Since the reform and opening up, there has been a great demand vacancy in the market. The development of green finance can just meet many market demands. Let it stand out among many financing schemes. As an innovative financial model that can effectively promote the global economy, green finance is of great significance to the global ecological economy and environmental state. In this article, we will discuss the economic coupling relationship between environmental quality and green finance. Because we lack a relatively perfect economic evaluation system, therefore we integrate green finance and environmental quality indicators, introduce them into relevant and suitable evaluation system indicators, and mainly analyze and judge based on the grey relational analysis model. The results show the following: (1) investigate the environmental quality and green financial comprehensive benefit development index of A, B, and C cities, judge the lagging types of the three cities, and evaluate the specific development status of the cities. (2) Collect data and calculate the coupling degree and coordination degree of cities. Their coupling levels and coordination types are summarized, respectively. (3) The higher the correlation between urban environmental quality and green financial indicators, the closer the relationship between the two indicators and the greater the mutual influence. (4) Sort them by grey relational degree. The correlation between Y1 and Z3 is the least, which is 0.55 and 0.552, respectively. Y3 can be correlated up to 0.655. The results of this paper are very good, and we get the result that green finance can promote the effective improvement of environmental quality.
To improve the ability of market to avoid and prevent credit risk and strengthen the awareness of market risk early warning, SMOTE is used to process the unbalanced sample, and fruit fly optimization algorithm (FOA) is utilized to optimize the parameters of support vector machine (SVM), and thus an improved SVM market risk early warning model is proposed. The simulation results show that the proposed model has excellent stability and generalization ability, and it can predict market credit risk accurately. Compared with the prediction model based on FOA-SMOTE-BP and FOA-SMOTE-Logit, the proposed model performs better on the indicators of G value, F value, and AUC value, which provides a reference for market credit risk prediction.
Data mining algorithms combine expertise in machine algorithm learning, software modeling pattern recognition, statistical analysis principles, database construction, and artificial intelligence. With the rapid development of Internet technology and the common application of cell phones, mobile medical, a new medical method based on this technology, has been spawned, which greatly facilitates multiple aspects of medical services such as doctor diagnosis, patient treatment, disease care, and health management of critically ill patients and also alleviates the imbalance of medical resources. This paper firstly starts from the background of rapid development of information technology and mobile technology, combines the theoretical knowledge of data mining algorithm and mobile medical, as well as previous research reviews, and presents the main research content of this paper: analysis of factors influencing the development of mobile medical innovation based on data mining algorithm. Based on the K-means algorithm in the data mining algorithm and the Apriori algorithm in the association algorithm, this paper analyzes the current situation and problems of mobile medical development in China based on the algorithm model, analyzes the influencing factors of mobile medical innovation development in China based on the algorithm model, and summarizes and concludes the influencing factors of mobile medical innovation development and concludes that there are four categories of mHealth innovation development influencing factors: demand influence, policy orientation, technological innovation, and capital injection.
Abstract. To figure out water demands of Billings in the future 30 years, some factor which are related to the water demand are selected to predict .Assume there are no extra factors to cause great changes in the predictions, we depict a prediction figure with the data of the GDP, population, and the personal income. Regression analyses are used to make a prediction with curve fitting model.
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