Showing 65,601 - 65,620 results of 68,616 for search '(( 5 ((ng decrease) OR (a decrease)) ) OR ( 50 ((we decrease) OR (mean decrease)) ))', query time: 1.04s Refine Results
  1. 65601

    The performance of S-YOFEO model on MOT17. by Wenshun Sheng (21485393)

    Published 2025
    “…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”
  2. 65602

    Algorithm flowchart of OFEO. by Wenshun Sheng (21485393)

    Published 2025
    “…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”
  3. 65603

    Partial tracking results of MOT17 dataset. by Wenshun Sheng (21485393)

    Published 2025
    “…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”
  4. 65604

    Improved detection layer. by Wenshun Sheng (21485393)

    Published 2025
    “…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”
  5. 65605

    The performance of S-YOFEO model on MOT16. by Wenshun Sheng (21485393)

    Published 2025
    “…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”
  6. 65606

    The matching process of EIOU. by Wenshun Sheng (21485393)

    Published 2025
    “…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”
  7. 65607

    DataSheet1_Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer.xlsx by Donghui Liu (122561)

    Published 2021
    “…We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes.</p><p>Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. …”
  8. 65608

    TBI is ESKD. by Belinda L. Baines (18446381)

    Published 2024
    “…</p><p>Sixteen patients (51.6%) had a normal TBI at baseline, 14 (45.2%) had a mildly low TBI, and one (3.2%) had a severely low TBI. …”
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  20. 65620

    Table_1_Variation of Small and Large Wild Bee Communities Under Honeybee Pressure in Highly Diverse Natural Habitats.DOCX by Imre Demeter (11780864)

    Published 2021
    “…High variation in abundances was detected from one year to another, and the species turnover by sites was 67% in site A, 66% in site V, and 63% in site F. This last one was the site with the previous contact with honeybees. …”