[Retracted] Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

Zheng, Jiafeng; Ma, Ruijun

doi:10.1155/2021/6596548

Cited by 14 publications

(11 citation statements)

References 28 publications

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“…6. This system can make human resources management more efficient through collaboration between different modules [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

Design and development of human resource management computer system for enterprise employees

Wang

2021

PLoS ONE

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Client-Server (C/S) application is always used in the existing Human Resource Management System (HRMS) as the system architecture, which has the problems of complex maintenance and poor compatibility; and cannot use professional database and development system, making the system development difficult and the data security low. To solve the above problems, the overall demand is analyzed, as well as feasibility and key technologies of the enterprise HRMS system. Then a HRMS is designed and developed, based on the user’s key functional requirements and related technologies, which is reasonable and easy to maintain. The system is supported by Browser-Server (B/S) structure, with the current popular Java 2 Platform Enterprise Edition (J2EE) multi-level structure as the overall architecture. The mature Microsoft SQL Server 2008 introduced by Microsoft is used as the database platform. Combined with Model View Controller (MVC) design pattern, this system can be used by users without geographical restrictions and system maintenance. In this system, performance logic and business logic are separated, which makes it convenient for the development and maintenance of the system. The system mainly includes six modules: personnel management, organizational management, recruitment management, training management, salary management and system management, which integrates enterprise information and realizes the functions of easy access and easy query of information database. Its interface is simple, easy to understand, and easy to operate, with low investment, low cost, high safety, good performance and easy maintenance, which help to improve the work efficiency and modern management level of enterprises. In the end, the operation performance of the system is tested. The results show that the throughput of the main functional modules in the system is greater than 100 times/s when dealing with the business, and the success rate of event processing is greater than 99%. The average response time of the business end is less than 0.4 s, and the average response time of the terminal side is less than 0.5 s, which all meet the standards. System CPU occupancy rate can be basically controlled below 30%, and memory usage rate is below 30%. In summary, the system designed here has the basic functions but also to ensure good performance, suitable for enterprise personnel management, organizational management, recruitment management, training management and salary management. The design and development of this system aims to provide technical support for the service quality of enterprise human resource management business, to improve the overall efficiency, promote the pace of enterprise strategic development, and enhance the market competitiveness of enterprises.

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“…6. This system can make human resources management more efficient through collaboration between different modules [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

Design and development of human resource management computer system for enterprise employees

Wang

2021

PLoS ONE

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“…Human resource planning can only be done effectively if the demands of the company's future development are clearly defined because of the company's current situation and the supply and demand of HR. There will be a skill scarcity in the company if there are too many staff demand estimates, and this might impede the company's future growth as well [ 2 ]. The purpose of industrial development is to utilize natural resources and energy, as well as human resources, to aid industrial expansion by providing jobs and increasing exports.…”

Section: Introductionmentioning

confidence: 99%

“…ere will be a skill scarcity in the company if there are too many sta demand estimates, and this might impede the company's future growth as well [2]. e purpose of industrial development is to utilize natural resources and energy, as well as human resources, to aid industrial expansion by providing jobs and increasing exports.…”

Section: Introductionmentioning

confidence: 99%

Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network

Rajagopal¹,

Saini

Huerta-Soto

et al. 2022

Computational Intelligence and Neuroscience

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Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization’s human resources input and output. It is tough to provide adequate instructions for HR’s unique task. In a time when the domestic labor market is still maturing, it is difficult for companies to make successful adjustments in HR structures to meet fluctuations in demand for human resources caused by shifting corporate strategies, operations, and size. Data on corporate human resources are often insufficient or inaccurate, which creates substantial nonlinearity and uncertainty when attempting to predict staffing needs, since human resource demand is influenced by numerous variables. The aim of this research is to predict the human resource demand using novel methods. Recurrent neural networks (RNNs) and grey wolf optimization (GWO) are used in this study to develop a new quantitative forecasting method for HR demand prediction. Initially, we collect the dataset and preprocess using normalization. The features are extracted using principal component analysis (PCA) and the proposed RNN with GWO effectively predicts the needs of HR. Moreover, organizations may be able to estimate personnel demand based on current circumstances, making forecasting more relevant and adaptive and enabling enterprises to accomplish their objectives via efficient human resource planning.

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“…Self-organizing feature mapping neural network was proposed by a professor of neural network expert self-organizing feature mapping network of University of Helsinki, Finland in 1981 [ 21 ]. This network simulates the function of self-organizing feature mapping of the brain nervous system.…”

Section: Methodsmentioning

confidence: 99%

Key Information Extraction Algorithm of Different Types of Digital Archives for Cultural Operation and Management

2022

Computational Intelligence and Neuroscience

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In order to improve the effect of key information extraction from digital archives, a key information extraction algorithm for different types of digital archives is designed. Preprocess digital archive information, taking part of speech and marks as key information. Self-organizing feature mapping network is used to extract the key information features of digital archives, and the semantic similarity calculation results are obtained by combining the feature extraction results. Combine with mutual information collection, take that word with the highest mutual information value as the collection cent, traverse all keywords, and take the central word as the key information of digital archives to complete the extraction of key information. Experiments show that the recall rate of the algorithm ranges from 96% to 99%, the extraction accuracy of key information of digital archives is between 96 and 98%, and the average extraction time of key information of digital archives is 0.63 s. The practical application effect is good.

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[Retracted] Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

Cited by 14 publications

References 28 publications

Design and development of human resource management computer system for enterprise employees

Design and development of human resource management computer system for enterprise employees

Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network

Key Information Extraction Algorithm of Different Types of Digital Archives for Cultural Operation and Management

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