scholarly journals Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Performance Optimization ◽  
Industrial Plant ◽  
Mathematical Treatment ◽  
Extraction Techniques ◽  
Peak Period ◽  
Linear Discriminant ◽  
Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

Discriminant Analysis for Biometric Recognition

2011 ◽  
pp. 25-29
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Sample Size ◽  
Linear Discriminant Analysis ◽  
Small Sample Size ◽  
Small Sample ◽  
Biometric Recognition ◽  
Linear Discriminant

This chapter is a brief introduction to biometric discriminant analysis technologies — Section I of the book. Section 2.1 describes two kinds of linear discriminant analysis (LDA) approaches: classification-oriented LDA and feature extraction-oriented LDA. Section 2.2 discusses LDA for solving the small sample size (SSS) pattern recognition problems. Section 2.3 shows the organization of Section I.

scholarly journals Comparison of Feature Extraction Techniques for Pattern Classification

2021 ◽  
Vol 9 (VI) ◽  
pp. 5511-5517
Author(s):  
Binu P Chacko
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Research Field ◽  
Svm Classifier ◽  
Discriminative Power ◽  
Extraction Techniques ◽  
Pattern Recognition Problem ◽  
Division Point ◽  
Research Problems

Pattern recognition is a challenging task in research field for the last few decades. Many researchers have worked on areas such as computer vision, speech recognition, document classification, and computational biology to tackle complex research problems. In this article, a pattern recognition problem for handwritten Malayalam character is presented. This system goes through two different stages of HCR namely, feature extraction and classification. Three feature extraction techniques – wavelet transform, zoning, division point – are used in this study. Among these, division is point is able to show best discriminative power using SVM classifier. All the experiments are conducted on size normalized and binarized images of isolated Malayalam characters.

ADAPTIVE KERNEL DISCRIMINANT ANALYSIS AND ITS APPLICATIONS ON PATTERN RECOGNITION

2006 ◽  
Vol 03 (04) ◽  
pp. 329-337
Author(s):  
JUN-BAO LI ◽  
JENG-SHYANG PAN
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Pattern Classification ◽  
Input Data ◽  
Feature Space ◽  
Extraction Techniques ◽  
Kernel Discriminant Analysis ◽  
Adaptive Kernel ◽  
Discriminatory Information

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. In this paper, we present an extension of KFD method based on the data-dependent kernel, called the adaptive kernel discriminant analysis (AKDA), for feature extraction and pattern classification. AKDA is more adaptive to the input data than KDA owing to the optimization of projection from input space to feature space with the data-dependent kernel, which enhances the performance of KDA. Experimental results on ORL, Yale and MNIST database show that the proposed AKDA gives the higher performance than KDA.

scholarly journals Facial recognition techniques using SVM: A comparative analysis

Enfoque UTE ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 98-111
Author(s):  
José Augusto Cadena Moreano ◽  
Nora Bertha La Serna Palomino ◽  
Alex Christian Llano Casa
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Facial Recognition ◽  
Human Being ◽  
Extraction Techniques ◽  
Work Activity ◽  
Recognition Process ◽  
Safety Work

This paper presents a literary review of facial recognition in 2D, which plays an important role in the life of the human being in terms of safety, work activity, etc. The focus is on the results obtained by some researchers with the application of feature extraction techniques, pattern classifiers, databases and their respective percentage of efficiency obtained. The objective is to determine efficient techniques that allow an optimal 2D facial recognition process, based on the quality of databases, feature extractors and pattern classifiers.

scholarly journals Numeral Recognition Using Statistical Methods Comparison Study

2011 ◽  
Vol 8 (1) ◽  
pp. 188-196
Author(s):  
Baghdad Science Journal
Keyword(s):  
Feature Extraction ◽  
Statistical Methods ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Arabic Numeral ◽  
Comparison Study ◽  
Extraction Techniques ◽  
Linear Discriminant ◽  
The World ◽  
Numeral Recognition

The area of character recognition has received a considerable attention by researchers all over the world during the last three decades. However, this research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic numeral using the Statistical styles in two methods and making comparison study between them. First method Linear Discriminant function that is yield results with accuracy as high as 90% of original grouped cases correctly classified. In the second method, we proposed algorithm, The results show the efficiency of the proposed algorithms, where it is found to achieve recognition accuracy of 92.9% and 91.4%. This is providing efficiency more than the first method.

scholarly journals Shape Feature Extraction Techniques for Fruits: A Review

2021 ◽  
pp. 2425-2430
Author(s):  
Israa Mohammed Hassoon
Keyword(s):  
Feature Extraction ◽  
Food Industry ◽  
Automated System ◽  
Shape Feature ◽  
Extraction Techniques ◽  
Quality Estimation ◽  
Advantages And Disadvantages ◽  
Quality Of Food ◽  
Shape Feature Extraction

          Fruits sorting, recognizing, and classifying are essential post-harvest operations, as they contribute to the quality of food industry, thereby increasing the exported quantity of food. Today, an automated system for fruit classification and recognition is very important, especially when exporting to markets where quality of fruit must be high. In this study, the advantages and disadvantages of the various shape-based feature extraction algorithms and technologies that are used in sorting, classifying, and grading of fruits, as well as fruits quality estimation, are discussed in order to provide a good understanding of the use of shape-based feature extraction techniques.

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