Search alternatives:
access optimization » process optimization (Expand Search), stress optimization (Expand Search), process optimisation (Expand Search)
based optimization » whale optimization (Expand Search)
primary data » primary care (Expand Search)
binary data » dietary data (Expand Search)
data access » data across (Expand Search), water access (Expand Search)
data based » data used (Expand Search)
access optimization » process optimization (Expand Search), stress optimization (Expand Search), process optimisation (Expand Search)
based optimization » whale optimization (Expand Search)
primary data » primary care (Expand Search)
binary data » dietary data (Expand Search)
data access » data across (Expand Search), water access (Expand Search)
data based » data used (Expand Search)
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141
VIF analysis results for hazard-causing factors.
Published 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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142
Benchmark function information.
Published 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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143
Geographical distribution of the study area.
Published 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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144
Results for model hyperparameter values.
Published 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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145
Flow chart of this study.
Published 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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146
Stability analysis of each model.
Published 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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147
Robustness Analysis of each model.
Published 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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148
Proposed method approach.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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149
LSTM model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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150
Descriptive statistics.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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151
CNN-LSTM Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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152
MLP Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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153
RNN Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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154
CNN Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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155
Bi-directional LSTM Model performance.
Published 2024“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
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156
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157
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158
Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
Published 2024“…The simulation process of the DLCM involves initializing SOC stocks with spatially detailed baseline data, adding organic matter inputs based on vegetation production, and simulating microbial decomposition while adjusting for climate variables such as temperature and soil moisture. …”
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159
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160
Predicting the Shear Viscosity of Carbonated Aqueous Amine Solutions and Their Blends by Using an Artificial Neural Network Model
Published 2020“…A total of 1682 amine + CO<sub>2</sub> + water viscosity data sets for primary, secondary, and tertiary amines and 220 data points for further accuracy examinations were used. …”