Showing 1 - 15 results of 15 for search '(( binary most robust optimization algorithm ) OR ( primary data whale optimization algorithm ))', query time: 0.31s Refine Results
  1. 1
  2. 2

    Proposed model tuned hyperparameters. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  3. 3

    The workflow of the proposed model. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  4. 4

    ResNeXt101 training and results. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  5. 5

    Proposed model specificity and DSC outcomes. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  6. 6

    Accuracy comparison of proposed and other models. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  7. 7

    Architecture of ConvNet. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  8. 8

    Comparison of state-of-the-art method. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  9. 9

    Proposed model sensitivity outcome. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  10. 10

    Proposed ResNeXt101 operational flow. by Subathra Gunasekaran (19492680)

    Published 2024
    “…To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). …”
  11. 11

    Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures by Hongpeng He (348094)

    Published 2021
    “…The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. …”
  12. 12

    Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

    Published 2021
    “…Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. …”
  13. 13

    Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

    Published 2025
    “…<br>The consistency of the results across different kernels demonstrates that the information contained in the habitat, by itself, leads to a very simple optimal decision rule (mostly the prediction of the most frequent class per habitat), which cannot be improved solely by model adjustments. …”
  14. 14

    DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. …”
  15. 15

    Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. …”