Search alternatives:
probes optimization » process optimization (Expand Search), robust optimization (Expand Search), property optimization (Expand Search)
library based » laboratory based (Expand Search)
based probes » based protein (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
Showing 1 - 20 results of 278 for search '(( library based probes optimization algorithm ) OR ( binary data _ optimization algorithm ))', query time: 0.48s Refine Results
  1. 1
    Sensitivity values of IRBMO vs. other binary algorithms.

    Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
    Save to List
    Saved in:
  2. 2
    Specificity values of IRBMO vs. other binary algorithms.

    Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
    Save to List
    Saved in:
  3. 3
    Fitness values of IRBMO vs. other binary algorithms.

    Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
    Save to List
    Saved in:
  4. 4
    An optimal solution for the HFS instance.

    An optimal solution for the HFS instance. by Xiang Tian (4369285)

    Published 2025
    “…This work firstly proposes an Improved Probe Machine with Multi-Level Probe Operations (IPMMPO) and ingeniously designs general data libraries and probe libraries tailored for multi-scenario HFS problems, including HFS with identical parallel machines and HFS with unrelated parallel machines, no-wait scenario, and standard scenario. …”
    Save to List
    Saved in:
  5. 5
    Number of features selected for IRBMO vs. other binary algorithms.

    Number of features selected for IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
    Save to List
    Saved in:
  6. 6
    Classification accuracy values of IRBMO vs. other binary algorithms.

    Classification accuracy values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
    Save to List
    Saved in:
  7. 7
    F-score values of IRBMO vs. other binary algorithms.

    F-score values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
    Save to List
    Saved in:
  8. 8
    One advantage of our algorithm is that it is not binary in nature.

    One advantage of our algorithm is that it is not binary in nature. by Yong Kuk Kim (13955214)

    Published 2022
    Save to List
    Saved in:
  9. 9
    CSTP-MAC algorithm backoff time-slots vs IEEE 802.15.4 binary backoff exponential algorithm backoff time-slots.

    CSTP-MAC algorithm backoff time-slots vs IEEE 802.15.4 binary backoff exponential algorithm backoff time-slots. by Innocent Uzougbo Onwuegbuzie (9243166)

    Published 2020
    Save to List
    Saved in:
  10. 10
    The result of the accuracy of tested algorithms.

    The result of the accuracy of tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  11. 11
    Feature selection ratio results for tested algorithms.

    Feature selection ratio results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  12. 12
    Best fitness value results for tested algorithms.

    Best fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  13. 13
    Mean fitness value results for tested algorithms.

    Mean fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  14. 14
    Binary contingency table.

    Binary contingency table. by Lu Xiao (314030)

    Published 2024
    Save to List
    Saved in:
  15. 15
    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
    Subjects:
    Save to List
    Saved in:
  16. 16
    Comparison based on hard instances from [79].

    Comparison based on hard instances from [79]. by Xiang Tian (4369285)

    Published 2025
    “…This work firstly proposes an Improved Probe Machine with Multi-Level Probe Operations (IPMMPO) and ingeniously designs general data libraries and probe libraries tailored for multi-scenario HFS problems, including HFS with identical parallel machines and HFS with unrelated parallel machines, no-wait scenario, and standard scenario. …”
    Save to List
    Saved in:
  17. 17
    Numerical example for determining the optimal number of clusters in Algorithm 2.

    Numerical example for determining the optimal number of clusters in Algorithm 2. by Yi Wei Tye (22208735)

    Published 2025
    Save to List
    Saved in:
  18. 18
    Standard deviation of fitness value results for tested algorithms.

    Standard deviation of fitness value results for tested algorithms. by Nebojsa Bacanin (13932723)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  19. 19
    DataSheet1_Improving the convergence of an iterative algorithm for solving arbitrary linear equation systems using classical or quantum binary optimization.pdf

    DataSheet1_Improving the convergence of an iterative algorithm for solving arbitrary linear equation systems using classical or quantum binary optimization.pdf by Erick R. Castro (11243697)

    Published 2024
    Save to List
    Saved in:
  20. 20
    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset.

    a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset. by Nebojsa Bacanin (13932723)

    Published 2022
    Subjects:
    Save to List
    Saved in:

Search Tools: