Evaluation of SMEs’ Credit Decision Based on Support Vector Machine-Logistics Regression

The study of the Yangtze River Delta urban agglomeration provides a reference for regional construction. We set up a suitable cluster model and a comprehensive evaluation model to evaluate the urban economic development and provide the basis for the government to formulate and adjust the economic measures. We use the heat map of correlation coefficient to eliminate the economic indicators that are less correlated with per capita GDP and use grey correlation analysis to detect the degree of correlation of economic indicators. The optimal method is determined by comparing the results of K-means clustering and fuzzy C-means clustering. In addition, we use principal component analysis to rank the status of urban economic development. The results show that the relationship between the added value of primary industry, the added value of agriculture, forestry, animal husbandry and fishery, and per capita GDP is close to 0. Regional development level is not balanced. Chizhou, Xuancheng, Bengbu, and other cities lag behind in the economic rankings. Finally, it is suggested to reform the laggard cities and try to improve their economic strength from various economic indicators.

Section: Analysis Of Resultsmentioning

confidence: 99%

Comprehensive Evaluation of Urban Economic Development in Yangtze River Delta Based on Cluster‐Principal Component Analysis

Quan

Dong

et al. 2021

“…In order to obtain the future trend of the development of higher education in various countries, we need to establish a reasonable prediction model. At present, the mainstream prediction models are ARIMA, neural network model, etc., but these prediction methods often need a large number of samples as a prerequisite [27,28]. In this study, the longitudinal length of panel data is only 20 years, which is difficult to meet the data requirements of ARIMA and other models.…”

Section: Extended Grey Prediction Modelmentioning

confidence: 97%

Research on Evaluation and Improvement Policy of Higher Education Development Based on TOPSIS‐Synthetic Control Method

Zhang

Mengxiang

et al. 2021

Self Cite

This article aims at the evaluation and improvement of the development of higher education. First of all, according to the theory and hypothesis, the comprehensive evaluation index system of higher education development level is established. The TOPSIS and the combination weighting method based on goal programming are used to measure the scores of higher education development levels of 19 major countries in the world in the recent 20 years, and the horizontal and vertical comparative analysis is carried out. Then, according to the practical feasibility, Turkey is selected as the target country to propose a higher education development and improvement policy by 2030, and the causal effect of our proposed policy is estimated using the synthetic control method. This study can provide a reference for other countries in the world to improve their higher education level.

“…After this, inputting it into the neural network. Secondly, the neuron transfer functions of the hidden layer and the output layer are constructed using the tangent function of the output range [-1,1], and the LM method was chosen as the training algorithm of the BP network; the neural network training is completed [36][37][38][39][40][41]. e specific training process is as follows:…”

Section: Model Algorithm and Principlementioning

confidence: 99%

Research on the Route Pricing Optimization Model of the Car‐Free Carrier Platform Based on the BP Neural Network Algorithm

2021

The car-free carrier platform is a product of the rapid development of the modern logistics industry and has a vital strategic value for promoting the construction of a country’s comprehensive transportation. However, due to the unreasonable platform pricing model, the industry is currently in a bottleneck period. In order to solve this problem, we established a gray correlation model to calculate the degree of correlation between each characteristic index and platform pricing based on the massive historical transaction data of a certain platform and performed K-means clustering on the results to discover the main factors affecting platform pricing. Based on the abovementioned results, we created a pricing optimization model based on the BP neural network, with the structure of 8-13-1 to predict the freight pricing of the order and test the prediction results. The test shows that the goodness of fit (R2) of the predicted value is close to 1, and the prediction error range is less than 3.7%, which proves the accuracy and effectiveness of the BP neural network model and provides an effective reference for the optimization of the pricing model of the car-free carrier platform.