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Showing 1 - 20 results of 222 for search '(( binary data required optimization algorithm ) OR ( binary base based optimization algorithm ))*', query time: 0.61s Refine Results
  1. 1
    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms.

    The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. by Olaide N. Oyelade (14047002)

    Published 2023
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  2. 2
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
    “…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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  3. 3
    An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA.

    An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. by Olaide N. Oyelade (14047002)

    Published 2023
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  4. 4
    Comparisons between ADAM and NADAM optimizers.

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

    Published 2025
    “…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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  5. 5
    Comparison between ILSTM and machine learning-based algorithms in binary classification.

    Comparison between ILSTM and machine learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

    Published 2023
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  6. 6
    Comparison between ILSTM and deep learning-based algorithms in binary classification.

    Comparison between ILSTM and deep learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

    Published 2023
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  7. 7
    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

    Published 2023
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  8. 8
    Medium-scale dataset comparative analysis using fitness and cost values for population size 50 and 100.

    Medium-scale dataset comparative analysis using fitness and cost values for population size 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  9. 9
    Large-scale dataset comparative analysis using computation resources.

    Large-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

    Published 2023
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  10. 10
    Datasets and their properties.

    Datasets and their properties. by Olaide N. Oyelade (14047002)

    Published 2023
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  11. 11
    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50... by Olaide N. Oyelade (14047002)

    Published 2023
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  12. 12
    Small-scale dataset comparative analysis using fitness and cost values for population sizes 50 and 100.

    Small-scale dataset comparative analysis using fitness and cost values for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  13. 13
    Medium-scale dataset comparative analysis using the number of features selected.

    Medium-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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  14. 14
    Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100.

    Small-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  15. 15
    Large-scale dataset comparative analysis using the number of features selected.

    Large-scale dataset comparative analysis using the number of features selected. by Olaide N. Oyelade (14047002)

    Published 2023
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  16. 16
    Medium-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100.

    Medium-scale dataset comparative analysis using classification accuracy for population sizes 50 and 100. by Olaide N. Oyelade (14047002)

    Published 2023
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  17. 17
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

    Published 2023
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  18. 18
    Medium-scale dataset comparative analysis using computation resources.

    Medium-scale dataset comparative analysis using computation resources. by Olaide N. Oyelade (14047002)

    Published 2023
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  19. 19
    Fig 7 -

    Fig 7 - by Olaide N. Oyelade (14047002)

    Published 2023
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  20. 20
    Fig 4 -

    Fig 4 - by Olaide N. Oyelade (14047002)

    Published 2023
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