Showing 1 - 20 results of 37 for search '(( binary based whole optimization algorithm ) OR ( primary data process segmentation algorithm ))', query time: 0.66s Refine Results
  1. 1
  2. 2
  3. 3
  4. 4
  5. 5

    Segmentation results of the proposed model. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
  6. 6
  7. 7

    Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
  8. 8

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
    “…The sequences can include from a single SNP-allele pair up to a maximum number of pairs defined by the user (<i>l</i><sub>max</sub>). <b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”
  9. 9
  10. 10

    Hybrid feature selection algorithm of CSCO-ROA. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”
  11. 11
  12. 12
  13. 13

    Minimal Dateset. by Hongwei Yue (574068)

    Published 2025
    “…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”
  14. 14

    Loss Function Comparison. by Hongwei Yue (574068)

    Published 2025
    “…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”
  15. 15

    Comparative Results of Different Models. by Hongwei Yue (574068)

    Published 2025
    “…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”
  16. 16

    Loss Function Comparison. by Hongwei Yue (574068)

    Published 2025
    “…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”
  17. 17

    Overall Framework of the PSO-KM Model. by Hongwei Yue (574068)

    Published 2025
    “…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”
  18. 18

    Overall Framework of the PSO-KM Model. by Hongwei Yue (574068)

    Published 2025
    “…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”
  19. 19

    Image_1_Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automat... by Mariam Aboian (8416743)

    Published 2022
    “…</p>Materials and methods<p>An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. …”
  20. 20