The application of array-driven neural network modeling is currently one of the hot topics of interdisciplinary research, especially in terms of employee performance prediction. This application extracts and explores key information in the form of a job training array to help job trainers accurately assess the work status of employees. In view of the current lack of accuracy of measurement and evaluation models, this paper proposes an innovative width-of-work employee value network model that incorporates DA-GRU-CNN technology. This model consists of three modules: an array processing module, a network DA-GRU-CNN measurement module, and a performance prediction module. After practical testing and verification, the DA-GRU-CNN model proposed in this paper has been shown to have higher accuracy and greater adaptability in determining the feedback time, with an average accuracy of up to 98.5% on width work. The performance prediction module relies on a proven high-quality network model to quickly identify and accurately predict employee value.
ZhangKGaoXTaoD, et al.Single image super-resolution with non-local means and steering kernel regression. IEEE Trans Image Process2012; 21(11): 4544–4556.
2.
TimofteRDeVVanGL, et al.Anchored neighborhood regression for fast example-based super-resolution. In: IEEE international conference on computer vision, Sydney, NSW, Australia, 01–08 December 2013, pp. 1920–1927.
3.
LiuXLiuHLiJ, et al.Research on student performance prediction based on multivariate linear degree return. Comput Technol Dev2022; 32(3): 203–208.
4.
HanYChenJLiuY. Exploring the practice of predicting college students’ grades and guiding their major streaming in China--an empirical analysis based on matrix filling and return model. J Nanjing Normal Univ (Nat Sci)2022; 45(2): 106–111.
5.
JinXZhuoY. Prediction of college entrance examination scores based on genetic algorithm and support vector-machine model. J Henan Eng College (Nat Sci)2020; 32(2): 62–65.
6.
LvPYuWWangX, et al.Machine learning-based student performance prediction and teaching insights. Comput Technol Dev2019; 29(4): 200–203.
7.
NabilASeyamMAbou-ElfetouhA. Prediction of students’ academic performance based on courses’ grades using deep neural networks. IEEE Access2021; 9(9): 140731–140746.
8.
Philip ChenCLLiuZL. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Transact Neural Networks Learn Syst2018; 29(1): 10–24.
9.
RenCXuY. Incremental broad learning for real-time updating of data-driven power system dynamic security assessment models. IET Gener, Transm Distrib2020; 14(19): 4052–4059.
10.
FengJYaoYLuSX, et al.Domain knowledge-based deep-broad learning framework for fault diagnosis. IEEE Trans Ind Electron2021; 68(4): 3454–3464.
11.
LiGZZhangANZhangQZ, et al.Pearson correlation coefficient-based performance enhancement of broad learning system for stock price prediction. IEEE Trans Circuits Syst II2022; 69(5): 2413–2417.
12.
XuGLiLWenZ, et al.Mudslide disaster forecasting based on width learning model. J Mountainology2019; 37(6): 868–878.
13.
ChenFCuiY. Utilizing student time series behavior in learning management systems for early prediction of course performance. Learning Analytics2020; 7(2): 1–17.
PandeyMSharmaVK. A decision tree algorithm about the student performance analysis and prediction. Int J Comput Appl2013; 61(13): 1–5.
16.
OkuboFYamashitaTShimadaA, et al.A neural network approach for students’ performance prediction. Proceedings of the seventh international learning analytics & knowledge conference. New York, NY: ACM, 2017, pp. 598–599. https://doi.org/10.1145/3027385.302947
17.
LuOHTHuangAYQHuangJCH, et al.Applying learning analytics for the early prediction of students’ academic performance in blended learning. J Educ Technol Soci2018; 21(2): 220–232.
18.
AydoğduŞ. Predicting student final performance using artificial neural networks in online learning environments. Educ Inf Technol2020; 25(3): 1913–1927.
19.
LiMWangXRuanS, et al.Student performance estimation based on two-way attention mechanism. Compu Res Dev2020; 57(8): 1729–1740.
20.
HassanSUWaheedHAljohaniNR, et al.Virtual learning environment to predict withdrawal by leveraging deep learning. Int J Intell Syst2019; 34(8): 1935–1952.
21.
DuX. Research and implementation of learning behavior analysis and academic early warning technology based on campus network array. Beijing: Beijing University of Posts and Telecommunications, 2020.
22.
WaheedHHassanSUAljohaniNR, et al.Predicting the academic performance of students from VLE big data using deep learning models. Comput Hum Behav2020; 104: 106189.
23.
ZhangYFLuC. Remaining life prediction based on integrated neural network. J Eng Sci2020; 42(10): 1372–1380.
24.
KuzilekJHlostaMZdrahalZ. Open university learning analytics dataset. Sci Data2017; 4(1): 1–8.
25.
NguyenVANguyenHHNguyenDL. A course recommendation model for students based on learning outcome. Educ Inf Technol2021; 26: 5389–5415. https://doi.org/10.1007/s10639-021-10524-0
26.
DongCLoyCCHeK, et al.Learning a deep convolutional network for image super-resolution. European conference on computer vision. Cham: Springer, 2014, pp. 184–199. https://doi.org/10.1007/978-3-319-10593-2_13
27.
CooperCJCooperSPDel JuncoDJ, et al.Web-based data collection: detailed methods of a questionnaire and data gathering tool. Epidemiol Perspect Innovat2006; 3: 1–11.
28.
MorcosMWTeeterMGSomervilleLE, et al.Correlation between hip osteoarthritis and the level of physical activity as measured by wearable technology and patient-reported questionnaires. J Orthop2020; 20: 236–239.
29.
YanYWangH. Study on the training strategy of students’ practical ability in education technology major. Sci Discov2018; 6(5): 53484870.
