يعرض 41 - 60 نتائج من 143 نتيجة بحث عن '(( python proof implementation ) OR ( ((python time) OR (python files)) implementation ))', وقت الاستعلام: 0.35s تنقيح النتائج
  1. 41

    Graphical abstract of HCAP. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  2. 42

    Recall analysis. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  3. 43

    Convergence rate analysis. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  4. 44

    Computational efficiency. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  5. 45

    Analysis of IoMT data sources. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  6. 46

    Prediction accuracy on varying attack types. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  7. 47

    <b> </b> Precision analysis. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  8. 48

    Impact of cyberattack types on IoMT devices. حسب Mohanad Faeq Ali (21354273)

    منشور في 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. …"
  9. 49
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    Accompanying data files (Melbourne, Washington DC, Singapore, and NYC-Manhattan) حسب Winston Yap (13771969)

    منشور في 2025
    "…<p dir="ltr">Supporting files to implement GNN training for Melbourne, Singapore, Washington DC, and NYC-Manhattan. …"
  11. 51

    Workflow of a typical Epydemix run. حسب Nicolò Gozzi (8837522)

    منشور في 2025
    "…<div><p>We present Epydemix, an open-source Python package for the development and calibration of stochastic compartmental epidemic models. …"
  12. 52

    Single Cell DNA methylation data for Human Brain altas MajorType allc files (CG+CH) حسب Wubin Ding (11823941)

    منشور في 2025
    "…</p><p dir="ltr">PMID: 37824674</p><p dir="ltr"><br></p><p dir="ltr">How to download</p><p dir="ltr">To quickly download the whole folder, Python package pyfigshare can be implemented. please refer to pyfigshare documentation: https://github.com/DingWB/pyfigshare</p><p dir="ltr">for example: figshare download 28424780 -o downlnoaded_data</p>…"
  13. 53

    Single Cell DNA methylation data for Human Brain altas (MajorType+Region CG allc files) حسب Wubin Ding (11823941)

    منشور في 2025
    "…</p><p dir="ltr">PMID: 37824674</p><p><br></p><h2>How to download</h2><p dir="ltr">To quickly download the whole folder, Python package <a href="https://github.com/DingWB/pyfigshare" rel="noreferrer" target="_blank">pyfigshare</a> can be implemented. please refer to pyfigshare documentation: <a href="https://github.com/DingWB/pyfigshare" rel="noreferrer" target="_blank">https://github.com/DingWB/pyfigshare</a></p><p dir="ltr">for example: <code>figshare download 28424780 -o downlnoaded_data</code></p>…"
  14. 54

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories حسب Elizaveta Mukhaleva (20602550)

    منشور في 2025
    "…Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. …"
  15. 55

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories حسب Elizaveta Mukhaleva (20602550)

    منشور في 2025
    "…Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. …"
  16. 56

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories حسب Elizaveta Mukhaleva (20602550)

    منشور في 2025
    "…Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. …"
  17. 57

    Deep Learning-Based Visual Enhancement and Real-Time Underground-Mine Water Inflow Detection حسب Huichao Yin (14589020)

    منشور في 2025
    "…<p dir="ltr">Python image preprocessing and model implementation for research of "Deep Learning-Based Visual Enhancement and Real-Time Underground-Mine Water Inflow Detection".…"
  18. 58

    Neural-Signal Tokenization and Real-Time Contextual Foundation Modelling for Sovereign-Scale AGI Systems حسب Lakshit Mathur (20894549)

    منشور في 2025
    "…The work advances national AI autonomy, real-time cognitive context modeling, and ethical human-AI integration.…"
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  20. 60

    A Structured Attempt at a Polynomial-Time Solution to the Subset Sum Problem and Its Implications for P vs NP حسب MInakshi Aggarwal (21677633)

    منشور في 2025
    "…The manuscript includes theoretical formulation, Python implementation, verified output snapshots, and detailed analysis — aimed at opening fresh discourse on the P vs NP question. …"