Showing 1,761 - 1,780 results of 8,689 for search 'significantly ((we decrease) OR (((teer decrease) OR (((nn decrease) OR (mean decrease))))))', query time: 0.53s Refine Results
  1. 1761

    Result of comparison of different lightweight. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  2. 1762

    DyHead Structure. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  3. 1763

    The parameters of the training phase. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  4. 1764

    Structure of GSConv network. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  5. 1765

    Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  6. 1766

    Improved model distillation structure. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
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  16. 1776

    <i>Aedes aegypti</i> database from SAGO traps. by Jesús A. Aguilar-Durán (9931967)

    Published 2025
    “…IVM treatment reduced the number of females per trap per week from 3.29 ± 0.24 to 2.41 ± 0.20 (33.7% reduction), AGO from 1.58 ± 0.17 to 0.25 ± 0.05 (85.2% reduction), and AGO + IVM from 1.49 ± 0.17 to 0.53 ± 0.08 (67.78% reduction), based on Henderson’s formula. We observed a non-significant increase in the control area (no treatment provided) in the mosquito populations, increasing from 2.94 ± 0.24 in the pretreatment period to 3.25 ± 0.28 of the post treatment period.…”
  17. 1777
  18. 1778

    Patient demographics. by Katrine Carlsen (5477300)

    Published 2024
    “…</p><p>Results</p><p>From 25 patients (median age 14.4 years) diagnosed with Crohn´s Disease (n = 16) or ulcerative colitis (n = 9), microbiota were analysed in 671 faecal samples collected across 15 treatment intervals.</p><p>A significant decrease over time in Shannon diversity, following the initial increase within four weeks of treatment, was found across patients. …”
  19. 1779

    All DEGs in wild-type and <i>atb’’αβγδ</i> plants. by Hyuk Sung Yoon (20208672)

    Published 2024
    “…Transcriptome analysis showed a significant decrease in the expression of <i>CYP707A3</i>, a gene potentially targeted by VIP1 that regulates abscisic acid (ABA) catabolism, in the <i>atb’’αβγδ</i> mutant compared to wild type leaves. …”
  20. 1780

    Alpha diversity measures across all 727 samples. by Katrine Carlsen (5477300)

    Published 2024
    “…</p><p>Results</p><p>From 25 patients (median age 14.4 years) diagnosed with Crohn´s Disease (n = 16) or ulcerative colitis (n = 9), microbiota were analysed in 671 faecal samples collected across 15 treatment intervals.</p><p>A significant decrease over time in Shannon diversity, following the initial increase within four weeks of treatment, was found across patients. …”