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According to the real-time and accuracy requirements of obtaining passenger flow by surveillance videos in scenic spot, a model based on deep learning is proposed. Aided by Yolov4 and Deep Sort, passenger flow is counted by detection and tracking tourists. Aiming at the real-time requirement, the model compression method is used to replace the backbone network of Yolov4 with lightweight network mobileNetv3 to improve the detection speed. For the accuracy of the model, a detection scale is used to Yolov4 for extracting shallow features and the features are concatenated with deep features. Furthermore, Soft-NMS is used to process the detection results. The purpose of these improvements is to solve the dense tourists and small target problems in the surveillance videos. Then Deep Sort tracks the tourists target and obtains passenger flow information in the scenic spot. Through the experiment of the model in the surveillance videos, it is verified that this model meets the real-time requirements and has high accuracy in passenger flow statistics.

Aim at the study of the relationship of correlation between the stocks of SSE(Shanghai Stock Exchange) companies and the movement of SSE complex index, by using the mathematical statistics method and correlation coefficient analysis, select each stock's daily closing price as object, establish the complex network of stocks. Through preprocessing the data sets, we used R Studio get the mean correlation coefficient matrix, which is the foundation of the financial network. The results confirm the utility of the correlation coefficient analysis as a stability indicator for SSE stock market, since it proves that the correlation of stocks is different according to the crisis and prosperous of stock market in times. Its development is opposite to the market index, the interaction between the stocks attenuates when the complex index rises and enhances when the index falls. We also used K-means clustering algorithm to classify these nodes into different communities and found that the structures of networks vary widely according to their correlations. The method of this paper and the model it proposed is not only for the selection of our data sets, but also can be generalized to other fields of research.

Nowadays, artificial intelligence is frequently used to various fields including medicine, chemistry and forecasting. In this paper, artificial intelligence is applied to network traffic prediction. Due to that network traffic prediction plays an important role in network management, planning, traffic congestion control and traffic engineering. Seeking for more accurate network traffic prediction techniques, this paper proposed a new hybrid method (SPLSSVM) which based on seasonal adjustment (SA) and least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO) to predict network traffic. The proposed method is examined by using the network traffic data from Lanzhou University. Empirical testing indicates that the proposed method can provide more accurate and effective results than the other forecasting methods.

The stock market has the huge effect and influence on a country or region’s economic and financial activities. But we have found that it is very hard for the prediction and control. This illustrates a critical need for new and fundamental understanding of the structure and dynamics of stock markets. Previous research and analysis on stock markets often focused on some assumptions of the game of competition and cooperation. Under the condition of these assumptions, the conclusions often reflect just part of the problem. The stock price is the core reflections of a stock market. So, in this paper, the authors introduce a methodology for constructing stock networks based on stock prices in a stock market and detecting dynamic communities in it. This strategy will help us from a new macroperspective to explore and mine the characteristics and laws hiding in the big data of stock markets. Through statistical analysis of many characteristics of dynamic communities, some interesting phenomena are found in this paper. These results are new findings in finance data analysis field and will potentially contribute to the analysis and decision-making of a financial market. The method presented in this paper can also be used to analyze other similar financial systems.