Resource management projects in entrepreneurship and retain customer based on big data analysis and artificial intelligence

被引:2
|
作者
Huy P.Q. [1 ]
Shavkatovich S.N. [2 ]
Abdul-Samad Z. [3 ]
Agrawal D.K. [4 ]
Ashifa K.M. [5 ]
Arumugam M. [6 ]
机构
[1] Department of Corporate Finance and Securities, Tashkent Institute of Finance, Tashkent
[2] Department of Quantity Surveying, Faculty of Built Environment, University of Malaya, Kuala Lumpur
[3] Department of Innovation, Incubation and Entrepreneurship, Krishna Vishwa Vidya Peeth, Karad (MS), Agashivnagar, Maharashtra, Malkapur
[4] Asst. Professor in Social Work, Faculty of Health Science, Istanbul Gelisim University
[5] Center for Transdisciplinary Research, Saveetha Dental College, Saveetha institute of Medical and Technical Science, Chennai
来源
关键词
Behavioral pattern analysis; Business entrepreneurship; Customer retain; Human resource management; Machine learning techniques;
D O I
10.1016/j.hitech.2023.100471
中图分类号
学科分类号
摘要
Retaining clients is turning into an estimation center in an industry with expanding rivalry. Because it is difficult to keep customers and easy for them to switch brands, the idea of customer retention has become the subject of research in the sales industry. Traditional human resource management systems are unable to manage and analyze data because of the rapid growth of enterprise-generated data's processing capacity. This exploration proposes novel strategy in human asset the executives for little new company business with their client hold utilizing Artificial intelligence (AI) procedures. Behavioral pattern analysis based on reinforcement radial fuzzy decision with quadratic kernel vector machine is utilized here for human resource management and customer relationship retention. In terms of prediction accuracy, area under the curve (AUC), average precision, sensitivity, and quadratic normalized square error, various human resource datasets based on entrepreneurship are the subjects of the experimental analysis. The proposed technique attained prediction accuracy of 98%, AUC of 89%, average precision of 83%, sensitivity of 66%, quadratic normalized square error of 59%. © 2023
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