@ARTICLE{Deepan_S._Toxic_2023,
 author={Deepan, S. and Saravanan, M.},
 volume={vol. 69},
 number={No 3},
 journal={International Journal of Electronics and Telecommunications},
 pages={499-506},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={With rapid population increases, people are facing the challenge to maintain healthy conditions. One of the challenges is air pollution. Due to industrial development and vehicle usage air pollution is becoming a high threat to human life. This air pollution forms through various toxic contaminants. This toxic contamination levels increase and cause severe damage to the living things in the environment. To identify the toxic level present in the polluted air various methods were proposed by the authors, But failed to detect the tolerance level of toxic gases. This article discusses the methods to detect toxic gasses and classify the tolerance level of gasses present in polluted air. Various sensors and different algorithms are used for classifying the tolerance level. For this purpose “Artificial Sensing Methodology” (ASM), commonly known as e-nose, is a technique for detecting harmful gases. SO2-D4, NO2-D4, MQ-135, MQ136, MQ-7, and other sensors are used in artificial sensing methods (e-nose). “Carbon monoxide, Sulfur dioxide, nitrogen dioxide, and carbon dioxide” are all detected by these sensors. The data collected by sensors is sent to the data register from there it is sent to the Machine learning Training module (ML) and the comparison is done with real-time data and trained data. If the values increase beyond the tolerance level the system will give the alarm and release the oxygen.},
 type={Article},
 title={Toxic Gases Detection and Tolerance Level Classification Using Machine Learning Algorithms},
 URL={http://czasopisma.pan.pl/Content/128288/PDF/12-3968-Deepan-sk1.pdf},
 doi={10.24425/ijet.2023.146498},
 keywords={Artificial Sensing Methodology, Machine Learning, Toxic gases, Tolerance Detections},
}