Improving Customer Value Index and Consumption Forecasts using a Weighted RFM Model and Machine Learning Algorithms

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

Analysis of Driving Control Characteristics in Typical Road Types

Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained.

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

Construction and Application of the Evaluation System of Natural Resources Asset Accountability Audit of Officials: A Case Study of Jiangxi, China

At the Third Plenary Session of the 18th Central Committee of the Communist Party of China, the Central Committee first proposed the establishment of the Natural Resources Asset Accountability Audit of Officials, which not only marked a new stage in China’s ecological construction, but also triggered many scholars’ discussion on the accountability audit evaluation indicator system of natural resources assets. In this paper, the combination of entropy weight method and TOPSIS method is introduced into the natural resource asset accountability audit evaluation system for the first time, and a system based on energy subsystem, economy subsystem and environment subsystem is constructed. The system is used to evaluate the performance of leading officials of Jiangxi Province, one of the first batch of national pilot zones for ecological conservation in China, in the responsibility of natural resource asset management from 2015 to 2019. The evaluation result indicates that the overall situation of natural resource assets in Jiangxi Province shows a steady upward trend, and the overall performance should be affirmed. The practical application of this evaluation system in the national pilot zone for ecological conservation will enlighten other provinces in China to improve the leading officials’ accountability audit of natural resource assets.

Research on maintenance method of relay protection state based on combination weight and gray relation

Relatively independent evaluation parameters are selected from many parameters through pedigree clustering.Learning the analytic hierarchy process (ahp) and entropy weight method can determine the weight, and at the same time to understand the error of the analytic hierarchy process (ahp) and entropy weight method is large, so the combination of the subjective and objective weight obtained by the two methods, using the improved entropy weight-ahp method to determine the weight. The improved weight calculation method has a clear hierarchical structure, which not only considers the influence of subjective and objective factors, but also makes full use of the weight information in the hierarchical structure. Considering the uncertainty of information, gray relation is adopted to deal with the data, so as to make maintenance rules.

Optimizing the distribution of food based on the vogel method

Currently, numerous people still suffer from hungry and food insecurity. To solve the problem of production and distribution of food, we firstly set up a quantitative examination on food system from four dimensions, including equitability, efficiency, stability, and sustainability. As we choose 2 to 4 indexes for each dimension, we applied entropy weight method to decide the weight of each index. Secondly, we used BP neural network by MATLAB to simulate the rising trend of population based on the data of population in recent years, and predicted the population until 2030, which is about 8.5 million. In the same way, we forecast that food yield will maintain at 1.63*109 litres approximately till 2030 in the whole world. With certain assumptions, we set a model about Transportation Problem, which help us optimize the food transportation system at present. We use the Vogel method to calculate the results and chose 9 ports for exporting countries and 10 ports for importing countries. After that, we calculate the minimum costs of transportation all over the world is $4979628112343. At the end of our paper, we make sensitively analysis, the result of which proves that our model has a good stability.