混合灰斑深度学习与分层稀疏网络为女性在月经周期期间的营养建议
E Logapriya1, Surendran Rajendran2, Mohammad Zakariah3
1Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, 602105, India.
Scientific reports
|February 18, 2025
概括
这项研究引入了一种新的深度学习模型,用于根据女性月经周期量身定制的个性化营养建议. 优化混合深度学习 (OdriHDL) 模型实现了高精度,解决了特定的生理需求.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 营养科学 营养科学
背景情况:
- 月经周期涉及复杂的身体和荷尔蒙变化.
- 现有的营养模式提供一般建议,缺乏个性化月经周期阶段.
- 具体的营养需求在整个月经周期中波动.
研究的目的:
- 提出一种新的优化混合深度学习 (OdriHDL) 模型,用于在月经周期期间提供个性化的营养建议.
- 解决传统营养模式在满足女性特定生理变化的局限性.
- 通过数据驱动的,特定阶段的饮食指导来增强妇女的健康.
主要方法:
- 数据预处理包括缺失值推算,Z分数规范化和一热编码.
- 功能提取是使用层级 Sparse 自动编码网络进行的.
- 营养建议是由基于混合注意力的双向卷积式灰门循环网络 (HABi-ConGRNet) 产生的,该网络使用灰优化算法进行了优化.
主要成果:
- 奥德里HDL模型实现了97.52%的最大精度.
- 与现有方法 (RNN,CNN-LSTM,注意力GRU) 相比,该模型表现出卓越的性能和增强的精度.
- 奥德里HDL有效地捕获了营养需求和月经症状之间的复杂模式.
结论:
- 奥德里HDL模型为妇女在月经周期期间的营养需求提供了强大的,个性化的解决方案.
- 这种方法可以通过解决独特的生理变化,显著改善健康管理.
- 这些发现突显了深度学习在个性化女性健康和营养方面的潜力.
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