Human Resource Planning and Configuration Based on Machine Learning

Yuan, Shuai; Qi, Qian; Dai, Enliang; Liang, Yong

doi:10.1155/2022/3605722

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…To get the best possible results, for network configuration, we use a hierarchical paradigm. According to study of [ 9 ], HR are an organization's most important asset, and accurate demand forecasting is essential to making the most use of them. Predictive models are used to examine the company's human resource demands and define essential components of human resource allocation, starting with fundamental ideas of forecasting HR.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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

View full text Add to dashboard Cite

show abstract

“…Integrity requires that each party should be able to identify whether the received message was sent by the claimant. One party of the message was sent, and the message was not tampered with in transit, including the source of the message and the content of the message [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

“…Adversaries easily forge the data files stored in the data center, and because the data center stores a large amount of data, it is difficult to detect when some data are forged or tampered with, and the tampered data are mixed with normal data for subsequent learning. Taking advantage of the immutability and decentralization of the blockchain, a blockchain-based IoT user privacy data protection scheme is proposed [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Device Identification Algorithm considering User Privacy

Wang

2022

Computational Intelligence and Neuroscience

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The Internet of Things has become the third wave of the information industry and cloud computing, big data, and Internet technologies. Among the many identification technologies used in the Internet of Things, radiofrequency identification technology is undoubtedly one of the most popular methods today. It is replacing the traditional contact IC card and becoming a new trend of smart cards. At the same time, a large amount of data is generated in the IoT environment. A lot of data involve user privacy, and users do not have good control over these data. Collecting and utilizing these data on the basis of protecting user privacy have become an important problem to be solved urgently. With the implementation of the strategy of rejuvenating the country through science and education, major colleges and universities are developing rapidly through enrollment and expansion, which also brings inconvenience to campus security management. Although the traditional campus all-in-one card system can guarantee the security identity of people entering and leaving, it does not reasonably integrate and utilize this information, resulting in waste of information resources and, to a certain extent, the problem of user privacy leakage. To solve the above problems, a new system was developed to integrate resources to identify users. To protect the privacy data of Internet of Things users, a specific solution using blockchain technology is proposed; for the identity authentication problem of Internet of Things users, the identity authentication based on the public key address of the blockchain is used on the chain, and the group signature is used off the chain. The identity authentication method solves the contradiction between anonymity and traceability in blockchain application scenarios. The simulation results show that the system not only considers user privacy but also has extremely important practical significance for the promotion of Internet of Things and RF applications.

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“…This optical refractionist task is different from that of the nurse, so the optical refractionist also performs the nurse's job. An enterprise's core resources are human resources, and demand forecasting is crucial to their allocation and optimization (Yuan, Qi, Dai, & Liang, 2022). Therefore, proper distribution is needed to place nurses at Adi Husada Undaan Wetan Hospital.…”

Section: Introductionmentioning

confidence: 99%

Analysis Of Nurse Needs Using Workload Indicator Staff Need (WISN) Method

Sungkonoputri,

Dhamanti

2023

jika

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Human resource planning is the first step that must be taken by management to ensure the availability of the right workforce following their work. There needs to be a match between the needs of officers and the number of tasks they must perform so that health services can run well. This study aimed to determine the needs of nurses at the eye clinic of Adi Husada Undaan Wetan Hospital to improve the quality of its services. This quantitative descriptive research is conducted through interviews, direct observation, and reviewing hospital documents. The subject in this study was an optical refractionist who had additional duties as a nurse at an eye clinic. The calculation of nurse needs is carried out using the WISN (Workload Indicator Staff Need) method. The analysis results of the nurses' needs at the eye clinic of Adi Husada Undaan Wetan Hospital are two nurses and one optical refractionist. So, at the eye clinic of Adi Husada Undaan Wetan Hospital, there is a shortage of 2 nurses. The management of Adi Husada Undaan Wetan Hospital needs to consider adding nurses to the eye clinic.

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Human Resource Planning and Configuration Based on Machine Learning

Cited by 4 publications

References 30 publications

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

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

Internet of Things Device Identification Algorithm considering User Privacy

Analysis Of Nurse Needs Using Workload Indicator Staff Need (WISN) Method

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