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相关概念视频

Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用机器学习算法预测肉中伤的建模

Pranee Pirompud1, Panneepa Sivapirunthep2, Veerasak Punyapornwithaya3

  • 1Doctoral Program in Innovative Tropical Agriculture, Department of Agricultural Education, Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

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概括

机器学习模型可以预测肉的伤. 极端梯度提升 (XGB) 算法在识别高伤风险,帮助福利和经济结果方面表现最好.

关键词:
极端梯度增强藏时间模型性能重要性的变量

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科学领域:

  • 动物科学
  • 农业工程
  • 数据科学

背景情况:

  • 肉的伤是家禽生产的一个重要问题,对动物福利和经济回报产生了负面影响.
  • 在养殖,捕捞,运输和屠宰过程中的身体压力和伤害导致了尸体的伤.

研究的目的:

  • 为了分类肉的伤风险 (低或高百分比的尸体伤) 每辆卡车.
  • 为了比较六种机器学习模型的预测性能,
  • 提供决策工具以加强福利监测和减少经济损失.

主要方法:

  • 评估了六种机器学习模型:LASSO,CT,RF,NB,SVM和XGB.
  • 使用了26,031辆卡车的数据集,其中包括从养到屠宰阶段的14个预测因素.
  • 在没有重新采样的情况下训练模型,因为自然分布的伤率很高 (41.4%的卡车载荷).

主要成果:

  • 与其他五种模型相比,极度梯度提升 (XGB) 算法显示出优异的预测性能.
  • 伤的关键预测因素包括平均体重,运输时间,养密度 (和箱子),运输距离,死亡率/屠宰率,养时间和养时间.
  • 整体预测准确度中等,可能是由于缺少关键变量.

结论:

  • 机器学习,特别是XGB,是早期识别容易受伤害的肉群的宝贵工具.
  • 有针对性的管理干预措施,重点关注鸟类的健康,养密度,养时间和息时间,可以有效地缓解伤.
  • 实施这些战略可以显著提高商业肉生产的动物福利和运营效率.