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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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人工智能算法比较和排名用于羊的体重预测.

Ambreen Hamadani1, Nazir Ahmad Ganai2

  • 1National Institute of Technology, Srinagar, India. escritor005@gmail.com.

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机器学习算法使用农场数据准确预测羊的体重. 五大模型,包括MARS和贝叶斯脊回归,为农场繁荣和粮食安全提供了洞察力.

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科学领域:

  • 农业科学 农业科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 农场数据的指数增长需要先进的分析解决方案.
  • 人工智能 (AI) 提供了强大的能力来处理农业中的大型,非线性和杂的数据集.
  • 传统的数据分析方法有局限性,人工智能没有面临.

研究的目的:

  • 为了比较和排名流行的机器学习 (ML) 算法用于绵羊养殖场数据预测.
  • 评估ML模型在11年内对绵羊体重的预测准确度.
  • 确定最有效的ML技术,以加强农场管理和粮食安全.

主要方法:

  • 数据预处理包括清洁,准备和Winsorization以删除异常值.
  • 应用了尺寸缩小技术,如主要组件分析 (PCA) 和特征选择 (FS).
  • 评估了11个ML算法,使用PCA,PCA+FS和FS创建数据集,用于体重预测.

主要成果:

  • 马斯算法在真正和预测的绵羊体重之间实现了最高的相关性 (0.993).
  • 贝叶斯脊回归 (0.992) 和脊回归 (0.991) 也显示出高的预测准确度.
  • 预测体重的前五个表现最好的算法是MARS,贝叶斯脊回归,脊回归,支持向量机器和梯度增强算法.

结论:

  • 机器学习技术为羊的体重提供了准确的预测,有助于农场管理.
  • 这些ML模型可以支持对经济繁荣和绩效改进的数据驱动推断.
  • 准确的农场预测有助于通过优化农业实践增强粮食安全.