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Showing 1 - 20 results of 173 for search '(( binary data models optimization algorithm ) OR ( binary image process optimization algorithm ))*', query time: 0.46s Refine Results
  1. 1
    The illustration depicts our CQ-CNN architecture for binary image classification.

    The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

    Published 2025
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  2. 2
    A* Path-Finding Algorithm to Determine Cell Connections

    A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

    Published 2025
    “…</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. …”
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  3. 3
    Sensitivity values of IRBMO vs. other binary algorithms.

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

    Published 2025
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  4. 4
    Specificity values of IRBMO vs. other binary algorithms.

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

    Published 2025
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  5. 5
    Fitness values of IRBMO vs. other binary algorithms.

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

    Published 2025
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  6. 6
    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
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  7. 7
    Classification accuracy values of IRBMO vs. other binary algorithms.

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

    Published 2025
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  8. 8
    F-score values of IRBMO vs. other binary algorithms.

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

    Published 2025
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  9. 9
    tUbenet Foundation Model Weights

    tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

    Published 2025
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  10. 10
    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
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  11. 11
    Binary contingency table.

    Binary contingency table. by Lu Xiao (314030)

    Published 2024
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  12. 12
    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values .

    RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

    Published 2023
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  13. 13
    Top-performing model configuration and performance by algorithm type.

    Top-performing model configuration and performance by algorithm type. by Uğur Ejder (22683228)

    Published 2025
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  14. 14
    Confusion matrix for a binary outcome (adapted from Fawcett [30]).

    Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

    Published 2022
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  15. 15
    Image processing workflow.

    Image processing workflow. by Denis Tamiev (7404980)

    Published 2020
    “…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”
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  16. 16
    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

    Published 2019
    “…This result reflects the effectiveness of the algorithm, which provides a basis for the effective analysis and processing of image big data.…”
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  17. 17
    Dataset 2: Zip file containing the Tables of the presented method and results

    Dataset 2: Zip file containing the Tables of the presented method and results by Suchismita Behera (22027316)

    Published 2025
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  18. 18
    Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data

    Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

    Published 2022
    “…In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. …”
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  19. 19
    The comparison of the accuracy score of the benchmark and the proposed models.

    The comparison of the accuracy score of the benchmark and the proposed models. by Uğur Ejder (22683228)

    Published 2025
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  20. 20
    The prediction accuracy of simple versus hybrid models.

    The prediction accuracy of simple versus hybrid models. by Uğur Ejder (22683228)

    Published 2025
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