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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Weighted Mean00:57

Weighted Mean

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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.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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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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Improving Translational Accuracy02:07

Improving Translational Accuracy

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相关实验视频

Updated: Jan 11, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

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牛网-XAI:一个可解释的CNN框架,用于高效的牛体重估计.

Md Junayed Hossain1, Jannatul Ferdaus1, Ashraful Islam1

  • 1Center for Computational & Data Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.

PloS one
|November 13, 2025
PubMed
概括

精确的牛体重估计是自动使用定制卷积神经网络 (CNN) 模型,CattleNet-XAI. 这种深度学习方法显著提高了对传统方法的预测准确度,以更好地管理牲畜.

科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 手动估计牛的体重是不准确的,劳动密集型.
  • 传统的回归模型在重量预测的复杂图像数据上扎.
  • 需要自动化方法来实现高效和精确的畜牧管理.

研究的目的:

  • 开发一个高效和可解释的框架 (CattleNet-XAI) 用于牛的自动体重估计.
  • 将定制卷积神经网络 (CNN) 与其他模型的性能进行比较.
  • 通过先进的图像处理和深度学习来提高重量预测的准确性.

主要方法:

  • 开发了CattleNet-XAI,这是一个定制的CNN框架,具有先进的图像预处理.
  • 在传统的机器学习模型中使用YOLOv5进行特征提取.
  • 训练和评估了多种模型,包括CNN,EfficientNetB3,随机森林和线性回归.
  • 使用平均绝对误差 (MAE),平均平方误差 (MSE) 和根平均平方误差 (RMSE) 测量性能.

主要成果:

  • 定制的CNN模型 (3Conv3Dense变体) 实现了卓越的准确性.
  • 获得了18.02公斤的平均绝对误差 (MAE) 和19.85公斤的根平均平方误差 (RMSE).

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  • 与传统的机器学习和其他CNN模型相比,显示出显著的改进.

    • 结论:

    • 深度学习,特别是CNN,为牲畜体重估计提供了高度准确和自动化的解决方案.
    • CattleNet-XAI提供了一种有效且易于解释的方法,用于现代牛管理.
    • 自动化重量估计提高了农场管理,健康评估和生产率优化.