Search alternatives:
coding algorithm » cosine algorithm (Expand Search), modeling algorithm (Expand Search), finding algorithm (Expand Search)
complement cc3d » complement c3 (Expand Search), complement c4d (Expand Search), complement c5 (Expand Search)
cc3d algorithm » cscap algorithm (Expand Search), cnn algorithm (Expand Search), wold algorithm (Expand Search)
te algorithm » tide algorithm (Expand Search), new algorithm (Expand Search), de algorithms (Expand Search)
level coding » level according (Expand Search), level modeling (Expand Search), level using (Expand Search)
element te » element _ (Expand Search), element g (Expand Search), element data (Expand Search)
Showing 61 - 80 results of 272 for search '(((( element te algorithm ) OR ( complement cc3d algorithm ))) OR ( level coding algorithm ))', query time: 0.44s Refine Results
  1. 61
    Parameters of stimuli used in the study.

    Parameters of stimuli used in the study. by Elnaz Nemati (21402730)

    Published 2025
    “…<div><p>This study introduces a neurobiologically inspired computational model based on the predictive coding algorithm, providing insights into coherent motion detection processes. …”
    Save to List
    Saved in:
  2. 62
    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived from the 3-level wavelet transform (WT) at four resolutions. These features were used as inputs for traditional ML algorithms. The left map demonstrates the correlation matrix of 52 features crafted from 4 resolutions of the 3-level WT, which are used as inputs for traditional ML algorithms. The heat map (on the right) is the correlation matrix after feature selection, in which features whose Pearson’s correlation coefficient is higher than 0.8 are considered highly correlated. We keep only one representative feature and remove its highly correlated features.

    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived... by Quang Dung Dinh (20995766)

    Published 2025
    Save to List
    Saved in:
  3. 63
    Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33]. (b) An alternating active-phase algorithm for multi-material topology optimization [34]. (c) A new mapping based interpolation function for multi-material topology optimization [35]. (d) A material-field series expansion (MFSE) model for multi-material topology optimization [36].

    Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33]. (b) An alternating active-phase... by Zhao Li (300229)

    Published 2025
    Save to List
    Saved in:
  4. 64
    Execution time comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset.

    Execution time comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  5. 65
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  6. 66
    Genes of the epistastic interactions detected on the seven GWAS data using Epi-SSA.

    Genes of the epistastic interactions detected on the seven GWAS data using Epi-SSA. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  7. 67
    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset.

    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  8. 68
    Execution time comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset.

    Execution time comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  9. 69
    The brief description on the WTCCC dataset.

    The brief description on the WTCCC dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  10. 70
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  11. 71
    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  12. 72
    The overall structure and execution flow of Epi-SSA.

    The overall structure and execution flow of Epi-SSA. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  13. 73
    Gene pairs of the epistastic interactions detected on the seven GWAS data using Epi-SSA.

    Gene pairs of the epistastic interactions detected on the seven GWAS data using Epi-SSA. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  14. 74
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  15. 75
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  16. 76
    Part of gene pairs of the epistastic interactions detected on the seven GWAS data using Epi-SSA.

    Part of gene pairs of the epistastic interactions detected on the seven GWAS data using Epi-SSA. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  17. 77
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 100 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  18. 78
    The strategy of sorting the sparrow population based on the rank.

    The strategy of sorting the sparrow population based on the rank. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  19. 79
    The gene network of the epistatic interactions detected for Bipolar Disorder.

    The gene network of the epistatic interactions detected for Bipolar Disorder. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:
  20. 80
    F-measure comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset.

    F-measure comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset. by Liyan Sun (760586)

    Published 2024
    Subjects:
    Save to List
    Saved in:

Search Tools: