{"title":"基于大数据分析和人工智能的创业和留住客户资源管理项目","authors":"Pham Quang Huy , Shavkatov Navruzbek Shavkatovich , Zulkiflee Abdul-Samad , D.K. Agrawal , K.M. Ashifa , Mahendran Arumugam","doi":"10.1016/j.hitech.2023.100471","DOIUrl":null,"url":null,"abstract":"<div><p>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%.</p></div>","PeriodicalId":38944,"journal":{"name":"Journal of High Technology Management Research","volume":"34 2","pages":"Article 100471"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Resource management projects in entrepreneurship and retain customer based on big data analysis and artificial intelligence\",\"authors\":\"Pham Quang Huy , Shavkatov Navruzbek Shavkatovich , Zulkiflee Abdul-Samad , D.K. Agrawal , K.M. Ashifa , Mahendran Arumugam\",\"doi\":\"10.1016/j.hitech.2023.100471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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%.</p></div>\",\"PeriodicalId\":38944,\"journal\":{\"name\":\"Journal of High Technology Management Research\",\"volume\":\"34 2\",\"pages\":\"Article 100471\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of High Technology Management Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1047831023000214\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Business, Management and Accounting\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of High Technology Management Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1047831023000214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
Resource management projects in entrepreneurship and retain customer based on big data analysis and artificial intelligence
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%.
期刊介绍:
The Journal of High Technology Management Research promotes interdisciplinary research regarding the special problems and opportunities related to the management of emerging technologies. It advances the theoretical base of knowledge available to both academicians and practitioners in studying the management of technological products, services, and companies. The Journal is intended as an outlet for individuals conducting research on high technology management at both a micro and macro level of analysis.