Metabolic syndrome (MetS) has a high prevalence and is prone to many complications. However, current MetS diagnostic methods require blood tests that are not conducive to self-testing, so a user-friendly and accurate method for predicting MetS is needed to facilitate early detection and treatment. In this study, a MetS prediction model based on a simple, small number of Traditional Chinese Medicine (TCM) clinical indicators and biological indicators combined with machine learning algorithms is investigated. Electronic medical record data from 2040 patients who visited outpatient clinics at Guangdong Chinese medicine hospitals from 2020 to 2021 were used to investigate the fusion of Bayesian optimization (BO) and eXtreme gradient boosting (XGBoost) in order to create a BO-XGBoost model for screening nineteen key features in three categories: individual bio-information, TCM indicators, and TCM habits that influence MetS prediction. Subsequently, the predictive diagnostic model for MetS was developed. The experimental results revealed that the model proposed in this paper achieved values of 93.35 %, 90.67 %, 80.40 %, and 0.920 for the F1, sensitivity, FRS, and AUC metrics, respectively. These values outperformed those of the seven other tested machine learning models. Finally, this study developed an intelligent prediction application for MetS based on the proposed model, which can be utilized by ordinary users to perform self-diagnosis through a web-based questionnaire, thereby accomplishing the objective of early detection and intervention for MetS.

This study proposes Combining Attention and Brightness Adjustment Network (CABA-Net), a deep learning network for underwater image restoration, to address the issues of underwater image color-cast, low brightness, and low contrast. The proposed approach achieves a multi-branch ambient light estimation by extracting the features of different levels of underwater images to achieve accurate estimates of the ambient light. Additionally, an encoder-decoder transmission map estimation module is designed to combine spatial attention structures that can extract the different layers of underwater images’ spatial features to achieve accurate transmission map estimates. Then, the transmission map and precisely predicted ambient light were included in the underwater image formation model to achieve a preliminary restoration of underwater images. HSV brightness adjustment was conducted by combining the channel and spatial attention to the initial underwater image to complete the final underwater image restoration. Experimental results on the Underwater Image Enhancement Benchmark (UIEB) and Real-world Underwater Image Enhancement (RUIE) datasets show excellent performance of the proposed method in subjective comparisons and objective assessments. Furthermore, several ablation studies are conducted to understand the effect of each network component and prove the effectiveness of the suggested approach.

Tongue diagnosis holds significant importance in Traditional Chinese Medicine (TCM), with cracked tongues serving as a key diagnostic feature. However, the considerable variability in the morphology, depth, and distribution of tongue cracks poses a challenge for accurate extraction. In this paper, a novel deep learning approach is proposed to enhance the decoder of the U-Net model for cracked tongue extraction by incorporating the Hybrid Parallel Attention Mechanism (HPAM). The inclusion of HPAM enables the model to better concentrate on the small-scale feature information of tongue cracks, thereby improving the accuracy of crack segmentation. Experimental results demonstrate the effectiveness of the proposed method across all three tongue crack datasets. The method achieves a MIoU of 69.31% on the open environment dataset, 76.05% MIoU on the non-open environment dataset, and an overall MIoU of 76.92% on the combined dataset. These results signify a significant improvement over existing methods. This study not only offers an effective approach for automating the extraction of cracked tongues but also contributes to the automation and accuracy of tongue diagnosis, thereby benefiting the field of TCM.

For realizing the good perception function of the human lower limbs and improve the quality of human-machine coordination, a method of estimating the continuous movement of the human lower limbs based on sEMG is proposed. Five kinds of time, frequency and time domain characteristic parameters of 8 signals are extracted, and the BP is used to estimate the continuous movement of the lower limbs of the human body. For the traditional neural network, the initial weights and thresholds are set for empirical values, resulting in a relatively high prediction accuracy. Poor, this paper proposes a neural network continuous motion estimation method based on particle swarm optimization. After the experiment, the overall average rmse of the angle estimates of each movement after optimization is optimized from 0.49rad to 0.30rad, and the overall average correlation coefficient is optimized from 0.87 to 0.89.