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Showing 1 - 20 results of 2,552 for search '(((( experiments based algorithm ) OR ( complement b algorithm ))) OR ( neural coding algorithm ))', query time: 0.40s Refine Results
  1. 1
    Data and code resources.

    Data and code resources. by Sam Hall-McMaster (10343795)

    Published 2025
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  2. 2
    Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code

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

    Published 2025
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  3. 3
    The genome coding scheme.

    The genome coding scheme. by Wenbing Shi (5806160)

    Published 2025
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  4. 4
    Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data.

    Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. by Daniel Suárez-Barrera (22676654)

    Published 2025
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  5. 5
    A bi-agent single-processor scheduling to minimize the sum of maximum lateness with release dates

    A bi-agent single-processor scheduling to minimize the sum of maximum lateness with release dates by Danyu Bai (3388310)

    Published 2025
    “…Simulation experiments endorse the high efficiency of the proposed algorithms.…”
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  6. 6
    Ablation experiment for the micro-localization algorithm model.

    Ablation experiment for the micro-localization algorithm model. by Zhongjian Xie (4633099)

    Published 2024
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  7. 7
    Ablation experiment for the macro-detection algorithm model.

    Ablation experiment for the macro-detection algorithm model. by Zhongjian Xie (4633099)

    Published 2024
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  8. 8
    Identification of CRG based on WGCNA algorithm.

    Identification of CRG based on WGCNA algorithm. by Lin Feng (84785)

    Published 2025
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  9. 9
    Algorithm flow based on CL-EPIDTN.

    Algorithm flow based on CL-EPIDTN. by Fang Yang (123514)

    Published 2025
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  10. 10
    Dynamic window based median filtering algorithm.

    Dynamic window based median filtering algorithm. by Li Yuan (102305)

    Published 2025
    “…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. …”
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  11. 11
    Algorithm parameter settings employed in the experiments on simulated data.

    Algorithm parameter settings employed in the experiments on simulated data. by Liyan Sun (760586)

    Published 2024
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  12. 12
    The parameter setting of the evolutionary neural network.

    The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

    Published 2025
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  13. 13
    The errors of the predictions based on different algorithm methods.

    The errors of the predictions based on different algorithm methods. by Zhang Wei (317854)

    Published 2025
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  14. 14
    Performance profile of the four algorithms based on CPU time.

    Performance profile of the four algorithms based on CPU time. by Sulaiman M. Ibrahim (20614376)

    Published 2025
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  15. 15
    Performance profile of the four algorithms based on number of iteration.

    Performance profile of the four algorithms based on number of iteration. by Sulaiman M. Ibrahim (20614376)

    Published 2025
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  16. 16
    The proportions of immune cells based on the CIBERSORTx algorithm.

    The proportions of immune cells based on the CIBERSORTx algorithm. by Wan-Jing Zhen (20427813)

    Published 2024
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  17. 17
    The proportions of immune cells based on the MCP algorithm.

    The proportions of immune cells based on the MCP algorithm. by Wan-Jing Zhen (20427813)

    Published 2024
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  18. 18
    Modulation signal classification algorithm based on feature network.

    Modulation signal classification algorithm based on feature network. by Li Yuan (102305)

    Published 2025
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  19. 19
    Face recognition algorithm flow based on the PCA.

    Face recognition algorithm flow based on the PCA. by Na Lin (69470)

    Published 2025
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  20. 20
    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
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