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Showing 1 - 20 results of 54 for search '(( binary edge policy optimization algorithm ) OR ( binary _ robust optimization algorithm ))', query time: 0.53s Refine Results
  1. 1
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
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  2. 2
    Comparisons between ADAM and NADAM optimizers.

    Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

    Published 2025
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  3. 3
    Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30.

    Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30. by Hend Bayoumi (22693738)

    Published 2025
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  4. 4
    Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30.

    Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30. by Hend Bayoumi (22693738)

    Published 2025
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  5. 5
    Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization

    Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization by Jian-Feng Cai (498405)

    Published 2022
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  6. 6
    Binary contingency table.

    Binary contingency table. by Lu Xiao (314030)

    Published 2024
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  7. 7
    Overview of the segmentation algorithm.

    Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

    Published 2022
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  8. 8
    An Example of a WPT-MEC Network.

    An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

    Published 2025
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  9. 9
    Related Work Summary.

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
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  10. 10
    Simulation parameters.

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
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  11. 11
    Training losses for N = 10.

    Training losses for N = 10. by Hend Bayoumi (22693738)

    Published 2025
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  12. 12
    Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods.

    Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods. by Hend Bayoumi (22693738)

    Published 2025
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  13. 13
    The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames.

    The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames. by Hend Bayoumi (22693738)

    Published 2025
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  14. 14
    Normalized computation rate for N = 10.

    Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

    Published 2025
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  15. 15
    CDF of task latency, approximated as the inverse of the achieved computation rate.

    CDF of task latency, approximated as the inverse of the achieved computation rate. by Hend Bayoumi (22693738)

    Published 2025
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  16. 16
    Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate.

    Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate. by Hend Bayoumi (22693738)

    Published 2025
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  17. 17
    Summary of Notations Used in this paper.

    Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

    Published 2025
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  18. 18
    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values .

    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

    Published 2023
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  19. 19
    Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization

    Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization by Sandeep Jagonda Patil (22048337)

    Published 2025
    “…By integrating Latent Encoder Coupled Generative Adversarial Network (LEGAN) optimized with Binary Emperor Penguin optimizer (BEPO), the scheme enhances routing efficiency and security. …”
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  20. 20
    The Pseudo-Code of the IRBMO Algorithm.

    The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

    Published 2025
    “…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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