Shiman Zhang

Shiman Zhang, Lakshmikar Reddy Polamreddy, Youshan Zhang

Canine cardiomegaly, marked by an enlarged heart, poses serious health risks if undetected, requiring accurate diagnostic methods. Current detection models often rely on small, poorly annotated datasets and struggle to generalize across diverse imaging conditions, limiting their real-world applicability. To address these issues, we propose a Confident Pseudo-labeled Diffusion Augmentation (CDA) model for identifying canine cardiomegaly. Our approach addresses the challenge of limited high-quality training data by employing diffusion models to generate synthetic X-ray images and annotate Vertebral Heart Score key points, thereby expanding the dataset. We also employ a pseudo-labeling strategy with Monte Carlo Dropout to select high-confidence labels, refine the synthetic dataset, and improve accuracy. Iteratively incorporating these labels enhances the model's performance, overcoming the limitations of existing approaches. Experimental results show that the CDA model outperforms traditional methods, achieving state-of-the-art accuracy in canine cardiomegaly detection. The code implementation is available at https://github.com/Shira7z/CDA.

Shiman Zhang, Jinghan Zhou, Zhoufan Yu et al.

To improve decision-making and planning efficiency in back-end centralized redundant supply chains, this paper proposes a decision model integrating deep learning with intelligent particle swarm optimization. A distributed node deployment model and optimal planning path are constructed for the supply chain network. Deep learning such as convolutional neural networks extracts features from historical data, and linear programming captures high-order statistical features. The model is optimized using fuzzy association rule scheduling and deep reinforcement learning, while neural networks fit dynamic changes. A hybrid mechanism of "deep learning feature extraction - intelligent particle swarm optimization" guides global optimization and selects optimal decisions for adaptive control. Simulations show reduced resource consumption, enhanced spatial planning, and in dynamic environments improved real-time decision adjustment, distribution path optimization, and robust intelligent control.