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Article
Publication date: 5 December 2017

Rabeb Faleh, Sami Gomri, Mehdi Othman, Khalifa Aguir and Abdennaceur Kachouri

In this paper, a novel hybrid approach aimed at solving the problem of cross-selectivity of gases in electronic nose (E-nose) using the combination classifiers of support…

Abstract

Purpose

In this paper, a novel hybrid approach aimed at solving the problem of cross-selectivity of gases in electronic nose (E-nose) using the combination classifiers of support vector machine (SVM) and k-nearest neighbors (KNN) methods was proposed.

Design/methodology/approach

First, three WO3 sensors E-nose system was used for data acquisition to detect three gases, namely, ozone, ethanol and acetone. Then, two transient parameters, derivate and integral, were extracted for each gas response. Next, the principal component analysis (PCA) was been applied to extract the most relevant sensor data and dimensionality reduction. The new coordinates calculated by PCA were used as inputs for classification by the SVM method. Finally, the classification achieved by the KNN method was carried out to calculate only the support vectors (SVs), not all the data.

Findings

This work has proved that the proposed fusion method led to the highest classification rate (100 per cent) compared to the accuracy of the individual classifiers: KNN, SVM-linear, SVM-RBF, SVM-polynomial that present, respectively, 89, 75.2, 80 and 79.9 per cent as classification rate.

Originality/value

The authors propose a fusion classifier approach to improve the classification rate. In this method, the extracted features are projected into the PCA subspace to reduce the dimensionality. Then, the obtained principal components are introduced to the SVM classifier and calculated SVs which will be used in the KNN method.

Details

Sensor Review, vol. 38 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 1 October 2021

Rabeb Faleh, Sami Gomri, Khalifa Aguir and Abdennaceur Kachouri

The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments…

Abstract

Purpose

The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors.

Design/methodology/approach

To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array.

Findings

The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF.

Originality/value

Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.

Details

Sensor Review, vol. 41 no. 5
Type: Research Article
ISSN: 0260-2288

Keywords

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