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Showing 61 - 80 results of 433 for search '(( primary data using optimization algorithm ) OR ( binary a robust optimization algorithm ))', query time: 0.56s Refine Results
  1. 61
    S1 Data -

    S1 Data - by Guangwei Liu (181992)

    Published 2023
    “…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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  2. 62
    Curve of step response signal of 6 algorithms.

    Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

    Published 2023
    “…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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  3. 63
    Data_Sheet_2_Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [18F]FDG PET-CT.docx

    Data_Sheet_2_Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [18F]FDG PET-CT.docx by Anthime Flaus (10153055)

    Published 2021
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  4. 64
    Data_Sheet_1_Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [18F]FDG PET-CT.docx

    Data_Sheet_1_Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [18F]FDG PET-CT.docx by Anthime Flaus (10153055)

    Published 2021
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  5. 65
    Proposed reinforcement learning architecture.

    Proposed reinforcement learning architecture. by Enoch Solomon (21416703)

    Published 2025
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  6. 66
    The images shown correspond the downscaled images an agent receives in Unity environment.

    The images shown correspond the downscaled images an agent receives in Unity environment. by Enoch Solomon (21416703)

    Published 2025
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  7. 67
    Average success rate (%) of the test experiment on ViZDoom and Unity environments.

    Average success rate (%) of the test experiment on ViZDoom and Unity environments. by Enoch Solomon (21416703)

    Published 2025
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  8. 68
    Comparative analysis of DDcGAN-GSOM’s True Positive Rate, False Alarm Rate, and Specificity.

    Comparative analysis of DDcGAN-GSOM’s True Positive Rate, False Alarm Rate, and Specificity. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  9. 69
    Architecture of proposed system model.

    Architecture of proposed system model. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  10. 70
    F1-score analysis of DDcGAN-GSOM with existing methods.

    F1-score analysis of DDcGAN-GSOM with existing methods. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  11. 71
    Accuracy analysis of DDcGAN-GSOM with existing techniques.

    Accuracy analysis of DDcGAN-GSOM with existing techniques. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  12. 72
    Network simulation of the proposed method.

    Network simulation of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  13. 73
    Simulation parameters of SDN network.

    Simulation parameters of SDN network. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  14. 74
    Generator loss of the proposed method.

    Generator loss of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  15. 75
    Simulation parameters of DDcGAN.

    Simulation parameters of DDcGAN. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  16. 76
    Throughput analysis of DDcGAN-GSOM with prevailing approaches.

    Throughput analysis of DDcGAN-GSOM with prevailing approaches. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  17. 77
    Performance metrics.

    Performance metrics. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  18. 78
    Performance comparison of proposed system with state-of-the-art approaches.

    Performance comparison of proposed system with state-of-the-art approaches. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  19. 79
    Schematic representation of univariate ensemble-based feature selection.

    Schematic representation of univariate ensemble-based feature selection. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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  20. 80
    Energy consumption analysis of DDcGAN-GSOM with existing approaches.

    Energy consumption analysis of DDcGAN-GSOM with existing approaches. by Nanavath Kiran Singh Nayak (22184565)

    Published 2025
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