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code implementation » model implementation (Expand Search), world implementation (Expand Search), _ implementation (Expand Search)
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code representing » model representing (Expand Search), models representing (Expand Search), tpd representing (Expand Search)
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Testing Code for JcvPCA and JsvCRP.
Published 2025“…<p>This file contains the code that implements both metrics in python and apply them on a simulated dataset.…”
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103
Data and code for: Automatic fish scale analysis
Published 2025“…</p><h3>Includeed in this repository:</h3><ul><li><b>Raw data files:</b></li><li><code>comparison_all_scales.csv</code> – comparison_all_scales.csv - manually verified vs. automated measurements of 1095 coregonid scales</li></ul><ul><li><ul><li><code>Validation_data.csv</code> – manually measured scale data under binocular</li><li><code>Parameter_correction_numeric.csv</code> – calibration data (scale radius vs. fish length/weight)</li></ul></li><li><b>Statistical results:</b></li><li><ul><li><code>comparison_stats_core_variables.csv</code> – verification statistics (bias, relative error, limits of agreement)</li><li><code>Validation_statistics.csv</code> – summary statistics and model fits (manual vs. automated)</li></ul></li><li><b>Executable script (not GUI):</b></li><li><ul><li><code>Algorithm.py</code> – core processing module for scale feature extraction<br>→ <i>Note: The complete Coregon Analyzer application (incl. …”
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104
Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations
Published 2025“…We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. …”
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105
Code and data for reproducing the results in the original paper of DML-Geo
Published 2025“…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”
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106
Data for "A hollow fiber membrane permeance evaluation device demonstrating outside-in and inside-out performance differences"
Published 2025“…</li><li>Plot data derived from the above data sources.</li><li>Python code to generate figures from the plot data.…”
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107
Data sets and coding scripts for research on sensory processing in ADHD and ASD
Published 2025“…The repository includes raw and matched datasets, analysis outputs, and the full Python code used for the matching pipeline.</p><h4>Ethics and Approval</h4><p dir="ltr">All procedures were approved by the University of Sheffield Department of Psychology Ethics Committee (Ref: 046476). …”
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108
Graphical abstract of HCAP.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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109
Recall analysis.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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110
Convergence rate analysis.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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Computational efficiency.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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Analysis of IoMT data sources.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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Prediction accuracy on varying attack types.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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<b> </b> Precision analysis.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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Impact of cyberattack types on IoMT devices.
Published 2025“…The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. …”
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116
Code for High-quality Human Activity Intensity Maps in China from 2000-2020
Published 2025“…<p dir="ltr">Code and remote sensing images and interpretation results of the samples for uncertainty analysis for "High-quality Human Activity Intensity Maps in China from 2000-2020"</p><p dir="ltr">“Mapping_HAI.py”:We generated the HAI maps using ArcGIS 10.8, and the geoprocessing tasks were implemented using Python 2.7 with the ArcPy library (ArcGIS 10.8 + Python 2.7 environment). …”
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Computing speed and memory usage.
Published 2025“…<b>(b)</b> Physical memory consumption depending on simulated plane in radial and depth direction. Color coding same as in (a). Memory consumption was recorded as the maximum resident size during simulation monitored with the Python built-in module resource. …”
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The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network"
Published 2024“…Each lane includes 'geometry' and 'inter_id' attributes.</li></ul><h2>Codes</h2><p dir="ltr">This repository contains the following Python codes:</p><ul><li>`data_processing.py`: Contains the implementation of data processing and feature extraction. …”
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MATH_code : False Data Injection Attack Detection in Smart Grids based on Reservoir Computing
Published 2025“…</li><li><b>3_literature_analysis_and_mapping.ipynb</b><br>Contains the Python code used for executing the systematic mapping study (SMS), including automated processing of literature data and thematic clustering.…”