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Showing 201 - 220 results of 1,023 for search '(( elements method algorithm ) OR ((( data encoding algorithm ) OR ( based binding algorithm ))))', query time: 0.46s Refine Results
  1. 201
    The accuracy of MNIST dataset under FedAvg, FedProx, and SCAFFOLD.

    The accuracy of MNIST dataset under FedAvg, FedProx, and SCAFFOLD. by Pan Wang (198097)

    Published 2025
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  2. 202
    Visualization Comparison Between Client Model with EA.

    Visualization Comparison Between Client Model with EA. by Pan Wang (198097)

    Published 2025
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  3. 203
    Comparison between methods with EA and methods without EA.

    Comparison between methods with EA and methods without EA. by Pan Wang (198097)

    Published 2025
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  4. 204
    Comparison of model distances.

    Comparison of model distances. by Pan Wang (198097)

    Published 2025
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  5. 205
    The process of FL.

    The process of FL. by Pan Wang (198097)

    Published 2025
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  6. 206
    Impact of the Dirichlet distribution.

    Impact of the Dirichlet distribution. by Pan Wang (198097)

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  7. 207
    Mutation operator.

    Mutation operator. by Pan Wang (198097)

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  8. 208
    Comparison of MNIST with different intervals under FedAvg, FedProx and SCAFFOLD.

    Comparison of MNIST with different intervals under FedAvg, FedProx and SCAFFOLD. by Pan Wang (198097)

    Published 2025
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  9. 209
    Impact of the hyperparameter <i>μ.</i>

    Impact of the hyperparameter <i>μ.</i> by Pan Wang (198097)

    Published 2025
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  10. 210
    The accuracy of FashionMNIST dataset under FedAvg, FedProx, and SCAFFOLD.

    The accuracy of FashionMNIST dataset under FedAvg, FedProx, and SCAFFOLD. by Pan Wang (198097)

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  11. 211
    Hyper-parameter settings.

    Hyper-parameter settings. by Pan Wang (198097)

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  12. 212
    The accuracy of MNIST dataset under FedAvg, FedProx, and SCAFFOLD.

    The accuracy of MNIST dataset under FedAvg, FedProx, and SCAFFOLD. by Pan Wang (198097)

    Published 2025
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  13. 213
    A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Proportional, <i>P</i>), or unconstrained (Equiprobable, <i>E</i>).

    A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

    Published 2024
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  14. 214
    Performance of classifiers for normal and abnormal task detection using Recall (R), Precision (P), and F1-score (F1). G.LSTM, G.DNN, and G.CNN denote proposed models with Noise-Aware Encoder (NAE).

    Performance of classifiers for normal and abnormal task detection using Recall (R), Precision (P), and F1-score (F1). G.LSTM, G.DNN, and G.CNN denote proposed models with Noise-Awa... by Amin Ullah (12015113)

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  15. 215
    Predictive factors affecting hepatitis patients survival results via application of the machine learning methods

    Predictive factors affecting hepatitis patients survival results via application of the machine learning methods by Xiaohua Li (311008)

    Published 2025
    “…Machine learning models help predict hepatitis risks using patient data, symptoms, and history. This study used Decision Tree Classification (DTC) and Extreme Gradient Boosting Classification (XGBC) with three optimizers Rhizotomy Optimization Algorithm (ROA), Gold Rush Optimizer (GRO), and Motion-Encoded Electric Charged Particles Optimization Algorithm (MEPO) to enhance accuracy. …”
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  16. 216
    Calibration capacitors (see Fig 2C), phases for the benchtop test, and <i>S</i><sub><i>nn</i></sub> scattering parameters with analogous phase MRI calibration values for each element of the 4-channel PTX coil configuration.

    Calibration capacitors (see Fig 2C), phases for the benchtop test, and <i>S</i><sub><i>nn</i></sub> scattering parameters with analogous phase MRI calibration values for each eleme... by Maryam Arianpouya (12299222)

    Published 2024
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  17. 217
    (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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  18. 218
    Table 1_i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.xlsx

    Table 1_i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.xlsx by Sakshi Gautam (10305897)

    Published 2025
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  19. 219
    Mean and standard deviation (SD) of MRI-derived myelin water fraction (MWF) values calculated using the data-driven mcT<sub>2</sub> approach and the conventional approach for the two mice groups and across six brain ROIs: cortex, medial corpus callosum (MCC), external corpus callosum (ECC), hippocampus (HIPP), thalamus, and striatum. All relevant data are available at data-driven-mcT2-cuprizone-validation GitHub repository and can be accessed at: https://github.com/NoamBenEliezer/data-driven-mcT2-cuprizone-validation.

    Mean and standard deviation (SD) of MRI-derived myelin water fraction (MWF) values calculated using the data-driven mcT<sub>2</sub> approach and the conventional approach for the t... by Noam Omer (21402801)

    Published 2025
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  20. 220
    Data Sheet 1_i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.docx

    Data Sheet 1_i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus.docx by Sakshi Gautam (10305897)

    Published 2025
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