Aiming to address challenges in SAR image ship detection tasks, such as the dense concentration of inshore ships, small target sizes, and large overlapping areas, this paper proposes an improved algorithm LAOBB-YOLOv8n based on YOLOv8n for detecting rotating ships in SAR images. In this model, we propose the Detect_LSCD detection head, which replaces the BatchNorm in the convolutional layers of the Detect module in YOLOv8n with GroupNorm to improve the positioning and classification capabilities of the detection head; Replace the original Spatial Pyramid Pooling-Fast (SPPF) module with the Attention-based Intrascale Feature Interaction (AIFI) module, which focuses on processing advanced image features through self-attention mechanism, thus improving the flexibility and accuracy of the model in processing complex scenes while reducing unnecessary computational consumption; Based on the original network structure, we add a small target detection layer to improve the detection ability for small targets; Use rotating bounding box instead of traditional horizontal bounding box, which can better accommodate the rotating posture of the target and reduce the overlapping areas between detection boxes, thereby improving the accuracy of inshore target detection. The comparative and ablation experiments are conducted on the RSDD-SAR dataset to verify the model’s effectiveness. The experimental results show that the mAP50 of LAOBB-YOLOv8n reaches 98%, which is 1.4% higher than that of YOLOv8n. Although the number of parameters of LAOBB-YOLOv8n is larger than that of YOLOv8n, it is still very small compared with other baseline models, and the accuracy of LAOBB-YOLOv8n is much higher than other models.

Maize, a significant global food crop, is essential in agriculture and the economy. The price of maize futures is affected by many factors, and its data is a nonlinear, unstable, and long-term correlation, so it is difficult to predict it accurately. Accordingly, this paper presents a combined model based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Gated Recurrent Unit (IGRU). EEMD decomposes the maize price data to produce multiple Intrinsic Mode Function (IMF) components and residual sequences. Subsequently, the noisy IMF components are removed, and the remaining IMF components are reconstructed into low, medium, and high frequencies. The CNN is tasked with the extraction of eigenvalues for these components. The IGRU improves in two key respects on the original Gated Recurrent Unit (GRU) model. First, it enhances the update and reset gates. Second, it incorporates the Self-Attention (SA) module. Thereby, the model's predictive capabilities are improved. This study uses the primary maize futures trading data from the Dalian Commodity Exchange as the experimental data. A comparative analysis of the EEMD-CNN-IGRU model with seven baseline models shows that it outperforms other models in all evaluation indexes.

As an important financial investment commodity, the price fluctuations of gold significantly impact the global economy and the stability of the financial market. Therefore, it is of great significance to accurately predict the price of gold futures. This paper presents a novel gold futures price prediction model, PCA-AGRU, based on Principal Component Analysis (PCA) and Adaptive Gated Recurrent Unit (AGRU). PCA is utilized for the dimensionality reduction of data while extracting essential features. AGRU bases on Gated Recurrent Unit (GRU), embeds Self-Attention (SA), and adds an adaptive adjustment mechanism, making the model more effective in capturing long-term dependencies within time series data. This paper uses the international gold futures market data as the experimental dataset, and uses Maximal Information Coefficient (MIC) to analyze the correlation of the influencing factors. The PCA-AGRU model is compared with five prediction models of GRU, SA-GRU, AGRU, PCA-GRU, and PCA-SA-GRU. The experimental results show that the PCA-AGRU model performs best in forecasting gold futures prices.