Fintech is continuously driving the overall upgrade of payment methods. Technologies such as Big Data, the Internet of Things, and Artificial Intelligence continue to be applied in the payment field and significantly impact the payment industry. Based on the fusion of multiple machine-learning models, the problem of identifying potential default credit card customers is investigated in this paper. The customers are mined and classified based on their bill amount, education level, marital status and other characteristic information. The various models predict using the AutoML framework, then fused and optimized by bagging and stacking methods, and the models are evaluated using evaluation metrics such as F1 values. The test results show that the F1 value of the integrated model after multiple stacks reaches 54.3%, which is better than that of a single algorithm.
KEYWORDS: Detection and tracking algorithms, Data modeling, Spine, Neural networks, Magnetic resonance imaging, Target detection, Performance modeling, Medical imaging, Tissues, Head
At present, spinal disease diagnosis mainly relies on manual inspection, which has much problems such as low recognition efficiency and human resources. This paper proposes a spinal disease recognition based on YOLOv5 algorithm analysis target detection model, can help doctors to identify patients more fast and efficient type of illness. While traditional YOLOv5 algorithm adopted by the box testing cannot effectively spinal lesion recognition, so the selection based on the data of the key position, which forecasts task cleverly into the key tasks. And the Head end of YOLOv5 algorithm, loss function and the proportion of the NMS, further improvement makes the model can accurately identify patients with spine illness situation and its type. The experimental results show that YOLOv5 algorithm based on key points regression in spinal MRI image validation test dataset, it concluded that model can correctly identify the disease location and disease types of the average accuracy of 52%, and the key position on the patients correctly identify whether the average accuracy of 87.8%.
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