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• W4285123772 abstract "An electronic nose (E-nose) based on three metal oxide semiconductor (MOS) gas sensors is designed to quantitatively analyse six types of volatile organic compounds (VOCs). Support vector machine (SVM), extreme learning machine (ELM), and back-propagation (BP) neural network, are used to design different classifiers and regressors for integrating a suitable pattern recognition model of the E-nose system. By using the output of the classifier as one of the input features of the regressor, the models can predict the concentration of different types of VOCs at the same time. The 5-fold cross-validation is applied to search optimal parameters of each model and the independent test is conducted to evaluate the generalization performance. A pipeline is used to connect the best classifier and the best regressor, constructing an integrated model for pattern recognition. The integrated model based on ELM-ELM structure exhibits the best performance. Classification accuracy can be as high as 99% and the R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> score of regression as high as 0.97 in the 5-fold cross-validation. Classification accuracy is up to 93% and the R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> score of regression is up to 0.94 in the independent tests. Furthermore, the integrated model has prominent training and test efficiency, of which time consumptions are both shorter than 0.1 s. This work provides an approach to design an efficient integrated model with high performance for gas type identification and concentration prediction in the E-nose system." @default.
• W4285123772 created "2022-07-14" @default.
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• W4285123772 date "2022-07-15" @default.
• W4285123772 modified "2023-10-16" @default.
• W4285123772 title "Classification and Concentration Prediction of VOCs With High Accuracy Based on an Electronic Nose Using an ELM-ELM Integrated Algorithm" @default.
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• W4285123772 doi "https://doi.org/10.1109/jsen.2022.3176647" @default.
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