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.

DHHFL-MABAC Approach based on DHHFLDM-LCME for Multi-criteria Group Decision Making Involving Sewage Treatment Company Selection

With the continuous acceleration of urban construction and development, ecological governance has become an important part of our green life. Therefore, how to select the appropriate governance company in the complex decision-making environment is very important. A double hierarchy hesitant fuzzy linguistic multi-attributive border approximation area method (DHHFL-MABAC) is proposed in this paper, which is based on distance measure and comprehensive weight. DHHFL-MABAC method not only considers the potential loss, but also has simple calculation and stable results. The double hierarchy hesitant fuzzy linguistic distance measure based on least common multiple (DHHFLDM-LCME) is proposed, which reduces the loss of original information and makes the result more accurate. Binary contrast method is proposed and combined with entropy weight to obtain comprehensive weight, which makes the determined weight more reasonable. Finally, this method is applied to the case selected by the sewage treatment company and proved its effectiveness.

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.

Classification Method and Application of Rock Fracture Ability by Supercritical CO2 Blasting

In order to study the fracture ability classification of rock mass under the cracking action of supercritical CO2 phase transition, based on the classification theory of rock mass in blasting engineering, an analytic hierarchy process (AHP)-entropy weight method (EWM) and the cloud model classification method for rock mass cracking under CO2 phase transformation are proposed. In this method, rock density, rock tensile strength, rock wave impedance, and rock mass integrity coefficient are used as the factors to determine the level of rock mass fracturing, and the evaluation index system of rock mass fracturing is established. Through this evaluation method, the rock mass in a reconstruction project section of Nyingchi, Tibet, is classified and evaluated. The results present that this new classification method of rock mass fracture ability uses AHP–EWM to carry out the weight distribution of the classification index. In addition, it is combined with the cloud model for the classification division, overcoming the traditional classification method fixed with appraisal pattern flaw. Therefore, it has validity and feasibility. According to the characteristics of fracture ability, the rock masses in the area to be rebuilt on the Tibet Highway are divided into grade II, grade III, and grade IV, which provides scientific guidance for the construction of the project.

Evaluation of regional low-carbon circular economy development: a case study in Sichuan province, China

Purpose This paper aims to build a scientific evaluation index system for regional low-carbon circular economic development. Taking Sichuan Province as the empirical research object, the paper evaluates its low-carbon circular economy (LCCE) development level and proposes policy recommendations for climate change improvement based on the evaluation results. Design/methodology/approach This paper, first, built an evaluation index system with 30 indicators within six subsystems, namely, economic development, social progress, energy consumption, low-carbon emissions, carbon sink capacity and environmental carrying capacity. Second, develop an “entropy weight-grey correlation” evaluation method. Finally, from a practical point of view, measure the development level of LCCE in Sichuan Province, China, from 2008 to 2018. Findings It was found that Sichuan LCCE development had a general downward trend from 2008 to 2012 and a steady upward trend from 2012 to 2018; however, the overall level was low. The main factors affecting the LCCE development are lagging energy consumption and environmental carrying capacity subsystem developments. Research limitations/implications This paper puts forward relevant suggestions for improving the development of a low-carbon economy and climate change for the reference of policymakers. Originality/value This paper built an evaluation index system with 30 indicators for regional low carbon circular economic development. The evaluation method of “entropy weight-grey correlation” is used to measure the development level of regional LCCE in Sichuan Province, China.

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.