Pedestrian detection has important research value and practical significance. It has been used in intelligent monitoring, intelligent transportation, intelligent therapy, and automatic driving. However, in the pixel-level fusion and the feature-level fusion of visible light images and thermal infrared images under shadows during the daytime or under low illumination at night in actual surveillance, missed and false pedestrian detection always occurs. To solve this problem, an algorithm for the pedestrian detection based on the two-stage fusion of visible light images and thermal infrared images is proposed. In this algorithm, in view of the difference and complementarity of visible light images and thermal infrared images, these two types of images are subjected to pixel-level fusion and feature-level fusion according to the varying daytime conditions. In the pixel-level fusion stage, the thermal infrared image, after being brightness enhanced, is fused with the visible image. The obtained pixel-level fusion image contains the information critical for accurate pedestrian detection. In the feature-level fusion stage, in the daytime, the previous pixel-level fusion image is fused with the visible light image; meanwhile, under low illumination at night, the previous pixel-level fusion image is fused with the thermal infrared image. According to the experimental results, the proposed algorithm accurately detects pedestrian under shadows during the daytime and low illumination at night, thereby improving the accuracy of the pedestrian detection and reducing the missed rate and false rate in the detection of pedestrians.

The automated classification of clinical diagnoses in electronic health records (EHRs) is critical for enhancing clinical decision-making and enabling large-scale medical research, yet existing methods struggle with heterogeneous data structures and limited annotated datasets. Current approaches fail to adequately address the dual challenges of extracting contextual medical semantics from unstructured clinical narratives while maintaining generalizability across institutions with divergent documentation practices. This study proposes a novel framework integrating three core components: a Transformer-based architecture for hierarchical feature extraction from clinical text, a multi-task learning paradigm leveraging diagnostic interdependencies, and transfer learning initialization using pretrained medical language models. Evaluation on the MIMIC-III dataset demonstrates state-of-the-art performance with 89.2% accuracy and 87.6% F1-score, outperforming conventional CNN-RNN hybrids by 8.0% in recall and showing 4.9-6.2% improvements over ablated configurations in critical metrics. The results establish that synergistic integration of contextual attention mechanisms, cross-task knowledge sharing, and medical domain adaptation effectively addresses EHR heterogeneity while reducing reliance on institution-specific annotations, providing a robust foundation for clinical decision support systems that balance accuracy with real-world implementability across diverse healthcare environments.

Exposure problems often affect image quality more significantly than noise and blurring, seriously affecting their effectiveness in computer vision applications. To address this core problem, this paper proposes an innovative deep-learning method. The method uses pyramidal multi-scale decomposition to repair the image layer by layer, handling both overexposure and underexposure problems. Firstly, we introduce the Strengthen-Operate-Subtract (SOS) feature enhancement module, which refines and enhances the image by building previously estimated images to make the exposure correction effect more pronounced. Additionally, we propose using the Multi-scale Aggregation Back-projection (MAB) module to improve the U-Net model. Through the error feedback mechanism, the features of non-adjacent layers are compensated and integrated effectively, and the loss of detailed information is reduced. We introduce a dense block of matte residuals at the maximum scale to expand the network's receptive field and effectively capture local information. These innovations significantly enhance image exposure correction, reducing detail loss, regional contrast saturation, and color imbalance. Extensive evaluations confirm the effectiveness of our model, which demonstrates superior performance across all datasets, particularly in PSNR, achieving an average value of 26.271.