Search alternatives:
learning optimization » learning motivation (Expand Search), lead optimization (Expand Search)
based optimization » whale optimization (Expand Search)
edge learning » deep learning (Expand Search), e learning (Expand Search), face learning (Expand Search)
primary data » primary care (Expand Search)
binary edge » binary image (Expand Search)
data based » data used (Expand Search)
Showing 1 - 20 results of 318 for search '(( binary edge learning optimization algorithm ) OR ( primary data based optimization algorithm ))', query time: 0.65s Refine Results
  1. 1
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  2. 2
    Comparisons between ADAM and NADAM optimizers.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  3. 3
    Features selected by optimization algorithms.

    Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
    Save to List
    Saved in:
  4. 4
    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
    Subjects:
    Save to List
    Saved in:
  5. 5
    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
    Subjects:
    Save to List
    Saved in:
  6. 6
    The convergence curve of each optimization algorithm under the test function.

    The convergence curve of each optimization algorithm under the test function. by Hao Yang (328526)

    Published 2025
    Save to List
    Saved in:
  7. 7
    Data_Sheet_1_How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework.ZIP

    Data_Sheet_1_How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework.ZIP by Hao Wu (65943)

    Published 2022
    Save to List
    Saved in:
  8. 8
    S1 Data -

    S1 Data - by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
    Save to List
    Saved in:
  9. 9
    Parameter settings for algorithms.

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
    Save to List
    Saved in:
  10. 10
    Parameter settings for algorithms.

    Parameter settings for algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
    Save to List
    Saved in:
  11. 11
    Average runtime of different algorithms.

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
    Save to List
    Saved in:
  12. 12
    Average runtime of different algorithms.

    Average runtime of different algorithms. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
    Save to List
    Saved in:
  13. 13
    Flowchart of GJO-GWO algorithm.

    Flowchart of GJO-GWO algorithm. by Guangwei Liu (181992)

    Published 2024
    “…<div><p>This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). …”
    Save to List
    Saved in:
  14. 14
    An Example of a WPT-MEC Network.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  15. 15
    Related Work Summary.

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  16. 16
    Simulation parameters.

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  17. 17
    Training losses for N = 10.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  18. 18
    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
    Subjects:
    Save to List
    Saved in:
  19. 19
    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
    Subjects:
    Save to List
    Saved in:
  20. 20
    Normalized computation rate for N = 10.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:

Search Tools: