Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.7K
Weighted Mean00:57

Weighted Mean

4.9K
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...
4.9K
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

102
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
102
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.6K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

An Adaptive Partial Least-Squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging.

Sensors (Basel, Switzerland)·2024
Same author

Challenges and Prospects of Plant-Protein-Based 3D Printing.

Foods (Basel, Switzerland)·2023
Same author

Electromagnetic, Air and Fat Frying of Plant Protein-Based Batter-Coated Foods.

Foods (Basel, Switzerland)·2023
Same author

Design and Development of 'Diet DQ Tracker': A Smartphone Application for Augmenting Dietary Assessment.

Nutrients·2023
Same author

3D Food Printing Applications Related to Dysphagia: A Narrative Review.

Foods (Basel, Switzerland)·2022
Same author

Non-Destructive Assessment of Chicken Egg Fertility.

Sensors (Basel, Switzerland)·2020

相关实验视频

Updated: Jun 9, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

6.9K

处理农业食品数据分析中的不平衡问题

Adeyemi O Adegbenjo1,2, Michael O Ngadi1

  • 1Department of Bioresource Engineering, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, Montreal, QC H9X 3V9, Canada.

Foods (Basel, Switzerland)
|October 26, 2024
PubMed
概括

食品加工中的不平衡数据导致不准确的预测模型. 本研究提出了先进的人工智能方法,以提高分类准确性和在农业食品应用中采用模型.

关键词:
食品加工 食品加工 食品加工不平衡的数据不平衡的数据.创新的可采用性 创新的可采用性机器学习算法的算法

更多相关视频

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

9.6K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

12.3K

相关实验视频

Last Updated: Jun 9, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

6.9K
Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

9.6K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

12.3K

科学领域:

  • 农业科学 农业科学
  • 食品科学 食品科学 食品科学
  • 计算机科学 计算机科学

背景情况:

  • 不平衡的数据是机器学习的一个重大挑战,特别是在食品加工领域.
  • 像SVM这样的现有算法在罕见情况下扎,导致错误分类和不可靠的预测模型.
  • 这限制了农业食品行业采用新技术的可能性.

研究的目的:

  • 突出农业食品应用中不平衡数据的普遍性和影响.
  • 提出先进的人工智能 (AI) 技术,以有效处理不平衡的数据.
  • 评估在这个领域不平衡数据分析的合适指标.

主要方法:

  • 数据重新采样技术数据重新采样技术
  • 一个类学习方法的学习方法.
  • 集合方法 集合方法
  • 功能选择策略 功能选择策略
  • 深度学习模型深度学习模型
  • 对不平衡数据集的专业指标的评估.

主要成果:

  • 在农业食品环境中证明不平衡数据问题.
  • 提出人工智能驱动的解决方案,包括重新采样,一类学习,合体方法,特征选择和深度学习.
  • 评估适当的指标,以评估不平衡数据的模型性能.

结论:

  • 对不平衡数据的准确分析对于食品加工中稳健的模型开发至关重要.
  • 实施先进的AI技术和合适的指标可以提高模型的准确性和可靠性.
  • 改进的模型性能将增加农业食品部门对创新的接受和采用.