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2023 1

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欺诈侦测；移动支付；银行卡绑定；移动设备；梯度增强决策树（GBDT）；XGBoost 1

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Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm

《结构与土木工程前沿（英文）》 2023年第17卷第12期页码 1777-1795 doi: 10.1007/s11709-023-0044-4

摘要： Real-time perception of rock conditions based on continuously collected data to meet the requirements of continuous Tunnel Boring Machine (TBM) construction presents a critical challenge that warrants increased attention. To achieve this goal, this paper establishes real-time prediction models for fractured and weak rock mass by comparing 6 different algorithms using real-time data collected by the TBM. The models are optimized in terms of selecting metric, selecting input features, and processing imbalanced data. The results demonstrate the following points. (1) The Youden’s index and area under the ROC curve (AUC) are the most appropriate performance metrics, and the XGBoost Random Forest (XGBRF) algorithm exhibits superior prediction and generalization performance. (2) The duration of the TBM loading phase is short, usually within a few minutes after the disc cutter contacts the tunnel face. A model based on the features during the loading phase has a miss rate of 21.8%, indicating that it can meet the early warning needs of TBM construction well. As the TBM continues to operate, the inclusion of features calculated from subsequent data collection can continuously correct the results of the real-time prediction model, ultimately reducing the miss rate to 16.1%. (3) Resampling the imbalanced data set can effectively improve the prediction by the model, while the XGBRF algorithm has certain advantages in dealing with the imbalanced data issue. When the model gives an alarm, the TBM operator and on-site engineer can be reminded and take some necessary measures for avoiding potential tunnel collapse. The real-time predication model can be a useful tool to increase the safety of TBM excavation.

关键词： Tunnel Boring Machine fractured and weak rock mass machine learning model real-time early warming tunnel face rock condition

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年第16卷第3期页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction