An evaluation model of e-commerce credit information based on social big dataZhang, Yun
2022 International Journal of Autonomous and Adaptive Communications Systems
doi: 10.1504/ijaacs.2022.127455
To overcome the problems of low accuracy and poor stability in the evaluation of Internet trading activities, an evaluation model of e-commerce credit information based on social big data is proposed. The model will be composed of four layers: basic data layer, synthetic data layer, random model layer and integrated learning layer. The logical structure of the model is divided into social communication big data preprocessing, credit evaluation submodel establishment, evaluation submodel integration, so as to enhance the ability of the credit division model. On this basis, the credit evaluation index system is established, and the e-commerce credit information is evaluated by the BP neural network method. The results of model verification show that the model has good generalisation ability and accuracy, can distinguish important variables effectively and stably, can acquire the e-commerce credit situation more scientifically, and can control the security situation of e-commerce credit information in the social big data environment.
Research on coverage holes repair in wireless sensor networks based on an improved artificial fish swarm algorithmDongliang, Li
2022 International Journal of Autonomous and Adaptive Communications Systems
doi: 10.1504/ijaacs.2022.127412
In a wireless sensor network (WSN), holes are formed when nodes become invalid. To resolve this problem, holes repairing algorithm based on fish swarm optimisation in wireless sensor network (HRFSO) is proposed in this paper. In the algorithm, network coverage is served as the objective function, and the biological behaviours of artificial fish are used to simulate node movements. The new actions of jumping and survival of the fittest are defined besides foraging, rear-ending and grouping to improve the convergence of optimisation. Self-adaptive vision and step length are used when updating the status of artificial fish. Failed holes are repaired by moving the sensor node with the shortest distance. The simulation results show that the algorithm is suitable for repairing holes with fast speed by moving fewer nodes. It can increase WSN coverage with better repairing results, faster convergence, higher accuracy, efficiency, and robustness. The results also show the lifetime of the network can be prolonged.
An adaptive prediction model for sparse data forecastingYao, Xuan
2022 International Journal of Autonomous and Adaptive Communications Systems
doi: 10.1504/ijaacs.2022.127409
Sparse data generated by the limitations of data acquisition are ubiquitous for prediction. However, the general prediction model is challenging to deal with those sparse data. Therefore, this paper aims to propose an adaptive sparse data prediction model. Firstly, we introduced the aXreme Gradient Boosting (XGBoost) algorithm to build an adaptive prediction model to correct sparse data constantly. Secondly, the sparsity perception of the XGBoost algorithm is used for parallel tree learning. Finally, we applied the model to the PM2.5 concentration forecasting of Nanjing, China. We trained the model and adjusted the parameters to get better prediction results, and compared the prediction results with actual data to prove the feasibility of the model.
A personalised recommendation algorithm for e-commerce network information based on two-dimensional correlationCao, Enwei
2022 International Journal of Autonomous and Adaptive Communications Systems
doi: 10.1504/ijaacs.2022.127411
In view of the poor accuracy and low efficiency of the traditional e-commerce personalised recommendation algorithm, a two-dimensional correlation-based personalised recommendation algorithm for e-commerce network information was proposed. Using two-dimensional correlation, categorise e-commerce user relevancy analysis to measure the personality interests of users in the electronic commerce network, e-commerce project through the Jaccard similarity coefficient, the similarity calculation between the interest spread model was constructed, differentiate the importance of data push grades, and numerical characteristics of e-commerce behaviour are influenced by the importance level, which is calculated by using the sorting result to realise e-commerce personalised recommendation. The experimental results show that the proposed method has high accuracy, diversity and efficiency.
Dynamic acquisition method of a user's implicit information demand based on association rule miningLi, Xiang
2022 International Journal of Autonomous and Adaptive Communications Systems
doi: 10.1504/ijaacs.2022.127410
In order to overcome the problems of low precision and poor recall in the current research results of user demand mining, a dynamic method based on association rule mining is proposed. Using association rules to get user behaviour-related data, analysing user behaviour through the crawler system, using different association strategies according to different businesses, combining user browsing time with a user interest attenuation factor to calculate user interest, and building a user dynamic interest model. Based on the analysis of user interest, in the initial stage of mining, support and trust are input, respectively, and an association rule mining algorithm is called to realise the dynamic mining of user implicit information demand. The experimental results show that the mining accuracy and recall rate of this method are higher than 95%, and the whole method has strong scalability and practicality.