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Showing 1 - 20 results of 2,758 for search '(((( elements fe algorithm ) OR ( experiments using algorithm ))) OR ( neural coding algorithm ))', query time: 0.41s 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
    The genome coding scheme.

    The genome coding scheme. by Wenbing Shi (5806160)

    Published 2025
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  3. 3
    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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  4. 4
    Hyperparameters of self-supervised learning algorithms used in Experiments 1–5.

    Hyperparameters of self-supervised learning algorithms used in Experiments 1–5. by Lalit Pandey (13195488)

    Published 2024
    “…<p>Hyperparameters of self-supervised learning algorithms used in Experiments 1–5.</p>…”
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  5. 5
    Algorithm comparison experiment on the RSOD dataset.

    Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

    Published 2025
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  6. 6
    Source Code and Dataset

    Source Code and Dataset by Swathi Ganesan (14394924)

    Published 2025
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  7. 7
    State and action space sizes of the robots in the experiment and the corresponding parameters of the algorithms used.

    State and action space sizes of the robots in the experiment and the corresponding parameters of the algorithms used. by Teh Noranis Mohd Aris (22600931)

    Published 2025
    “…<p>State and action space sizes of the robots in the experiment and the corresponding parameters of the algorithms used.…”
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  8. 8
    Characteristics of the databases used in the experiments.

    Characteristics of the databases used in the experiments. by Loan T. T. Nguyen (20660789)

    Published 2025
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  9. 9
    The dataset used in the experiments.

    The dataset used in the experiments. by Genliang Li (5816264)

    Published 2025
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  10. 10
    Images of brain tumors used in the experiments and their corresponding histograms.

    Images of brain tumors used in the experiments and their corresponding histograms. by Jiaqi Ma (773451)

    Published 2024
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  11. 11
    The prompt used for the experiment.

    The prompt used for the experiment. by Ali Abir Shuvro (21982871)

    Published 2025
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  12. 12
    Datasets used in the experiment [3].

    Datasets used in the experiment [3]. by Ahmet Gürhanlı (21602042)

    Published 2025
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  13. 13
    Performance comparison experiments with mainstream object detection algorithms.

    Performance comparison experiments with mainstream object detection algorithms. by Dongshan Jiang (21866339)

    Published 2025
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  14. 14
    Sanitized databases using MLHProtector algorithm.

    Sanitized databases using MLHProtector algorithm. by Loan T. T. Nguyen (20660789)

    Published 2025
    “…Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.…”
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  15. 15
    An overview of the organization of the study program.

    An overview of the organization of the study program. by Marialuisa Gandolfi (8639238)

    Published 2024
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  16. 16
    Overview of the development of the biomarkers in the motor, exteroceptive, interoceptive, and cerebral domains.

    Overview of the development of the biomarkers in the motor, exteroceptive, interoceptive, and cerebral domains. by Marialuisa Gandolfi (8639238)

    Published 2024
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  17. 17
    The schedule of enrolment, interventions, and assessments.

    The schedule of enrolment, interventions, and assessments. by Marialuisa Gandolfi (8639238)

    Published 2024
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  18. 18
    Algorithm comparison experiment on the NWPU VHR-10 dataset.

    Algorithm comparison experiment on the NWPU VHR-10 dataset. by Pingping Yan (462509)

    Published 2025
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  19. 19
    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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  20. 20
    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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