基于深度学习的物联网下的体育课程推系统
1School of Physical Education, Shanghai Normal University, Shanghai, 200234, China.
Heliyon
|October 22, 2024
概括
本研究介绍了一种使用物联网 (IoT) 数据进行个性化体育课程推的深度学习 (DL) 系统. 先进的生成对抗网络 (GAN) 模型提高了准确性,有效地应对数据挑战.
科学领域:
- 计算机科学 计算机科学
- 教育技术的教育技术
- 数据科学数据科学数据科学
背景情况:
- 传统的体育课程建议缺乏个性化和准确性.
- 现有的系统在数据稀疏性和冷启动问题上扎.
研究的目的:
- 提出一个基于深度学习 (DL) 的体育课程推系统.
- 通过整合物联网 (IoT) 技术和DL来提高推准确性和个性化性.
- 使用生成对抗网络 (GAN) 模型解决数据稀疏性和冷启动问题.
主要方法:
- 利用物联网设备 (智能手,智能服装) 来实时监控生理和环境数据.
- 捕获学生的社会互动,以提供社会导向的课程建议.
- 整合物联网数据与学术数据,以优化课程匹配.
- 采用规范化惩罚条件特征生成对抗网络 (RP-CFGAN) 模型来处理数据稀疏性和冷启动问题.
主要成果:
- 拟议的基于DL的系统在TopN评估中表现强.
- 与传统推模型相比,观察到显著的改进.
- 物联网和GAN模型的整合改善了对学生个性化建议需求的理解.
结论:
- 物联网技术和GAN模型的结合为个性化的体育课程建议提供了强大的解决方案.
- 该系统有效地解决了推系统的关键挑战,提高了准确性和用户满意度.
- 未来的工作包括探索先进的规范化,确保用户隐私,并扩大系统的适用性.
相关概念视频
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