High Level Talent Training Platform Based on Artificial Intelligence Algorithm
- DOI
- 10.2991/978-2-38476-068-8_31How to use a DOI?
- Keywords
- Artificial Intelligence; High-level Talents; Talent Cultivation; Platform Research
- Abstract
With the rapid development and application of artificial intelligence technology, in the training of high-level talents, more and more needs to be solved is how to transform it into actual productivity. Therefore, based on the current research situation at home and abroad, this paper proposes a set of platform architecture related to AI algorithm. This paper first studies the environment of high-level talent cultivation, then expounds the importance of high-level talent cultivation and construction, then studies the artificial intelligence algorithm, and based on this, designs a high-level talent cultivation platform. Finally, the performance of the platform is tested by simulation experiments. The test results show that the high-level talent training platform based on intelligent algorithms has short response time and delay time, high software compatibility and security. This shows that the platform has complete functions and good performance.
- Copyright
- © 2023 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
TY - CONF AU - Yanmei Guo PY - 2023 DA - 2023/07/19 TI - High Level Talent Training Platform Based on Artificial Intelligence Algorithm BT - Proceedings of the 2nd International Conference on Humanities, Wisdom Education and Service Management (HWESM 2023) PB - Atlantis Press SP - 235 EP - 242 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-068-8_31 DO - 10.2991/978-2-38476-068-8_31 ID - Guo2023 ER -