Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

5.1K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
5.1K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

6.5K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
6.5K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.0K
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...
3.0K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

6.8K
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...
6.8K
Weighted Mean00:57

Weighted Mean

6.0K
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...
6.0K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

98.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
98.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep Monocular Depth Estimation Based on Content and Contextual Features.

Sensors (Basel, Switzerland)·2023
Same journal

Parametric Modeling of Cochlear Electrode Arrays Using Design of Experiments and Finite Element Analysis (FEA).

Applied bionics and biomechanics·2026
Same journal

Mathematical Model Construction of the Bionic Irregular Surface of Turtle Shell.

Applied bionics and biomechanics·2026
Same journal

Finite Element Analysis on the Effect of Activation Time on Cervical Spine Biomechanical Response During Pilot Ejection.

Applied bionics and biomechanics·2026
Same journal

Locomotion Decoding (<i>LocoD</i>): An Open-Source and Modular Platform for Researching Control Algorithms for Lower Limb Assistive Devices.

Applied bionics and biomechanics·2026
Same journal

Research on the Coupled Bionic Design and Validation of Flying Car Folding Wings Based on Eurasian Eagle-Owl Wing Shape.

Applied bionics and biomechanics·2025
Same journal

Continuous Relative Phase Angle and Variability: A Crossover Analysis of Duration and Surface Effects While Long-Distance Running Over Treadmill and Over-Ground Running.

Applied bionics and biomechanics·2025
See all related articles

Related Experiment Video

Updated: Nov 12, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.4K

Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier.

Zainab N Ali1, Iman Askerzade1, Saddam Abdulwahab2

  • 1Computer Engineering, Engineering Faculty, Ankara University, 06830, Turkey.

Applied Bionics and Biomechanics
|March 17, 2021
PubMed
Summary
This summary is machine-generated.

This study developed a Fuzzy Weighted Relevance Vector Machine (FWRVM) model for wheat bread quality estimation. The FWRVM model achieved 96.67% accuracy, outperforming other classifiers.

More Related Videos

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.9K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

638

Related Experiment Videos

Last Updated: Nov 12, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.4K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.9K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

638

Area of Science:

  • Food Science and Technology
  • Machine Learning Applications
  • Data Analytics in Manufacturing

Background:

  • Accurate food product quality estimation is crucial for manufacturing processes.
  • Real-time sensory data collection and analysis are key to determining food properties.
  • Wheat bread quality assessment requires robust analytical methods.

Purpose of the Study:

  • To develop and evaluate an efficient data analytics model for wheat bread quality estimation.
  • To utilize sensory data for predicting the quality of baked food products.
  • To compare the performance of a novel Fuzzy Weighted Relevance Vector Machine (FWRVM) classifier against existing models.

Main Methods:

  • Collected real-time sensory data from 300 wheat bread samples over 15 days.
  • Applied dimensionality reduction using Linear Discriminant Analysis (LDA) on raw data.
  • Developed and implemented a Fuzzy Weighted Relevance Vector Machine (FWRVM) classifier in MATLAB.
  • Trained and tested the FWRVM model, comparing its performance with Support Vector Machine (SVM), RVM, and Deep Neural Networks (DNN).

Main Results:

  • The proposed FWRVM classifier achieved an estimation accuracy of 96.67%.
  • Performance metrics included 96.687% precision, 96.6% recall, and 96.6% F-measure.
  • The FWRVM model demonstrated superior performance compared to SVM, RVM, and DNN classifiers.
  • Processing time for the FWRVM model was recorded at 8.96726 seconds.

Conclusions:

  • The Fuzzy Weighted Relevance Vector Machine (FWRVM) is an effective tool for accurate wheat bread quality estimation.
  • The developed model offers a significant improvement over traditional classifiers for food quality assessment.
  • Real-time data analytics and advanced machine learning can enhance food manufacturing quality control.