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Showing 1 - 20 results of 214 for search '(( binary based process optimization algorithm ) OR ( lines used all optimization algorithm ))', query time: 0.50s 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
    Classification results for all subsampling scenarios and algorithms for Gefitinib.

    Classification results for all subsampling scenarios and algorithms for Gefitinib. by Sofia P. Miranda (11418930)

    Published 2021
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  3. 3
    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm

    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm by Anonymous authors (for blind review purpose) (6473313)

    Published 2020
    “…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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  4. 4
    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm

    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm by Anonymous authors (for blind review purpose) (6473313)

    Published 2021
    “…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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  5. 5
    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm

    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm by Jiaxuan Shi (7455707)

    Published 2020
    “…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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  6. 6
    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm

    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm by Jiaxuan Shi (7455707)

    Published 2020
    “…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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  7. 7
    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm

    A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm by Jiaxuan Shi (7455707)

    Published 2020
    “…This is the experimental data of the manuscript entitled “A New Bifuzzy Optimization Method for Remanufacturing Scheduling Using Extended Discrete Particle Swarm Optimization Algorithm”. …”
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  8. 8
    Regression results for all combinations of subsampling scenarios and algorithms for Gefitinib.

    Regression results for all combinations of subsampling scenarios and algorithms for Gefitinib. by Sofia P. Miranda (11418930)

    Published 2021
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  9. 9
    Average RMSE rank for all combinations of subsampling scenarios and algorithms.

    Average RMSE rank for all combinations of subsampling scenarios and algorithms. by Sofia P. Miranda (11418930)

    Published 2021
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  10. 10
    Summary of AUC values across all combinations of subsampling scenario and algorithm.

    Summary of AUC values across all combinations of subsampling scenario and algorithm. by Sofia P. Miranda (11418930)

    Published 2021
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  11. 11
    Feature selection process.

    Feature selection process. by Balraj Preet Kaur (20370832)

    Published 2024
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  12. 12
    Minimum, mean and maximum RMSE value for each drug and algorithm combination, averaged across all subsampling scenarios.

    Minimum, mean and maximum RMSE value for each drug and algorithm combination, averaged across all subsampling scenarios. by Sofia P. Miranda (11418930)

    Published 2021
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  13. 13
    Minimum, mean and maximum AUC value for each combination of drug and algorithm, averaged across all subsampling scenarios.

    Minimum, mean and maximum AUC value for each combination of drug and algorithm, averaged across all subsampling scenarios. by Sofia P. Miranda (11418930)

    Published 2021
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  14. 14
    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results

    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

    Published 2025
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  15. 15
    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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  16. 16
    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>.

    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>. by Benjamin Gallois (10131683)

    Published 2021
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  17. 17
    Relative predictive performance when using default algorithm hyperparameters and all features vs. tuning hyperparameters or selecting features.

    Relative predictive performance when using default algorithm hyperparameters and all features vs. tuning hyperparameters or selecting features. by Stephen R. Piccolo (8367780)

    Published 2022
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  18. 18
    Schematic of iteration process of IDE-IIGA.

    Schematic of iteration process of IDE-IIGA. by Ling Zhao (111365)

    Published 2025
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  19. 19
    Correlation between predicted drug responses based on GDSC cell lines and recorded clinical responses in TCGA patients for selected combinations of subsampling scenarios and algorithms across all drugs.

    Correlation between predicted drug responses based on GDSC cell lines and recorded clinical responses in TCGA patients for selected combinations of subsampling scenarios and algori... by Sofia P. Miranda (11418930)

    Published 2021
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  20. 20
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
    “…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
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