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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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 number is...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Related Experiment Videos

CrayStack: a simplified crayfish optimization driven stacking ensemble for prediction of machining quality

Ithipri Emonena1, Xu Yong2, Festus I Ashiedu1

  • 1Department of Mechanical Engineering, Federal University of Petroleum Resources, P.M.B, Effurun, 1221, Delta State, Nigeria.

Scientific Reports
|June 18, 2026
PubMed
Summary

This study introduces the CrayStack ensemble model for predicting machining quality with limited data. The model significantly improves prediction accuracy and speed, offering a practical solution for intelligent manufacturing.

Keywords:
Crayfish Optimization AlgorithmEnsemble learningMachining process modelingProcess InnovationStacking

Related Experiment Videos

Area of Science:

  • Manufacturing Engineering
  • Machine Learning
  • Data Science

Background:

  • Intelligent manufacturing requires accurate prediction of machining quality characteristics, which is challenging with limited experimental data.
  • Conflicting behaviors among machining quality characteristics further complicate accurate predictions.

Purpose of the Study:

  • To develop an accurate and efficient ensemble model for predicting machining quality characteristics using limited experimental data.
  • To compare the performance of the developed ensemble model against individual machine learning models.

Main Methods:

  • Collected Taguchi L27 experimental data with 6 influencing variables and 8 machining quality characteristics.
  • Developed five base learners: Gaussian Process Regression (GPR), Least Squares Boosting (LSB), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost).
  • Utilized metaheuristic algorithms (Genetic Algorithm, Particle Swarm Optimization, Crayfish Optimization Algorithm) to determine optimal weights for base learners, leading to the CrayStack ensemble model.

Main Results:

  • The CrayStack ensemble model, combining GPR, SVR, LSB, and Crayfish Optimization Algorithm, outperformed all individual learners across eight machining quality characteristics.
  • Achieved a mean absolute percent error of 9.8% across nine test cases, significantly lower than individual models.
  • Demonstrated near-instantaneous inference speeds (0.003 ms/sample), suitable for real-time monitoring.

Conclusions:

  • The CrayStack ensemble model provides a robust and accurate solution for predicting machining quality characteristics with limited experimental datasets.
  • The developed model offers practical utility for industrial process optimization in intelligent manufacturing.
  • The Crayfish Optimization Algorithm efficiently optimized ensemble weights, contributing to superior prediction performance.