The Impact of Improving Employee Psychological Empowerment and Job Performance Based on Deep Learning and Artificial Intelligence

被引：6
|
作者：
Fan, Xiaoxue [1 ]
Zhao, Shulang [2 ]
Zhang, Xuan [3 ]
Meng, Lingchai [4 ]
机构：
[1] Semyung Univ, Jecheon Si, South Korea
[2] Beijing Univ Technol, Beijing, Peoples R China
[3] Monroe Coll, Bronx, NY USA
[4] Qingdao Hengxing Univ Sci & Technol, Qingdao, Peoples R China
来源：
JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING | 2023年 / 35卷 / 03期
关键词：
Artificial Intelligence; Back Propagation Neural Network; Deep Learning; Psychological Empowerment; Work Performance;
D O I：
10.4018/JOEUC.321639
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to improve the management mode of the human resources system and enhance the work efficiency of employees, this paper uses artificial intelligence (AI) technology to enhance the psychological empowerment of employees. It affects employees' work performance from a psychological perspective with the help of psychological empowerment. The questionnaire survey method collects data on the influencing factors of employees' psychological empowerment. The statistical data are analyzed by regression. Back propagation neural network (BPNN) algorithm based on deep learning is used to establish the work condition evaluation model. The job satisfaction, pressure, and performance of employees based on psychological empowerment are analyzed.
引用
收藏
页数：14
相关论文
共 50 条
  • [21] A review of artificial intelligence based malware detection using deep learning
    Mustafa Majid A.-A.
    Alshaibi A.J.
    Kostyuchenko E.
    Shelupanov A.
    Materials Today: Proceedings, 2023, 80 : 2678 - 2683
  • [22] Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
    Dimiduk, Dennis M.
    Holm, Elizabeth A.
    Niezgoda, Stephen R.
    INTEGRATING MATERIALS AND MANUFACTURING INNOVATION, 2018, 7 (03) : 157 - 172
  • [23] Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
    Dennis M. Dimiduk
    Elizabeth A. Holm
    Stephen R. Niezgoda
    Integrating Materials and Manufacturing Innovation, 2018, 7 : 157 - 172
  • [24] Machine learning and role of artificial intelligence in optimizing work performance and employee behavior
    Ramachandran, K. K.
    Mary, A. Apsara Saleth
    Hawladar, Shibani
    Asokk, D.
    Bhaskar, Bandi
    Pitroda, J. R.
    MATERIALS TODAY-PROCEEDINGS, 2022, 51 : 2327 - 2331
  • [25] Artificial intelligence design decision making model based on deep learning
    Wang Y.
    Yu S.
    Chen D.
    Chu J.
    Liu Z.
    Wang J.
    Ma N.
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2019, 25 (10): : 2467 - 2475
  • [26] RETRACTED: Application of deep learning and artificial intelligence in the psychological mechanism of language activity (Retracted Article)
    Anxie, Tuo
    Bing, Li
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 38 (06) : 7315 - 7327
  • [27] The unreasonable effectiveness of deep learning in artificial intelligence
    Sejnowski, Terrence J.
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (48) : 30033 - 30038
  • [28] Demystifying artificial intelligence and deep learning in dentistry
    Rodrigues, Jonas Almeida
    Krois, Joachim
    Schwendicke, Falk
    BRAZILIAN ORAL RESEARCH, 2021, 35
  • [29] A Hybrid Gomoku Deep Learning Artificial Intelligence
    Yan, Peizhi
    Feng, Yi
    PROCEEDINGS OF 2018 ARTIFICIAL INTELLIGENCE AND CLOUD COMPUTING CONFERENCE (AICCC 2018), 2018, : 48 - 52
  • [30] Applications of Artificial Intelligence and Deep Learning in Glaucoma
    Chen, Dinah
    Ran, Emma Anran
    Tan, Ting Fang
    Ramachandran, Rithambara
    Li, Fei
    Cheung, Carol
    Yousefi, Siamak
    Tham, Clement C. Y.
    Ting, Daniel S. W.
    Zhang, Xiulan
    Al-Aswad, Lama A.
    ASIA-PACIFIC JOURNAL OF OPHTHALMOLOGY, 2023, 12 (01): : 80 - 93
12345