@inproceedings{7dfc27f33b04467e9f6aa1064005ae21,
title = "Averaging neural network ensembles model for quantification of volatile organic compound",
abstract = "After a certain period of time, there is a change in response of the gas sensors, which is caused by drift. This change in response of the gas sensors causes deterioration which makes the artificial intelligence algorithms worthless for prediction. As the algorithms are trained on data without drift and once the effect of drift starts causing an error in prediction, the system needs re-calibration, which is a cumbersome process. Neural Networks (NN) are proved to have the capability of solving many complex problems in different fields. In this paper, an averaging Neural Network ensemble model is proposed to compensate the effect of drift in gas sensors and is tested for quantification of Industrial gases. The dataset used for validating the proposed model is a large scale experimental data, available online.",
keywords = "Concentration estimation, Electronic nose, Ensemble learning, Neural Networks, Sensors drift",
author = "Rehman, {Atiq Ur} and Amine Bermak",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019 ; Conference date: 24-06-2019 Through 28-06-2019",
year = "2019",
month = jun,
doi = "10.1109/IWCMC.2019.8766776",
language = "English",
series = "2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "848--852",
booktitle = "2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019",
address = "United States",
}