The CGO risk prediction process based on data augmentation and neuroevolution.

The CGO risk prediction process based on data augmentation and neuroevolution.

<p>The CGO risk prediction process based on data augmentation and neuroevolution.</p>

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Bibliographic Details
Main Author:	Wenbing Shi (5806160) (author)
Other Authors:	Ji Huang (295410) (author), Gaoming Yang (7464977) (author), Shuzhi Su (20760086) (author), Shexiang Jiang (20760089) (author)
Published:	2025
Subjects:	Genetics Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified transfer function using pointwise intensity transformation lightweight network architecture constructing smart mines complicated natural disaster initial genome code evolutionary neural network div >< p imbalanced data samples neuroevolution </ p neuroevolution algorithm genome mutation evolutionary generations augmented samples rmse </ mae </ evar </ test set species differentiation sample features results show precision mapping population initialization paper proposes outburst risk optimal phenotype multiple aspects method comparisons insufficient diversity gas outburst fitness evaluation feature parameters data augmentation
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Summary:	<p>The CGO risk prediction process based on data augmentation and neuroevolution.</p>

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