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python model » python tool (Expand Search), action model (Expand Search), motion model (Expand Search)
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The ROC, calibration curve and DCA of the predictive model based on enhanced CT.
Published 2025Subjects: -
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FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair
Published 2025“…<p dir="ltr">This is the replication package associated with the paper: 'FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test for Code Repair'</p><p><br></p><p dir="ltr">### Requirements</p><p dir="ltr">This is a list of all required python packages:</p><p dir="ltr">-imbalanced_learn==0.8.1</p><p dir="ltr">-numpy==1.19.5</p><p dir="ltr">-pandas==1.3.3</p><p dir="ltr">-transformers==4.10.2</p><p dir="ltr">-torch==1.5.0</p><p dir="ltr">-scikit_learn==0.24.2</p><p dir="ltr">-openai==v0.28.1</p><p><br></p><p dir="ltr">#Automated tool for labelling dataset with flaky test fix categories</p><p><br></p><p dir="ltr">This is a step-by-step guideline for automatically labelling dataset with flaky test fix categories</p><p><br></p><p><br></p><p dir="ltr">### Input Files:</p><p dir="ltr">This is a an input file that is required to accomplish this step:</p><p dir="ltr">* Data/IdoFT_dataset_filtered.csv</p><p dir="ltr">https://figshare.com/s/47f0fb6207ac3f9e2351</p><p><br></p><p dir="ltr">### Output Files:</p><p dir="ltr">* Results/IdoFT_dataset_filtered.csv</p><p><br></p><p><br></p><p dir="ltr">### Replicating the experiment</p><p><br></p><p dir="ltr">This experiment can be executed using the following commands after navigating to the `Code\` folder:</p><p><br></p><p dir="ltr">```console</p><p dir="ltr">bash Automated_labelling_tool.sh</p><p>```</p><p><br></p><p dir="ltr">It will generate the dataset required to run our prediction models to predict the category of the fix, given a flaky test code</p><p><br></p><p>---</p><p><br></p><p dir="ltr"># Prediction models for fix categories using the test case code</p><p><br></p><p dir="ltr">This is the guideline for replicating the experiments we used to evaluate our prediction models i.e. …”
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Python-Based Algorithm for Calculating Physical Properties of Aqueous Mixtures Composed of Substances Not Available in Databases
Published 2025“…In this study, we developed a Python-based open-source algorithm compatible with the aqueous physical property models provided in the electrolyte templates of AspenTech software. …”
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Python-Based Algorithm for Calculating Physical Properties of Aqueous Mixtures Composed of Substances Not Available in Databases
Published 2025“…In this study, we developed a Python-based open-source algorithm compatible with the aqueous physical property models provided in the electrolyte templates of AspenTech software. …”
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Code for the HIVE Appendicitis prediction modelRepository with LLM_data_extractor_optuna for automated feature extraction
Published 2025“…</p><p dir="ltr"><b>LLM Data Extractor optuna repo</b> is a Python framework for generating and evaluating clinical text predictions using large language models (LLMs) like <code>qwen2.5</code>. …”
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The ROC calibration curve and DCA of the combined prediction model based on CT and MRI.
Published 2025Subjects: -
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Comparison of previous predictive models.
Published 2024“…<div><p>Background</p><p>Optic neuritis (ON) can be an initial clinical presentation of multiple sclerosis This study aims to provide a practical predictive model for identifying at-risk ON patients in developing MS.…”
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The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation"
Published 2025“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”
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The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation"
Published 2025“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”