Showing 1 - 20 results of 1,523 for search '(( learning ((ml decrease) OR (nn decrease)) ) OR ( b ((large decrease) OR (marked decrease)) ))', query time: 0.49s Refine Results
  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12

    Performance Table for ML Models. by Gaurav Sandeep Dave (21519884)

    Published 2025
    “…The quality of recorded data plays a crucial role and directly influences the data transformation phase in machine learning (ML) and deep learning (DL) models. The Dewesoft FFT DAQ system is designed to extract the high-quality data from the CPM based on sensor fusion technology. …”
  13. 13
  14. 14
  15. 15
  16. 16

    <b>Effect of Marked Weight Loss on Adipose Tissue Biology in People with Obesity and Type 2 Diabetes</b> by Dmitri Samovski (305400)

    Published 2025
    “…</p><p dir="ltr"><b>Results: </b>Weight loss: <a href="" target="_blank">i) </a><a href="" target="_blank">decreased adipose tissue </a>expression of genes related to extracellular matrix remodeling; ii) decreased adipose tissue expression of SERPINE 1 which encodes plasminogen activator inhibitor-1 (PAI-1); iii) did not decrease adipose tissue immune cell content or expression of genes involved in inflammation; iv) decreased adipose tissue ceramide content; v) decreased plasma <a href="" target="_blank">PAI-1 </a>and leptin concentrations and increased plasma high-molecular weight (HMW) adiponectin; and vi) decreased plasma small extracellular vesicle (sEV) concentration and the sEV content of microRNAs proposed to inhibit insulin action, and completely reversed the inhibitory effect of plasma sEVs on insulin signaling in myotubes.…”
  17. 17
  18. 18

    ROC analysis to mark selectivity results in mostly mixed-selective units. by Thomas S. Wierda (22404198)

    Published 2025
    “…<b>b</b> There do not seem to be any significant differences between fast and slow groups using the ROC definition, likely because almost all neurons are marked to be mixed selective as compared to our rate-based classification approach. …”
  19. 19
  20. 20