使用混合人工智能模型估计每日能源需求
1Institute of Health Sciences, Department of Nutrition and Dietetics, Erciyes University, Kayseri, Turkey. zeliha.guvenc1@gmail.com.
Scientific reports
|November 6, 2025
概括
混合人工智能模型使用人体测量和人口统计数据准确估计每日能源需求. 这种方法为健康和营养应用提供了传统方法的实用,灵活和更精确的替代方案.
科学领域:
- 卫生科学 卫生科学 卫生科学
- 人工智能的人工智能
- 营养科学 营养科学
背景情况:
- 精确的能源需求估计对健康至关重要.
- 传统的方法往往不准确,复杂和昂贵.
- 对于实际的现场应用,需要新的方法.
研究的目的:
- 探索混合人工智能模型来计算日常能源需求.
- 开发一个高度准确和用户友好的现场设置模型.
- 将AI模型的性能与传统的能源估计公式进行比较.
主要方法:
- 从志愿者那里收集了人体测量 (生物电阻分析) 和人口统计数据.
- 使用斯皮尔曼特征选择来分析87个特征.
- 开发了混合高斯过程回归 (GPR) 模型,整合了三个核心,按性别结构化.
主要成果:
- 最好的男性模型在10级实现了100%的R2,最好的女性模型在15级显示了最高的准确性.
- 主要组件分析 (PCA) 为简单性降低模型性能 (R2 = 0.23).
- 人工智能模型的准确性明显高于传统公式.
结论:
- 混合人工智能模型为能源需求估计提供了实用,准确和灵活的解决方案.
- 基于性别的建模提高了预测的准确性和可靠性.
- 这种方法对推进健康和营养科学有很大的潜力.
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