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Showing 21 - 40 results of 3,162 for search '(( element method algorithm ) OR ((( forest using algorithm ) OR ( neural coding algorithm ))))', query time: 0.42s Refine Results
  1. 21
    Description of the dataset used for the machine learning algorithm.

    Description of the dataset used for the machine learning algorithm. by Tri Ngo Quang (20390189)

    Published 2024
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  2. 22
    Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  3. 23
    Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx

    Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  4. 24
    Image 10_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 10_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  5. 25
    Image 3_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 3_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  6. 26
    Image 4_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 4_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  7. 27
    Image 8_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 8_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  8. 28
    Image 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  9. 29
    Image 6_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 6_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  10. 30
    Image 5_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 5_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  11. 31
    Image 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  12. 32
    Table 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx

    Table 2_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
    Save to List
    Saved in:
  13. 33
    Image 7_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg

    Image 7_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

    Published 2025
    “…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”
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  14. 34
    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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  15. 35
    The genome coding scheme.

    The genome coding scheme. by Wenbing Shi (5806160)

    Published 2025
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  16. 36
    <b>QSPR Study on the Maximum Absorption Wavelength of Indoline Dyes by Using Random Forest Algorithm</b>

    <b>QSPR Study on the Maximum Absorption Wavelength of Indoline Dyes by Using Random Forest Algorithm</b> by Long Jiao (22697457)

    Published 2025
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  17. 37
    TreeMap 2016 Forest Type Name Algorithm (Image Service)

    TreeMap 2016 Forest Type Name Algorithm (Image Service) by U.S. Forest Service (17476914)

    Published 2024
    “…We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). …”
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  18. 38
    Variable selection procedure using the Boruta algorithm.

    Variable selection procedure using the Boruta algorithm. by Qiudie Liu (22655907)

    Published 2025
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  19. 39
    Feature selection using the Boruta algorithm method.

    Feature selection using the Boruta algorithm method. by Alemu Birara Zemariam (17540938)

    Published 2025
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  20. 40
    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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