Please use this identifier to cite or link to this item:
https://digital.lib.ueh.edu.vn/handle/UEH/70271
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pham Quang Huy | - |
dc.contributor.other | Shavkatov Navruzbek Shavkatovich | - |
dc.contributor.other | Zulkiflee Abdul-Samad | - |
dc.contributor.other | D.K. Agrawal | - |
dc.contributor.other | K.M. Ashifa | - |
dc.contributor.other | Mahendran Arumugam | - |
dc.date.accessioned | 2023-11-29T08:44:55Z | - |
dc.date.available | 2023-11-29T08:44:55Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 1047-8310 | - |
dc.identifier.uri | https://digital.lib.ueh.edu.vn/handle/UEH/70271 | - |
dc.description.abstract | 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%. | en |
dc.format | Portable Document Format (PDF) | - |
dc.language.iso | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Journal of High Technology Management Research | - |
dc.relation.ispartofseries | Vol. 34, Issue 2 | - |
dc.rights | Elsevier | - |
dc.subject | Human resource management | en |
dc.subject | Business entrepreneurship | en |
dc.subject | Customer retain | en |
dc.subject | Machine learning techniques | en |
dc.subject | Behavioral pattern analysis | en |
dc.title | Resource management projects in entrepreneurship and retain customer based on big data analysis and artificial intelligence | en |
dc.type | Journal Article | en |
dc.identifier.doi | https://doi.org/10.1016/j.hitech.2023.100471 | - |
ueh.JournalRanking | Scopus | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairetype | Journal Article | - |
item.fulltext | Only abstracts | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
Appears in Collections: | INTERNATIONAL PUBLICATIONS |
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