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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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评估机器学习算法来预测乳牛的情况.

Rajesh Neupane1, Ashrant Aryal2, Angelika Haeussermann3

  • 1Department of Animal Science, Texas A&M University, College Station, Texas, United States of America.

PloS one
|July 18, 2024
PubMed
概括

使用加速度计数据的机器学习模型可以识别乳牛的情况. ROCKET分类器在检测需要治疗性爪子修剪的奶牛和分类的严重程度方面显示出高准确度.

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科学领域:

  • 动物科学动物科学
  • 兽医医学 兽医医学 兽医医学
  • 数据科学数据科学数据科学

背景情况:

  • 乳牛是商业农业的一个重大问题,影响动物福利和生产力.
  • 步行异常是的关键指标,促使使用精确技术,如加速度计监测.
  • 机器学习 (ML) 提供了在奶牛群中自动和准确检测的潜力.

研究的目的:

  • 用加速计数据评估机器学习算法的有效性,以识别乳牛的情况.
  • 为了比较不同ML模型和功能集的性能,用于的分类.
  • 评估细粒状疾病分类和严重程度分级的潜力.

主要方法:

  • 310只荷尔斯坦奶牛配备了基于腿部的加速度计,以收集躺时间,每日步骤和每日运动的数据.
  • 奶牛被分为纠正爪修剪 (CCT),治疗爪修剪 (TCT) 为,或健康的对照.
  • 数据经过中位过,并使用随机森林,天真贝叶斯,后勤回归和ROCKET ML算法使用三个特征集 (常规,斜率和所有特征).

主要成果:

  • 在对需要CCT和TCT的奶牛进行分类时,ROCKET分类器获得了高精度 (>90%),ROC-AUC (>74%) 和F1分数 (>0.61) .
  • 整合斜率特征和使用所有特征 (传统 + 斜率) 显著改善了算法性能.
  • 此外,ROCKET分类器在将运动分数分类为严重和中度的条件时也表现出令人满意的准确性 (>0.85).
  • 在目前的模型中,感染性与非感染性的分类并不有效.

结论:

  • 机器学习模型,特别是带有加速度计数据的ROCKET分类器,是识别奶牛的有效工具.
  • 从加速度计数据中获得的斜率特征的使用提高了脚检测的准确性.
  • 需要进一步的研究来提高ML模型的颗粒度和准确性,以便更精确地分类和疾病差异化.