Design and interactive performance of human resource management system based on artificial intelligence

Gong, Yangda; Zhao, Min; Qin, Wang; Lv, Zhihan

doi:10.1371/journal.pone.0262398

Cited by 18 publications

(11 citation statements)

References 35 publications

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“…Zhu [ 11 ] used machine learning technology to manage and analyze human resource data in modern enterprises. Machine learning technology realized the function of the human resources system, reduced the business volume of human resources, and improved the efficiency and management level of human resources [ 12 ]. According to Tambe et al [ 13 ], AI deployments in HRM issues such as staffing and selection are becoming gradually prevalent and have drastically reduced the time and cost of carrying out these functions.…”

Section: Introductionmentioning

confidence: 99%

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Jiaping

2022

Computational Intelligence and Neuroscience

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Artificial intelligence (AI) is a potentially transformative force that is likely to change the role of management and organizational practices. AI is revolutionizing corporate decision-making and changing management structures. The visible effects of AI can be observed in key competencies and corporate processes such as knowledge management, as well as consumer outcomes including service quality perceptions and satisfaction. This study aims to optimize the human resource management (HRM) process, reduce the workload of human resource managers, and improve work efficiency. Based on AI digitization technology, a salary prediction model (SPM) is designed using a backpropagation neural network (BPNN), and the Nesterov and Adaptive Moment Estimation (Nadam) algorithms are integrated to optimize the model. Next, the content information of the resumes are used to predict the hiring salary of the candidates and validate the model. Results show that compared with other optimization algorithms, the final predicted result score of the Nadam optimization algorithm is 0.75%, and the training period is 186 s, providing the best optimization effect and the fastest convergence speed. Moreover, the BPNN-based SPM optimized by Nadam has good performance in the learning process and the accuracy rate can reach 79.4%, which verifies the validity of the SPM. The outcomes of this study can provide a reference for HRM systems based on data mining technology.

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Section: Introductionmentioning

confidence: 99%

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Jiaping

2022

Computational Intelligence and Neuroscience

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“…An impressive amount of work has been done by reference [33] in the area of artificial intelligence art design, particularly in the area of combining robots and humans and then controlling the creative production of human behaviour. Reference [34] uses AI pattern recognition technology to study the visual information recognition and image generation art of intelligent robots.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

Zhao

2022

Journal of Sensors

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Interactive art design (IAD) is an organic integration of art and technology. From the perspective of the development of AI machines from ancient times to the present, it has gone through the stages of command interface, graphical interface, and multimedia interface. The development of interactive art has been around for some years. As an art form, it not only brings optimization and enjoyment to people’s quality of life but also meets the needs of human-computer interaction, improves the efficiency of art design, and realizes human-human interaction. The purpose is to bring different feelings and experiences to the works and people’s psychology. In particular, the application of AI in IAD has not only brought great changes to designers. It also derives the interaction of behavioral limbs, which brings a greater experience and interaction to the audience, thereby creating a better interactive effect. Therefore, this paper completes the following work: (1) the research progress of AI in IAD at home and abroad is introduced. (2) The combination of AI and IAD is proposed, and the basic principle of RBF neural network and intelligent optimization algorithm are introduced, and the evaluation index of IAD is constructed. (3) Using the constructed dataset to test two intelligent optimization algorithms, the results show that the PSO-RBF model is more excellent in evaluating the quality of IAD. The trained model is used for experiments, and the output of the model is compared with the expert evaluation results, and the error is very small. Comparing the quality indicators of IAD before and after the integration of AI, the results show that AI has an excellent improvement effect on IAD.

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“…Their findings underscore the method’s potential for bolstering network security risk management, extending novel data security safeguards to scientific research project management. Gong et al 17 devised an AI-driven human resources management system. This system autonomously evaluates employee performance and needs, proffering intelligent managerial recommendations.…”

Section: Related Workmentioning

confidence: 99%

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

Dong,

Qiu

2024

Sci Rep

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This study begins by considering the resource-sharing characteristics of scientific research projects to address the issues of resource misalignment and conflict in scientific research project management. It comprehensively evaluates the tangible and intangible resources required during project execution and establishes a resource conflict risk index system. Subsequently, a resource conflict risk management model for scientific research projects is developed using Back Propagation (BP) neural networks. This model incorporates the Dropout regularization technique to enhance the generalization capacity of the BP neural network. Leveraging the BP neural network’s non-linear fitting capabilities, it captures the intricate relationship between project resource demand and supply. Additionally, the model employs self-learning to continuously adapt to new scenarios based on historical data, enabling more precise resource conflict risk assessments. Finally, the model’s performance is analyzed. The results reveal that risks in scientific research project management primarily fall into six categories: material, equipment, personnel, financial, time, and organizational factors. This study’s model algorithm exhibits the highest accuracy in predicting time-related risks, achieving 97.21%, surpassing convolutional neural network algorithms. Furthermore, the Root Mean Squared Error of the model algorithm remains stable at approximately 0.03, regardless of the number of hidden layer neurons, demonstrating excellent fitting capabilities. The developed BP neural network risk prediction framework in this study, while not directly influencing resource utilization efficiency or mitigating resource conflicts, aims to offer robust data support for research project managers when making decisions on resource allocation. The framework provides valuable insights through sensitivity analysis of organizational risks and other factors, with their relative importance reaching up to 20%. Further research should focus on defining specific strategies for various risk factors to effectively enhance resource utilization efficiency and manage resource conflicts.

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Design and interactive performance of human resource management system based on artificial intelligence

Cited by 18 publications

References 35 publications

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology

Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

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