Showing 121 - 140 results of 608 for search 'T46 (classification)', query time: 0.05s Refine Results
  1. 121
  2. 122
  3. 123

    Dynamics of land use land cover change and its effect on urban heat island in Halaba Kulito Town by Begonet Dale, Mihret Dananto, Bisrat Kifle

    Published 2025-01-01
    “…The evaluation of classification accuracy was checked using a confusion error matrix. …”
    Get full text
    Article
  4. 124

    Comparison of ultrasound features and lesion sites in dysfunctional arteriovenous fistula by Yin Wang, Xiao-mei Huang, Yi Zhang, Jingjing Li, Jun Li, Zheng Ye, Yu Peng, Xian-jin Zhang, Na Tang, Wen-wen Qiu, Li Xu

    Published 2024-12-01
    “…Among 185 patients, 100 (54.05%), 36 (19.46%), 22 (11.89%), 11 (5.95%), and 16 (8.65%) were classified into the intima-dominant, non-intima-dominant, valve obstruction, vascular calcification, and mixed groups, respectively. …”
    Get full text
    Article
  5. 125
  6. 126
  7. 127
  8. 128
  9. 129
  10. 130
  11. 131

    Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images by Miao Wu, Chuanbo Yan, Gan Sen

    Published 2025-01-01
    “…The experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. The overall classification accuracy from low to high was 90.46 ± 1.59% for KNN classifier, 90.92 ± 2.49% for transfer learned VGG19, and 92.01 ± 1.48% for SVM, indicating SVM classifiers with deep CNN features achieved the best performance (P < 0.05). …”
    Get full text
    Article
  12. 132
  13. 133
  14. 134
  15. 135
  16. 136
  17. 137
  18. 138
  19. 139
  20. 140