In order to reduce the risk of non-performing loans, losses, and improve the loan approval efficiency, it is necessary to establish an intelligent loan risk and approval prediction system. A hybrid deep learning model with 1DCNN-attention network and the enhanced preprocessing techniques is proposed for loan approval prediction. Our proposed model consists of the enhanced data preprocessing and stacking of multiple hybrid modules. Initially, the enhanced data preprocessing techniques using a combination of methods such as standardization, SMOTE oversampling, feature construction, recursive feature elimination (RFE), information value (IV) and principal component analysis (PCA), which not only eliminates the effects of data jitter and non-equilibrium, but also removes redundant features while improving the representation of features. Subsequently, a hybrid module that combines a 1DCNN with an attention mechanism is proposed to extract local and global spatio-temporal features. Finally, the comprehensive experiments conducted validate that the proposed model surpasses state-of-the-art baseline models across various performance metrics, including accuracy, precision, recall, F1 score, and AUC. Our proposed model helps to automate the loan approval process and provides scientific guidance to financial institutions for loan risk control.
Objective: To explore the pre-treatment and efficacy analysis of comprehensive anti-inflammatory treatment for lymphedema in patients with irritating contact dermatitis. Method: Convenience sampling method was used to observe the skin of 160 patients with upper limb lymphedema admitted to the lymphedema outpatient department of our hospital. They were divided into an observation group (80 cases) and a control group (80 cases), and both groups received a course of comprehensive anti-inflammatory treatment (20 treatments). The control group received routine skin care;On the basis of the control group, the observation group received pre-treatment of the affected limb skin: Laofuzi herbal ointment was applied externally to the prone areas of irritating contact dermatitis (such as the upper arm, inner forearm, and cubital fossa). Result: The incidence of irritating contact dermatitis in the observation group was significantly lower than that in the control group (P 0.05). Patients in the observation group felt significantly better in terms of comfort, skin moisture, and itching relief after being wrapped with low elasticity bandages than those in the control group (P Conclusion: Preventive treatment can effectively reduce the incidence of irritating contact dermatitis, prolong the time of stress treatment, thereby increasing efficacy and improving patient compliance.
Qiaoling ZhongFeng LiuHuiting ZhangLiping ZhangJinlan LiLijuan ZhangNa LiQinghua Luo
The rail surface status image is affected by the noise in the shooting environment and contains a large amount of interference information, which increases the difficulty of rail surface status identification. In order to solve this problem, a preprocessing method for the rail surface state image is proposed. The preprocessing process mainly includes image graying, image denoising, image geometric correction, image extraction, data amplification, and finally building the rail surface image database. The experimental results show that this method can efficiently complete image processing, facilitate feature extraction of rail surface status images, and improve rail surface status recognition accuracy.