Ming Li¹, Zhong Fang², Wanwan Cao¹, Yong Ma^1,*, Shang Wu¹, Yang Guo¹, Yu Xue³, Romany F. Mansour⁴

Computer Systems Science and Engineering, Vol.38, No.1, pp. 39-46, 2021, DOI:10.32604/csse.2021.016189 - 01 April 2021

Abstract With the rapid development and popularization of new-generation technologies such as cloud computing, big data, and artificial intelligence, the construction of smart grids has become more diversified. Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents, which is essential to ensure the normal operation of the power system, energy management and planning. Based on the distributed architecture of cloud computing, this paper designs an improved random forest residential electricity classification method. It uses the unique out-of-bag error of random More >

Haoliang Cui¹, Shaozhang Niu¹, Keyue Li^1,*, Chengjie Shi², Shuai Shao³, Zhenguang Gao⁴

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 277-291, 2021, DOI:10.32604/iasc.2021.016408 - 17 March 2021

Abstract At present, the research on the classification of e-commerce users is relatively mature, but with the rise of mobile social networks, the combination of social networks and e-commerce networks has become a trend and is developing rapidly. Traditional e-commerce user classification methods are not suitable for social e-commerce users. Therefore, based on the research on traditional e-commerce user classification methods, according to the characteristics of social e-commerce users, we improved data preprocessing and parameter tuning methods, and proposed a clustering method of social e-commerce users based on the K-means++ algorithm. The test on the actual More >