使用基于鱼优化的集体学习框架,预测乳品种小牛第一次分娩时的年龄
Tewodros Shekure1, Hussien Seid Worku2, Sudhir Kumar Mohapatra3
1Artificial Intelligent and Robotics, Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Scientific reports
|December 27, 2024
概括
使用机器学习预测埃塞俄比亚乳牛犊的第一个分娩年龄,可以帮助弥合牛奶供应差距. 优化的模型实现了98.3%的准确性,提高了奶牛养殖的效率.
科学领域:
- 动物科学动物科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 埃塞俄比亚面临着日益严重的乳制品需求供应差距,原因是料短缺,成本高昂以及当地品种遗传不佳.
- 现代育种设施和先进技术,如大数据分析和机器学习,对于应对这些挑战至关重要.
研究的目的:
- 开发一种预测模型,用于断奶小牛的第一个产卵年龄.
- 利用断奶前和断奶前的参数来准确预测年龄.
主要方法:
- 使用支持向量回归 (SVR),线性SVR (LSVR) 和Nu SVR开发预测模型.
- 对SVR模型进行超参数调整,达到96.46%的精度.
- 使用鱼优化技术和SVR,LSVR和NuSVR的整体建模的功能优化.
主要成果:
- 优化的SVR模型实现了96.46%的精度.
- 在优化特征上训练的整体模型达到98.3%的卓越精度.
- 开发的模型有效地使用小牛和大参数预测了第一个分娩时的年龄.
结论:
- 机器学习,特别是集体SVR,提供了一种强大的工具,用于预测乳牛犊的第一个分娩年龄.
- 准确的预测可以帮助优化埃塞俄比亚的育种策略和提高乳制品生产效率.
- 这种方法可以通过加强畜牧管理来减轻牛奶供应赤字.
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