The structure of the data sample.

<div><p>Coal and gas outburst (CGO) is a complicated natural disaster in underground coal mine production. In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on data augmentation and a neuroevo...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Wenbing Shi (5806160) (author)
مؤلفون آخرون:	Ji Huang (295410) (author), Gaoming Yang (7464977) (author), Shuzhi Su (20760086) (author), Shexiang Jiang (20760089) (author)
منشور في:	2025
الموضوعات:	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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The structure of the data sample.

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