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model implementation » modular implementation (Expand Search), world implementation (Expand Search), time implementation (Expand Search)
tool implementation » world implementation (Expand Search), proof implementation (Expand Search), time implementation (Expand Search)
python model » python code (Expand Search), action model (Expand Search), motion model (Expand Search)
model implementation » modular implementation (Expand Search), world implementation (Expand Search), time implementation (Expand Search)
tool implementation » world implementation (Expand Search), proof implementation (Expand Search), time implementation (Expand Search)
python model » python code (Expand Search), action model (Expand Search), motion model (Expand Search)
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141
Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil)
Published 2025“…<p dir="ltr">This deposit provides the Python notebook and the input dataset used in the study “Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil).” …”
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142
Neural-Signal Tokenization and Real-Time Contextual Foundation Modelling for Sovereign-Scale AGI Systems
Published 2025“…The work advances national AI autonomy, real-time cognitive context modeling, and ethical human-AI integration.</p><p dir="ltr"><b>Availability</b> — The repository includes LaTeX sources, trained model checkpoints, Python/PyTorch code, and synthetic datasets. …”
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143
face recognation with Flask
Published 2025“…Built using the <b>Flask</b> web framework (Python), this system provides a lightweight and scalable solution for implementing facial recognition capabilities in real-time or on-demand through a browser interface.…”
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144
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145
Image 1_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif
Published 2025“…LR, SVM, and AdaBoost algorithms were implemented using Python to build the models. In the training set, the accuracies of the LR, SVM, and AdaBoost models were 79.4, 84.0, and 80.9%, respectively; the sensitivities were 74.1, 74.1, and 81.0%, respectively; the specificities were 83.6, 91.8, and 80.8%, respectively; and the AUC values were 0.837, 0.886, and 0.900, respectively. …”
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146
Image 2_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif
Published 2025“…LR, SVM, and AdaBoost algorithms were implemented using Python to build the models. In the training set, the accuracies of the LR, SVM, and AdaBoost models were 79.4, 84.0, and 80.9%, respectively; the sensitivities were 74.1, 74.1, and 81.0%, respectively; the specificities were 83.6, 91.8, and 80.8%, respectively; and the AUC values were 0.837, 0.886, and 0.900, respectively. …”
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147
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148
Supplementary file 1_ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.docx
Published 2025“…The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.…”
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149
Collaborative Research: Framework: Improving the Understanding and Representation of Atmospheric Gravity Waves using High-Resolution Observations and Machine Learning
Published 2025“…Establishing a framework for implementing and testing ML-based parameterizations in atmospheric models. …”
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150
Missing Value Imputation in Relational Data Using Variational Inference
Published 2025“…Additional results, implementation details, a Python implementation, and the code reproducing the results are available online. …”
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151
Parallel Sampling of Decomposable Graphs Using Markov Chains on Junction Trees
Published 2024“…We find that our parallel sampler yields improved mixing properties in comparison to the single-move variate, and outperforms current state-of-the-art methods in terms of accuracy and computational efficiency. The implementation of our work is available in the Python package parallelDG. …”
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152
Comparison data 7 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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153
Sample data for <i>Neolamprologus multifasciatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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154
Sample data for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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155
Comparison data 3 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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156
Sample data for <i>Telmatochromis temporalis</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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157
Comparison data 4 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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158
Comparison data 1 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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159
Comparison data 2 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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160
Comparison data 5 for <i>Lamprologus ocellatus</i>.
Published 2024“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”