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在乳牛中使用循环神经网络预测分娩到受孕间隔 (开放日)

Mahdi Ravakhah^1,2, Mohammad Alishahi^1,2, Mohammad Mahdi Gheysari Gholami³

¹Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

The Journal of dairy research

|December 23, 2025

概括

长期短期记忆 (LSTM) 网络准确地预测奶牛奶牛的情况.

科学领域:

牛奶养殖牛奶养殖牛奶养殖
机器学习是机器学习.
动物科学动物科学

背景情况:

优化"开放日" (DO) 对奶牛场的利能力和动物福利至关重要.
机器学习为预测DO提供了潜力,有助于农场管理和决策.

研究的目的:

评估基于序列的长短期内存 (LSTM) 架构在预测下一个"开放日" (DO) 的有效性.
将LSTM性能与前多层感知子和使用暂时有效预测器的Cox模型进行比较.

主要方法:

使用了一个包含 16,472 个乳品繁殖记录的数据集.
我们比较了feed-forward神经网络,包括LSTM在内的两种类型的反复神经网络 (RNNs),以及Cox模型.
根据平均绝对误差和一致性评估模型性能.

主要成果:

与其他模型相比,LSTM架构在预测下一个"开放日"方面表现出卓越的准确性.
循环神经网络 (RNN) 的表现明显优于前和传统的统计方法.
LSTM捕捉时间模式的能力是其增强预测性能的关键.

结论:

基于序列的LSTM模型对于预测奶牛的"开放日"非常有效.
对于这个预测任务,RNN,特别是LSTM,比传统方法具有显著的优势.
使用机器学习准确的DO预测可以提高奶牛养殖场管理,疾病预防和杀决策.

关键词:

开放日开放日开放日深度学习是一种深度学习.机器学习是机器学习.精密奶牛养殖精密奶牛养殖经常性的神经网络.

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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.