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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.

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.

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.

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.

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.

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.

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.

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.

This study aims to explore the antecedents of donors’ attitudes toward fundraising campaigns to fight COVID-19 in the United Arab Emirates (UAE) during the pandemic crisis. This manuscript identified how moderating effects of ethical dimensions can strengthen the relationship between trust in charity and charity projects with their attitude to raise funds to mitigate pandemic repercussions.

This study follows a quantitative approach by administering survey instruments to collect the data from the sample of respondents. A total of 391 responses were obtained adopting snowball sampling and analyzed through structural equation modeling (SEM) to derive meaningful results for path analysis.

The findings of this study indicate that certain insights need to be considered to trigger the donors’ attitude toward raising or participating in charity-oriented campaigns, especially during pandemic situations. For instance, organizing more transformable processes in charity projects and establishing more trust factors among donors is highly essential in charity activities. Similarly, promoting ethical dimensions of the donors toward supporting the vulnerable more effectively and encouraging them to participate or organize philanthropic activities certainly benefit and support this noble cause.

This study will help the government and nonprofit organizations in devising their campaigns for raising funds. The findings of this study suggest that ethics is an important consideration and driver for donors in philanthropy-serving organizations and individuals.

This research contributes to the literature on donation and philanthropic studies focusing on fundraising campaigns attitudes during COVID-19. This study contributes influential factors and attitudes of individuals and organizations toward charity and philanthropic service.