An Application of Deep Belief Networks in Early Warning for Cerebrovascular Disease Risk

To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.

Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

Study on the BL Early Warning Model of Transmission Line Tower Based on KNN-SVM Algorithm

With the expansion of China’s power grid construction scale, the transmission line span are gradually improved, which also increases the risk of BL stroke on the transmission line. However, the traditional passive BL protection has many problems, such as weak pertinence and high investment cost, which can not meet the needs of social development. KNN can well describe the similarity measure between the two, which can effectively reduce the training samples. SVM can find the best compromise between model complexity and learning ability in small samples, which is a good sample training method. Through KNN - in-depth learning of the historical data of BL activities accumulated in the power grid, a supervised BL early warning model (hereinafter referred to as EWM) of transmission line can be trained. At the same time, the BL strike of transmission line tower (hereinafter referred to as TLT) has complex meteorological conditions, which requires comprehensive confirmation of various monitoring point parameters. Therefore, it is of great significance to study the BL EWM of TLT based on KNN-SVM algorithm. Firstly, this paper analyzes the KNN-SVM algorithm. Then, this paper establishes an EWM. Finally, this paper is verified.

Study on the Uncertainty of Critical Rainfall for Flash Floods in Small Watersheds Based on the Random Rainfall Pattern

Flash floods cause great harm to people's lives and property safety. Rainfall is the key factor which induces flash floods, and critical rainfall (CR) is the most widely used indicator in flash flood early warning systems. Due to the randomness of rainfall, the CR has great uncertainty, which causes missed alarms when predicting flash floods. To improve the early warning accuracy for flash floods, a random rainfall pattern (RRP) generation method based on control parameters, including the comprehensive peak position coefficient (CPPC) and comprehensive peak ratio (CPR), is proposed and an early warning model with dynamic correction based on RRP identification is established. The rainfall-runoff process is simulated by the HEC-HMS hydrological model, and the CR threshold space corresponding to the RRP set is calculated based on the trial algorithm. Xinxian, a small watershed located in Henan Province, China, is taken as the case study. The results show that the method for generating the RRP is practical and simple, and it effectively reflects the CR uncertainty caused by the rainfall pattern uncertainty. The HEC-HMS model is proved to have good application performance in the Xinxian watershed. Through sensitivity analysis, the effect of the antecedent soil moisture condition, CPPC, and CPR are compared. The proposed early warning model is practical and effective, which increases the forecast lead time.

Evaluate the sustainable reuse strategy of the corporate financial management based on the big data model

PurposeThe purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.Design/methodology/approachFirst, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.FindingsThe experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.Originality/valueThe obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.

Financial Risk Analysis and Early Warning Research Based on Data Mining and Statistical Analysis Technology

Using the advantage of decision tree algorithm in the screening work, the traditional ID3 algorithm is improved and optimized, and a new and simplified financial index system is constructed. At the same time, combined with the unique value of artificial neural network in early warning model and data analysis, B-P model is used to build a mixed financial early warning model. In the model study, the HFPM model and Z-score model were compared and analyzed by using test samples and training samples, and the superior warning ability of the former was effectively verified.

Intelligent Informatization Early Warning Analysis of Agricultural Economy Based on Support Vector Sequential Regression Model

The development of science and technology has laid a solid foundation for the economic informatization of agriculture, and at the same time it brought technical guarantee for the development of agriculture, and the development of agriculture has provided an important material foundation for the development of science and technology. How to identify and deeply study agricultural economic informatization, give early warning to risk information, and ensure the steady development of the whole industry has become a key issue in the application of Internet technology in the field of agricultural development. This paper studies the present situation of agricultural economy informatization development process and applies support vector machine to forecast regional economic development level. The warning limit of agricultural economic growth rate is obtained on the basis of warning situation and warning indicator in early warning index system. The economic early warning model is established based on the support vector sequential regression method, and then the data is trained by MATLAB software to verify the rationality of the early warning model, and the accuracy and corresponding error of the model are given. Experimental results show that the prediction accuracy is 99.3%, the error is less than 0.05, and the prediction effect is relatively ideal, for agricultural economic intelligence information to provide accurate warning and agricultural economic research agricultural commercial development to provide support.