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  • Open Access


    A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

    Jintao Fu1, Yong Zhou1,*, Qian Qiu2, Guangwei Xu3, Neng Wan3

    Journal of Quantum Computing, Vol.4, No.3, pp. 147-163, 2022, DOI:10.32604/jqc.2022.038358

    Abstract In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression… More >

  • Open Access


    Research and Practice of Telecommunication User Rating Method Based on Machine Learning

    Qian Tang, Hao Chen, Yifei Wei*

    Journal on Big Data, Vol.4, No.1, pp. 27-39, 2022, DOI:10.32604/jbd.2022.026850

    Abstract The machine learning model has advantages in multi-category credit rating classification. It can replace discriminant analysis based on statistical methods, greatly helping credit rating reduce human interference and improve rating efficiency. Therefore, we use a variety of machine learning algorithms to study the credit rating of telecom users. This paper conducts data understanding and preprocessing on Operator Telecom user data, and matches the user’s characteristics and tags based on the time sliding window method. In order to deal with the deviation caused by the imbalance of multi-category data, the SMOTE oversampling method is used to balance the data. Using the… More >

  • Open Access


    Low-Carbon Efficiency Model Evaluation of China’s Iron and Steel Enterprises Based on Data and Empirical Evidence

    Xuesong Xu, Hongyan Shao, Shengjie Yang*, Rongyuan Chen

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1063-1072, 2020, DOI:10.32604/iasc.2020.010137

    Abstract The aim of this study is to consider the economic, resource, energy and environmental factors in a low-carbon economic efficiency evaluation system and to analyze the factors affecting iron and steel enterprises. A combined data envelopment analysis and Malmquist index model have been used in this paper. We empirically investigate the low-carbon efficiency of the Chinese steel industry using observations of 17 listed enterprises from 2009 to 2013. The results show that the economic efficiency of China’s iron & steel enterprises is generally low. The Malmquist productivity index also shows a decreasing trend. Based on our findings, some policies are… More >

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