Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

QR Code

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such...

Full description

Bibliographic Details
Main Authors:	Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa
Format:	Article
Language:	English
Published:	Mdpi 2023
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/9021/ http://eprints.uthm.edu.my/9021/1/J15700_059e5b6d8fb9c3ee505a7faedffe6ac7.pdf

Similar Items

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
by: Waseem Alwan, Waseem Alwan, et al.
Published: (2023)

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
by: Alwan, Waseem, et al.
Published: (2023)

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
by: Alwan, Waseem, et al.
Published: (2023)

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
by: Alwan, Waseem, et al.
Published: (2023)

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
by: Alwan, Waseem, et al.
Published: (2023)

A new revised M-Of-K Run Rules Method in X-Bar control chart for estimated parameters.
by: Teh, Sin Yin
Published: (2016)

Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers
by: Lysiak, R., et al.
Published: (2014)

An improved scheme for online recognition of control chart patterns
by: Hassan, Adnan
Published: (2011)

Ensembles of diverse classifiers using synthetic training data
by: Akhand, M.A.H, et al.
Published: (2012)

Solving classification problem using ensemble binarization classifier
by: Zainudin, Muhammad Noorazlan Shah, et al.
Published: (2018)

A probabilistic model of classifier competence for dynamic ensemble selection
by: Woloszynski, Tomasz, et al.
Published: (2011)

Improvise the working condition at Kilang Beras Jelapang Selatan (M) Sdn. Bhd.
by: Tajurahim, Nurul Ainina Nadhirah, et al.
Published: (2020)

Control Charts With Estimated Process Parameters And A Proposed Coefficient Of Variation Chart
by: You , Huay Woon
Published: (2015)

Excess water production diagnosis in oil fields using ensemble classifiers
by: Rabiei, Minou
Published: (2011)

Extensions Of Multivariate Coefficient Of Variation Control Charts
by: Ayyoub, Heba Nasr Awwad
Published: (2021)

Human posture recognition classifiers
by: Htike, Kyaw Kyaw, et al.
Published: (2011)

Ensemble Of Multiple Matchers For Finger Vein Recognition
by: Soh, Siang Loong
Published: (2017)

A measure of competence based on randomized reference classifier for dynamic ensemble selection
by: Woloszynski, Tomasz, et al.
Published: (2010)

On two measures of classifier competence for dynamic ensemble selection - Experimental comparative analysis
by: Kurzynski, M., et al.
Published: (2010)

A comparative study of the ensemble and base classifiers performance in Malay text categorization
by: Alshalabi, Hamood Ali, et al.
Published: (2017)

Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
by: Sulaiman, Md. Nasir, et al.
Published: (2018)

Issues in development of artificial neural network-based control chart pattern recognition schemes
by: Masood, Ibrahim, et al.
Published: (2010)

Application of GaBi software in identifying potential environmental impacts in fabrication of 3D bone tissue engineering
by: Senusi, Farasyafinaz, et al.
Published: (2022)

Probabilistic approach to the dynamic ensemble selection using measures of competence and diversity of base classifiers
by: Lysiak, R., et al.
Published: (2011)

Physiological signals as predictors of mental workload: Evaluating single classifier and ensemble learning models
by: Nailul, Izzah, et al.
Published: (2023)

Majority voting of ensemble classifiers to improve shoreline extraction of medium resolution satellite images
by: Abd Manaf, Syaifulnizam, et al.
Published: (2017)

Recognition of contour invariants with neurofuzzy classifier
by: Shamsuddin, Siti Mariyam, et al.
Published: (2006)

Materials for Tribology’s Application: A Mini Analysis
by: Norazlianie, Sazali, et al.
Published: (2020)

Meta-classifier based on boosted approach for object class recognition
by: Manshor, Noridayu, et al.
Published: (2014)

Development Of Variable Sampling Interval Run Sum T Chart And Triple Sampling X ̅ Chart With Estimated Process Parameters
by: Nahar Mim, Faijun
Published: (2022)

Adaptive Control Charts For Monitoring The Univariate And Multivariate Coefficient Of Variation
by: Khaw, Khai Wah
Published: (2017)

Monitoring the coefficient of variation through a variable parameters chart
by: Lim, Chuen Yang
Published: (2017)

X-bar parameter triggering: approach to enhance syntax proficiency
by: Saeipoor, Neda, et al.
Published: (2011)

Developing grammar teaching approach based on X-bar theory
by: Saeipoor, Neda
Published: (2011)

Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
by: Rahman, Sam Matiur, et al.
Published: (2020)

Classifying the Zoo of Ultraluminous X-ray Sources
by: Soria, Roberto, et al.
Published: (2005)

Neural Network Classifiers For Mongolian Character Recognition
by: Batsaikhan, Osorkhuu
Published: (2003)

Neural Network Classifiers For Mongolian Character Recognition
by: Osorkhuu, Batsaikhan
Published: (2003)

A comparison between the performances of double sampling X and variable sample size X charts
by: Teoh, W.L, et al.
Published: (2014)

Carbon footprint of 3D-printed bone tissue engineering scaffolds: an life cycle assessment study
by: Tajurahim,, Nurul Ainina Nadhirah, et al.
Published: (2022)