Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

<Previous Article In Volume
Next Article In Volume>

Prediction of Large-Scale Instrument Usage Based on Catboost Algorithm for Science and Education Integration in Local Universities

Authors
Lin Tao1, Jiading Bao2, *, Bao Zhu2
1Department of State-owned Assets Management, Guilin University of Electronic Technology, Guilin, 541004, China
2Faculty of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, 541004, China
*Corresponding author. Email: jdbao@guet.edu.cn
Corresponding Author
Jiading Bao
Available Online 30 December 2022.
DOI
10.2991/978-94-6463-108-1_81How to use a DOI?
Keywords
machine learning; time series; forecasting; science education integration
Abstract

Under the background of the "Double First-Class" university plan, the sharing mechanism of large-scale instruments plays an indispensable role in science and education integration in universities. This paper first collects usage data (from September 2020 to September 2022) of large campus-shared devices in a real-world setting. Second, based on the machine learning algorithm, CatBoost, this paper constructs a method to predict the usage of devices. The results show that the best prediction is achieved when the input time step is 3 (with MAE, MSE, and RMSE being 56.10, 7445.43, and 86.28, respectively). Based on the obtained prediction results, corresponding policy recommendations are proposed further. Finally, taking Guilin University of Electronic Science and Technology as an example, this paper illustrates the use cases of large campus-shared devices in the real world. The method in this paper provides concrete decision support for large campus-shared equipment managers to estimate the busy and idle periods of equipment usage and develop equipment maintenance plans.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
Series
Advances in Computer Science Research
Publication Date
30 December 2022
ISBN
978-94-6463-108-1
ISSN
2352-538X
DOI
10.2991/978-94-6463-108-1_81How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Lin Tao
AU  - Jiading Bao
AU  - Bao Zhu
PY  - 2022
DA  - 2022/12/30
TI  - Prediction of Large-Scale Instrument Usage Based on Catboost Algorithm for Science and Education Integration in Local Universities
BT  - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
PB  - Atlantis Press
SP  - 728
EP  - 738
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-108-1_81
DO  - 10.2991/978-94-6463-108-1_81
ID  - Tao2022
ER  -