A Review on Artificial Intelligence-based Sensor Array System for Detection of Harmful Gases of the Environment
Abstract
Human health and the environment both are affected by hazardous gases. So, recognizing these gases are very necessary for human health and the environment. The detection of these complex aroma samples is a very tough task. In our research work, we have proposed a sensor-based electronic nose system that can detect hazardous gases sample with subtle differences. We have found some hazardous gases, which are volatile organic gases like acetone, ethanol, propane, butane, methane, etc. to detect in the environment. The MOS (Metal Oxide Semiconductor) sensors MICS 5135, MICS 5521, TGS 2600, TGS 2602, TGS 2611, and TGS 2620 sets are used to detect these gases. The sensors array is remarkably responsive, accurate, low-effort, and low-power consuming that can differentiate the most significant harmful gasses. In this review, techniques that are used to detect these gases are pattern recognition like ANN (Artificial Neural Networks), PARAFAC (Parallel Factor Analysis), PLS (Partial Least Square), and PCA (Principal Component Analysis) with cross-validation. We have shown the comparison of these proposed techniques in this research paper. The algorithm will be developed using MATLAB on the proposed techniques.
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