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在机器学习的背景下,基于U-net卷积神经网络的体育成绩预测系统的设计

Guoliang Wang¹, Tianping Ren¹

¹College of Sport, Henan Polytechnic University, Jiaozuo, Henan, 454003, China.

|May 23, 2024

概括

这项研究引入了改进的U-Net卷积神经网络 (CNN),用于预测大学生体育表现. 改进后的模型实现了卓越的准确性,提高了身体健康和智能教室的发展.

科学领域:

机器学习机器学习
人工智能的人工智能
运动科学运动科学运动科学

背景情况:

体育表现预测对于学生的动力和身体健康至关重要.
现有的方法在准确性和特征利用方面面临挑战.
研究体育教师的教学能力,以确定需要改进的领域.

研究的目的:

为大学生开发一个准确的体育成就预测系统.
在预测模型中增强网络特征的利用和传播.
提高体育教育教学和学习的效率.

主要方法:

使用U-Net卷积神经网络 (CNN) 构建了一个体育成就预测系统.
将密集连接纳入U-Net模型以解决梯度消失问题.
引入了改进的混合损失函数,以减轻类不平衡问题.

主要成果:

改进的U-Net CNN模型展示了卓越的体育表现预测准确度.
与原来的U-Net.net相比,预测准确度增加了4.22%.
超过了DUNet的预测准确度5.22%,并且比其他现有网络表现更好.

结论:

拟议的U-Net CNN系统显著提高了体育成就预测.
这些发现提供了关于将人工智能应用于智能课堂开发的见解.
该系统提高了体育教育的教学和学习效率.

关键词:

预测成就的预测.注意模块的注意力模块.密集的连接密集的连接机器学习机器学习剩余的学习学习.这是一个U-Net卷积神经网络.

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