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Showing 1 - 20 results of 672 for search '(( element based algorithm ) OR ((( element processing algorithm ) OR ( level coding algorithm ))))', query time: 0.60s Refine Results
  1. 1
    Comparison of different optimization algorithms.

    Comparison of different optimization algorithms. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  2. 2
    Convergence curve of the DBO algorithm.

    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. …”
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  3. 3
    ML algorithms in cyber security.

    ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

    Published 2025
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  4. 4
    Stress Nephogram of the Splitting Specimen Process.

    Stress Nephogram of the Splitting Specimen Process. by Nan Ru (9594384)

    Published 2025
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  5. 5
    Flowchart of Random Aggregate Placement Process.

    Flowchart of Random Aggregate Placement Process. by Nan Ru (9594384)

    Published 2025
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  6. 6
    Conflict resolution matrix based on TRIZ theory.

    Conflict resolution matrix based on TRIZ theory. by Ronghua Meng (20978441)

    Published 2025
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  7. 7
    Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction

    Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction by Mohamad A. Raja (19640297)

    Published 2025
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  8. 8
    (A) Illustration of grouping and segmentation based on the global similarity structure when the number of elements is small. A, B, C, and D represent stimulus elements at different spatial locations. The physical pairwise similarities are assumed to be high between A and B and between C and D, while they are relatively low for the other four pairs, as indicated by the physical similarity matrix. This configuration potentially leads to the grouping of {A, B} and {C, D}, as depicted by the dashed red squares. Following neural processing that incorporates the global similarity structure, similar pairs are grouped, whereas dissimilar pairs are segregated, as reflected in the perceptual connection matrix. (B) Demonstration of how computational complexity increases with stimulus size. The left panel presents the physical similarity matrix for 3 × 6 and 4 × 8 element stimuli, respectively. The right panel illustrates the exponential growth in the number o

    (A) Illustration of grouping and segmentation based on the global similarity structure when the number of elements is small. A, B, C, and D represent stimulus elements at different... by Yen-Ju Chen (354876)

    Published 2025
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  9. 9
    Comparison of results based on CycleGAN method.

    Comparison of results based on CycleGAN method. by Xiaojun Wang (50332)

    Published 2025
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  10. 10
    Model-Based Clustering of Categorical Data Based on the Hamming Distance

    Model-Based Clustering of Categorical Data Based on the Hamming Distance by Raffaele Argiento (647076)

    Published 2024
    “…<p>A model-based approach is developed for clustering categorical data with no natural ordering. …”
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  11. 11
    Video 1_A hybrid elastic-hyperelastic approach for simulating soft tactile sensors.mp4

    Video 1_A hybrid elastic-hyperelastic approach for simulating soft tactile sensors.mp4 by Berith Atemoztli De la Cruz Sánchez (21758708)

    Published 2025
    “…A significant challenge for simulating tactile sensors is balancing the trade-off between accuracy and processing time in simulation algorithms and models. …”
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  12. 12
    Process followed to develop and validate the MAMELI algorithm.

    Process followed to develop and validate the MAMELI algorithm. by Valentina Bollati (106778)

    Published 2025
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  13. 13
    Pareto optimal front result of MOCOA.

    Pareto optimal front result of MOCOA. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  14. 14
    Confusion matrix.

    Confusion matrix. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  15. 15
    Different patterns of electrocardio signals with Gaussian noise.

    Different patterns of electrocardio signals with Gaussian noise. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  16. 16
    Sensitivity analysis of parameter K and in VMD.

    Sensitivity analysis of parameter K and in VMD. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  17. 17
    Summary of key parameters and performance indices of VMD.

    Summary of key parameters and performance indices of VMD. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  18. 18
    Action potential of sample points in model 1.

    Action potential of sample points in model 1. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  19. 19
    Performance validation on the MIT-BIH database.

    Performance validation on the MIT-BIH database. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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  20. 20
    Exponentially attenuated sinusoidal function.

    Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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