With the rapid development of information technology, artificial intelligence technology and the financial industry began to deeply integrate up. Algorithmic trading, credit card fraud detection and a series of other new technologies being applied to the financial industry all require a large amount of data support. However, due to the increasing amount of online financial data, it is difficult for the majority of investors and financial industry practitioners to obtain the required information in a timely manner. Entity recognition technology, as the basis of natural language processing, can quickly extract effective information from the massive financial texts and can provide effective help for investors and financial industry practitioners. In this paper, we propose a neural network model based on Bert-BiLSTM-CRF, which is applied to recognize financial entities. Through experimental analysis, the model achieves more than 95% of all indicators. Compared with the conventional model, the model has superior performance.
KEYWORDS: Material fatigue, Eye, Mouth, Video, Detection and tracking algorithms, Video acceleration, Facial recognition systems, Deep learning, Matrices, Instrument modeling
In order to avoid fatigue driving, the driver fatigue detection technology is studied by extracting facial fatigue feature parameters. Use the optimized SSD to extract facial features, use PFLD to detect key points of the face, and detect the key points and spatial attitude angles of the eyes, mouth, and head of the face; calculate the face fatigue feature parameters based on time series The matrix is input to GRU for fatigue driving detection. Compared with other eight methods in the case of low computing power, it has a high accuracy rate and detection speed, which meets the needs of the fatigue driving detection system.
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