بدائل البحث:
feature optimization » resource optimization (توسيع البحث), feature elimination (توسيع البحث), structure optimization (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
input feature » input features (توسيع البحث)
binary input » binary depot (توسيع البحث)
primary data » primary care (توسيع البحث)
data model » data models (توسيع البحث)
feature optimization » resource optimization (توسيع البحث), feature elimination (توسيع البحث), structure optimization (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
input feature » input features (توسيع البحث)
binary input » binary depot (توسيع البحث)
primary data » primary care (توسيع البحث)
data model » data models (توسيع البحث)
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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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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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Table_1_Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique.DOCX
منشور في 2023"…The obtained features were optimized by using correlation and the mRMR-based algorithm. …"
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65
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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66
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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67
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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68
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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69
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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