MCDC-GAN: A Generative Adversarial Framework for Efficient Diagnosis of Hazardous Gas Emissions

DOI

10.2991/978-94-6239-616-6_51How to use a DOI?

Keywords

Gas-Leakage; Thermal-Image; MC-DCGAN

Abstract

Gas leakage is the most common aspect to be considered in home appliances, industries, coal mines, transportation, and so on. Identification of this gas leakage at an early stage is most important as intervention by humans is always impossible due to the nature of the gas, which is colourless and odourless, to avoid chemical accidents in the surrounding environment. Sensors were used to detect these hazardous gases. Thermal cameras can also be used to detect the emission of these gases, which can measure minute differences in temperature and work in a dark environment. In this paper, the MCDC-GAN model with LR scheduler is applied to the benchmark dataset and obtains an accuracy of 96%, 95% sensitivity, and 97% specificity on the public dataset.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. Madhuram
AU  - R. Kalpana
PY  - 2026
DA  - 2026/03/31
TI  - MCDC-GAN: A Generative Adversarial Framework for Efficient Diagnosis of Hazardous Gas Emissions
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 685
EP  - 694
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_51
DO  - 10.2991/978-94-6239-616-6_51
ID  - Madhuram2026
ER  -