Welcome to Francis Academic Press

The Frontiers of Society, Science and Technology, 2024, 6(1); doi: 10.25236/FSST.2024.060126.

Research on Deep Learning Programming Methods and Systems Based on Digital Mirror Platforms

Author(s)

Yuqiang Tan, Yanbin Long

Corresponding Author:
Yuqiang Tan
Affiliation(s)

University of Science and Technology Liaoning, Anshan, Liaoning, China

Abstract

This paper introduces a deep learning programming method and system based on a digital mirror platform. It involves several key steps: acquiring equipment operation data from various terminal equipment across diverse business processes, including the process numbers of each suboperation. We then compare and analyze the data modeling of this equipment operation data from different terminal equipment. Based on the analysis results, we determine if the terminal equipment requires upgrading or optimization.

Keywords

Digital Mirroring Platform, Deep Learning, Programming Methods

Cite This Paper

Yuqiang Tan, Yanbin Long. Research on Deep Learning Programming Methods and Systems Based on Digital Mirror Platforms. The Frontiers of Society, Science and Technology (2024), Vol. 6, Issue 1: 155-165. https://doi.org/10.25236/FSST.2024.060126.

References

[1] Lin Yuanguo, Lin Fan, Zeng Wenhua, Xiahou Jianbing, Li Li, Wu Pengcheng, Liu Yong, Miao Chunyan. Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation [J]. Knowledge-Based Systems, 2022,123-124

[2] Zheng Wei, Du Qing, Fan Yongjian, Tan Lijuan, Xia Chuanlin, Yang Fengyu. A personalized programming exercise recommendation algorithm based on knowledge structure tree [J]. Journal of Intelligent & Fuzzy Systems, 2022,45-46

[3] Yujia Huo, Derek F. Wong, Lionel M. Ni, Lidia S. Chao, Jing Zhang. Knowledge modelling via contextualized representations for LSTM-based personalized exercise recommendation [J]. Information Sciences, 2020,66-68

[4] Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, Jimeng Sun. Hierarchical Reinforcement Learning for Course Recommendation in MOOCs[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019,234-235

[5] Raciel Yera Toledo, Yailé Caballero Mota, Luis Martínez. A Recommender System for Programming Online Judges Using Fuzzy Information Modeling[J]. Informatics, 2018.5-7

[6] Raciel Year, Luis Martínez. A recommendation approach for programming online judges supported by data preprocessing techniques [J]. Applied Intelligence, 2017.65-69

[7] Raciel Yera Toledo, Yailé Caballero Mota. An e-Learning Collaborative Filtering Approach to Suggest Problems to Solve in Programming Online Judges [J]. International Journal of Distance Education Technologies (IJDET), 2014.89-89

[8] Xitao Fan. Item Response Theory and Classical Test Theory: An Empirical Comparison of their Item/Person Statistics [J]. Educational and Psychological Measurement,1998(3),45-66

[9] Volodymyr Mnih;Koray Kavukcuoglu;David Silver; Alex Graves;Ioannis Antonoglou;Daan Wierstra;Martin A. Playing Atari with Deep Reinforcement Learning.[J]. Riedmiller.CoRR,2013,34-38

[10] De la Torre Jimmy.DINA Model and Parameter Estimation: A Didactic[J]. de la Torre Jimmy.Journal of Educational and Behavioral Statistics,2009(1),45-46