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Showing 61 - 80 results of 134 for search '(( lines based process optimization algorithm ) OR ( binary mapk driven optimization algorithm ))', query time: 1.56s Refine Results
  1. 61
    Schematic view of fog layer.

    Schematic view of fog layer. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  2. 62
    Simulation parameter.

    Simulation parameter. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  3. 63
    Systematic model of fog-cloud COVID prediction.

    Systematic model of fog-cloud COVID prediction. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  4. 64
    Comparative discussion on database-2.

    Comparative discussion on database-2. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  5. 65
    Solution encoding.

    Solution encoding. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  6. 66
    Architectural view of DNFN.

    Architectural view of DNFN. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  7. 67
    Modelled view of cloud layer.

    Modelled view of cloud layer. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  8. 68
    Architecture of Deep LSTM.

    Architecture of Deep LSTM. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  9. 69
    Architectural view of PSP-Net.

    Architectural view of PSP-Net. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  10. 70
    Simulation parameters.

    Simulation parameters. by Shanthi Amgothu (17692146)

    Published 2023
    “…Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). …”
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  11. 71
    DataSheet1_Stochastic and Iterative Based Optimization for Enhancing Dynamic Performance of Interconnected Power System With Hybrid Energy Storage.docx

    DataSheet1_Stochastic and Iterative Based Optimization for Enhancing Dynamic Performance of Interconnected Power System With Hybrid Energy Storage.docx by Niveditha Sivadanam (12328148)

    Published 2022
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  12. 72
    Parallel plot showing hyperparameter exploration.

    Parallel plot showing hyperparameter exploration. by Ramona Leenings (11167294)

    Published 2021
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  13. 73
    Gray variance product line chart.

    Gray variance product line chart. by Qianwei Deng (19353264)

    Published 2024
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  14. 74
    Inspection of Line Defects in Transition Metal Dichalcogenides Using a Microscopic Hyperspectral Imaging Technique

    Inspection of Line Defects in Transition Metal Dichalcogenides Using a Microscopic Hyperspectral Imaging Technique by Xiangmin Hu (1612399)

    Published 2022
    “…A light selection method was proposed to optimize the optical contrast of line defects. Via application of an image processing algorithm, an automatic detection of the line defects with a classification accuracy of 95% was achieved for WS<sub>2</sub>, MoS<sub>2</sub>, and MoSe<sub>2</sub>. …”
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  15. 75
    Post scan feature estimates and confidence ratings.

    Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

    Published 2025
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  16. 76
    Decoding results.

    Decoding results. by Sam Hall-McMaster (10343795)

    Published 2025
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  17. 77
    Subgroup analysis.

    Subgroup analysis. by Sam Hall-McMaster (10343795)

    Published 2025
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  18. 78
    Behavioral results.

    Behavioral results. by Sam Hall-McMaster (10343795)

    Published 2025
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  19. 79
    Learning dynamics on training tasks.

    Learning dynamics on training tasks. by Sam Hall-McMaster (10343795)

    Published 2025
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  20. 80
    Comparison of city decoders trained on session one and session two data.

    Comparison of city decoders trained on session one and session two data. by Sam Hall-McMaster (10343795)

    Published 2025
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