بدائل البحث:
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
data model » data models (توسيع البحث)
b process » _ process (توسيع البحث), a process (توسيع البحث)
binary b » binary _ (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
data model » data models (توسيع البحث)
b process » _ process (توسيع البحث), a process (توسيع البحث)
binary b » binary _ (توسيع البحث)
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81
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84
DEM error verified by airborne data.
منشور في 2024"…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …"
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85
A portfolio selection model based on the knapsack problem under uncertainty
منشور في 2019"…The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. …"
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86
S1 Data -
منشور في 2025"…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …"
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87
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88
Iteration curve of the optimization process.
منشور في 2025"…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …"
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89
Error of ICESat-2 with respect to airborne data.
منشور في 2024"…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …"
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90
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91
The prediction error of each model.
منشور في 2025"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"
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92
Results for model hyperparameter values.
منشور في 2025"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"
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93
Stability analysis of each model.
منشور في 2025"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"
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94
Robustness Analysis of each model.
منشور في 2025"…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …"
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95
The workflow of the proposed model.
منشور في 2024"…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …"
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96
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100