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modular implementation » model implementation (Expand Search), world implementation (Expand Search)
time implementation » _ implementation (Expand Search), policy implementation (Expand Search), effective implementation (Expand Search)
python time » python files (Expand Search)
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BSTPP: a python package for Bayesian spatiotemporal point processes
Published 2025“…<p>Spatiotemporal point process models have a rich history of effectively modeling event data in space and time. However, they are sometimes neglected due to the difficulty of implementing them. …”
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Multi-Version PYZ Builder Script: A Universal Python Module Creation Tool
Published 2024“…Once the protected .pyc files are prepared, the script bundles them into a single .pyz archive.The script requires Python 3.6 or higher, and the following Python packages:</p><ul><li>requests</li><li>psutil</li><li>cryptography</li><li>astor</li></ul><p dir="ltr"><b>Recommendations and Best Practices</b></p><ul><li><b>Enhance Protection with Multiple Layers</b>: Apply the <b>Local Python Code Protector</b> multiple times to each .pyc file before bundling them. …”
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Five Operator Lattice Simulation
Published 2025“…</p><p dir="ltr">Running the included file <code>five_operator_lattice_sim.py</code> (Python 3.14 + NumPy 2.1) reproduces the dynamic interactions and figures reported in Appendix A of the paper, generating time-series data that demonstrate operator balance, instability, and renewal cycles.…”
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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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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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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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BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories
Published 2025“…Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. …”
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BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories
Published 2025“…Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. …”