The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems.

Accurate segmentation of the tongue is fundamental to tongue diagnosis, yet existing methods are predicated on images captured under standard conditions. In practical tongue diagnosis, such controlled environments are not always feasible. This study, therefore, aims to develop a segmentation method capable of effectively isolating the tongue region from complex backgrounds, thereby enhancing the precision and efficiency of tongue diagnosis. Initially, we constructed a novel tongue image segmentation dataset with complex backgrounds, which comprises 1,600 images obtained through data augmentation techniques. Subsequently,We introduceVDAU-Net, an enhanced U-Net-based model for tongue segmentation, featuring three core innovations: (1) the use of VGG16 as the encoder (V) to extract multi-level semantic features; (2) the implementation of a dual-attention mechanism (DA) that integrates Position Convolution Block Attention Module (PCBAM) — focusing on tongue fissures and coating textures — with Shifted Window Attention (SWA) to enhance the continuity of tongue contours; and (3) the realization of cross-scale feature fusion within the U-shaped encoder-decoder framework. Together, these innovations enable pixel-precise tongue segmentation against complex backgrounds. The VDAU-Net demonstrated exceptional performance on our dataset, achieving a Mean Intersection over Union of 96.73%. It effectively captured detailed tongue features and accurately localized the tongue region, even under challenging conditions. The VDAU-Net, by integrating advanced attention mechanisms and transfer learning, can effectively handle complex backgrounds and variable imaging conditions, making it a powerful tool for intelligent tongue diagnosis systems. Future research will focus on expanding the dataset and refining the algorithm to enhance its applicability across diverse environments.

In this article, a new triple-loop recurrent neural network and observer-based two-stage fast terminal sliding mode controller (TRNNO-TFTSMC) is proposed for a quadrotor unmanned aerial vehicle (UAV) with nonlinear dynamics and external disturbances. The two-stage fast terminal sliding mode control scheme is designed to guarantee fast convergence of trajectory tracking within a finite time. In the architecture of the flight control system, the triple-loop recurrent neural network (TRNN) is designed to approximate the nonlinear dynamics, which include system uncertainties and known nominal terms. Furthermore, to mitigate the impact of external disturbances and the approximation error of TRNN on the performance of the control system, an observer is employed. Finally, the closed-loop stability of the quadrotor system is ensured based on Lyapunov theory, and the outperformance of the proposed flight control scheme is clearly demonstrated through a comparative study with other techniques.