo simulation to evaluate the impact of cognitive biases on penalty shootouts under two formats: ABAB (alternating shots) and ABBA (similar to tennis tiebreak format). …”
    Save to List
    Saved in:

Search Tools: