Showing 61 - 80 results of 1,355 for search '(((( data code algorithm ) OR ( path finding algorithm ))) OR ( element method algorithm ))', query time: 0.49s Refine Results
  1. 61

    Convergence curve of the DBO algorithm. by Ma Haohao (22177538)

    Published 2025
    “…The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. …”
  2. 62
  3. 63

    Element model generation method with geometric distribution errors by Yiqi Liu (21357815)

    Published 2025
    “…The product surface geometric distribution error is directly attached to the element nodes of the product ideal element model using the error surface reconstruction method and the replacement algorithm of the element node vector height based on the product’s point cloud data. …”
  4. 64
  5. 65
  6. 66
  7. 67

    NSGA-III algorithm flow chart. by Manxian Yang (20521600)

    Published 2025
    “…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …”
  8. 68
  9. 69
  10. 70
  11. 71
  12. 72

    Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

    Published 2025
    “…The data is stored in JSON format under the following labels:</p><ul><li><b>"kannada text"</b> – Kannada algorithmic statement</li><li><b>"text"</b> – English algorithmic statement</li><li><b>"code"</b> – Expected Python output</li></ul><h4><b>Dataset Highlights: -</b></h4><ul><li><b>Triplet Structure:</b> Kannada algorithmic statements, their English translations, and Python code.…”
  13. 73
  14. 74
  15. 75

    Beluga Whale Optimization algorithm flow chart. by Manxian Yang (20521600)

    Published 2025
    “…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …”
  16. 76
  17. 77
  18. 78

    Agricultural field path planning diagram. by Manxian Yang (20521600)

    Published 2025
    “…In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). …”
  19. 79
  20. 80