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author:

Li, Wen (Li, Wen.) [1] | Zhou, Xiukao (Zhou, Xiukao.) [2]

Abstract:

Efficient human resource management (HRM) is essential for company achievement in today's fast-paced corporate world. Businesses must find effective ways to retrieve and categorise the ever-growing amount of HR-related knowledge. This work presents a new method for retrieving and classifying HRM data using machine learning. Modern natural language processing (NLP) and CNN methods are used by the algorithm we developed to handle unorganized human resources (HR) information, including worker records, job postings, and certificates. HR decision-making is facilitated by the system's ability to derive insightful information from the information using sophisticated text mining and machine learning algorithms.The two main parts of the method are classification and data extraction. HR workers can more easily obtain the required knowledge thanks to information retrieval, which makes it possible to search HR data quickly and accurately. Contrarily, categorisation optimises the division of human resources information into pertinent classifications, including job positions, competencies, and achievement grades. We assess our algorithm's effectiveness on various datasets from actual HR datasets. The outcomes show how well our strategy works to streamline HRM procedures, cut down on manual labour, and increase the precision of decisions. Furthermore, our technology is compatible with corporate human resources offices and educational settings because it complies with worldwide university requirements.This study belongs to the growing body of knowledge in HRM. It provides a useful tool for businesses looking to improve employee relations, simplify HR procedures, and attract and retain talent. The suggested method is a valuable resource for academics and businesses alike because of its versatility and compliance with global educational norms.

Keyword:

classification algorithm human resource management information retrieval Machine learning

Community:

  • [ 1 ] [Li, Wen]Fuzhou Technol & Business Univ, Sch Management, Fuzhou 35071, Fujian, Peoples R China
  • [ 2 ] [Zhou, Xiukao]Fuzhou Univ, Sch Econ & Management, Fuzhou 350108, Fujian, Peoples R China

Reprint 's Address:

  • [Zhou, Xiukao]Fuzhou Univ, Sch Econ & Management, Fuzhou 350108, Fujian, Peoples R China;;

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Source :

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE

ISSN: 1895-1767

Year: 2024

Issue: 6

Volume: 25

Page: 5431-5440

0 . 9 0 0

JCR@2023

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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