To address challenges in stock price prediction including data nonlinearity and anomalies, we propose a hybrid CNN-BiGRU-AM framework integrated with deep learning-based anomaly detection. First, an anomaly detection module identifies irregularities in stock price data. The CNN component then extracts local features while filtering anomalous information, followed by nonlinear pattern modeling through BiGRU with attention mechanisms. Final predictions undergo secondary anomaly screening to ensure reliability. Experimental evaluation on Shanghai Composite (SSE) daily closing prices demonstrates superior performance with $R^2$ = 0.9903, RMSE = 22.027, MAE = 19.043, and a Sharpe Ratio of 0.65. It is noteworthy that the MAE of this model is reduced by 14.7%, and the RMSE is decreased by 7.7% compared to its ablation model. The framework achieves multi-level feature extraction through convolutional operations and bidirectional temporal modeling, effectively enhancing model generalization via nonlinear mapping and anomaly correction. Comparative Sharpe Ratio analysis across models provides practical insights for investment decision-making. This dual-functional system not only improves prediction accuracy but also offers interpretable references for market mechanism analysis and regulatory policy formulation.

As a critical fastener connecting steel rails, fish-tail bolts ensure the safety of railway transportation. To improve the efficiency of fish-tail bolt loosening detection, this paper proposes a computer vision-based method for detecting fish-tail bolt looseness under tilted perspectives. The method first identifies bolt positions and coordinates of corner points on rail clamp edges through object detection and key point detection. Then, considering diverse rail clamp dimensions and combining with bolt positions, it employs dual perspective transformations for image rectification. Finally, utilizing the Lightweight OpenPose network, angle recognition of key bolt edges is achieved through Gaussian ring-shaped smooth labels, with loosening determination made by comparing angular variations across temporal frames. In experimental validation, tests were first conducted on a public dial-reading dataset for pointer angle recognition, showing a minimum average error of only 0.8°, which verifies the algorithm’s feasibility. Subsequently, based on fish-tail bolt images captured under various tilted perspectives, we constructed a self-made dataset of bolt key edges and performed loosening detection experiments. For bolt images in boundary postures, after rotation preprocessing, the average detection error was reduced to 0.7°. When the loosening threshold was set to 2.1°, the detection accuracy reached 97%. Experimental results indicate that the proposed method effectively identifies fish bolt loosening, providing crucial technical reference for railway safety maintenance.