Media information. by Sylvia Iasulaitis (8301189)

“…Python algorithms were developed to model each primary collection type. …”

Information from interactions. by Sylvia Iasulaitis (8301189)

“…Python algorithms were developed to model each primary collection type. …”

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

“…Python algorithms were developed to model each primary collection type. …”

Tweets information. by Sylvia Iasulaitis (8301189)

“…Python algorithms were developed to model each primary collection type. …”

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

“…Python algorithms were developed to model each primary collection type. …”

Methodological flowchart. by Sylvia Iasulaitis (8301189)

“…Python algorithms were developed to model each primary collection type. …”

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

“…Python algorithms were developed to model each primary collection type. …”

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

“…Python algorithms were developed to model each primary collection type. …”

Users information. by Sylvia Iasulaitis (8301189)

“…Python algorithms were developed to model each primary collection type. …”

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

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

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)

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

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

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

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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)

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

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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)

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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)

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The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network" by Chongshan Wan (19247614)

“…</p><h3><b>Model training</b></h3><h4><code>python train_GTN.py</code></h4><p dir="ltr">This step trains the GTN model. …”

<b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b> by Pajtim Zariqi (22155799)

“…</li><li>Python/Kaggle notebooks (<code>.ipynb</code>): reproducibility pipeline for accuracy metrics and statistical analysis.…”