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Synergistic Integration of Machine Learning with Microstructure/Composition-Designed SnO 2 and WO 3 Breath Sensors.
- Source :
-
ACS sensors [ACS Sens] 2024 Jan 26; Vol. 9 (1), pp. 182-194. Date of Electronic Publication: 2024 Jan 11. - Publication Year :
- 2024
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Abstract
- A high-performance semiconductor metal oxide gas sensing strategy is proposed for efficient sensor-based disease prediction by integrating a machine learning methodology with complementary sensor arrays composed of SnO <subscript>2</subscript> - and WO <subscript>3</subscript> -based sensors. The six sensors, including SnO <subscript>2</subscript> - and WO <subscript>3</subscript> -based sensors and neural network algorithms, were used to measure gas mixtures. The six constituent sensors were subjected to acetone and hydrogen environments to monitor the effect of diet and/or irritable bowel syndrome (IBS) under the interference of ethanol. The SnO <subscript>2</subscript> - and WO <subscript>3</subscript> -based sensors suffer from poor discrimination ability if sensors (a single sensor or multiple sensors) within the same group (SnO <subscript>2</subscript> - or WO <subscript>3</subscript> -based) are separately applied, even when deep learning is applied to enhance the sensing operation. However, hybrid integration is proven to be effective in discerning acetone from hydrogen even in a two-sensor configuration through the synergistic contribution of supervised learning, i.e., neural network approaches involving deep neural networks (DNNs) and convolutional neural networks (CNNs). DNN-based numeric data and CNN-based image data can be exploited for discriminating acetone and hydrogen, with the aim of predicting the status of an exercise-driven diet and IBS. The ramifications of the proposed hybrid sensor combinations and machine learning for the high-performance breath sensor domain are discussed.
- Subjects :
- Humans
Algorithms
Hydrogen
Machine Learning
Acetone
Irritable Bowel Syndrome
Subjects
Details
- Language :
- English
- ISSN :
- 2379-3694
- Volume :
- 9
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- ACS sensors
- Publication Type :
- Academic Journal
- Accession number :
- 38207118
- Full Text :
- https://doi.org/10.1021/acssensors.3c01814