A hybrid approach for automated quality control combining learning vector quantization neural networks and fuzzy logic

2003 ◽  
Author(s):  
O. Castillo ◽  
R. Cardona ◽  
P. Melin
Keyword(s):  
Neural Networks ◽  
Quality Control ◽  
Fuzzy Logic ◽  
Vector Quantization ◽  
Hybrid Approach ◽  
Learning Vector Quantization ◽  
Automated Quality Control

scholarly journals Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shahenda Sarhan ◽  
Aida A. Nasr ◽  
Mahmoud Y. Shams
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Vector Quantization ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Vector Quantization ◽  
Majority Voting ◽  
Support Vector ◽  
Reliability And Robustness

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.

Discrimination of wines based on 2D NMR spectra using learning vector quantization neural networks and partial least squares discriminant analysis

2006 ◽  
Vol 558 (1-2) ◽  
pp. 144-149 ◽  
Author(s):  
Saeed Masoum ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
Joseph Vercauteren ◽  
Mehdi Jalali-Heravi ◽  
Douglas Neil Rutledge
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Vector Quantization ◽  
Nmr Spectra ◽  
2D Nmr ◽  
Learning Vector Quantization ◽  
2D Nmr Spectra
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