| Title |
A Machine Learning Approach for the Classification of Refrigerant Gases |
| ID_Doc |
18060 |
| Authors |
Argirusis, N; Konstantaras, J; Argirusis, C; Dimokas, N; Thanopoulos, S; Karvelis, P |
| Title |
A Machine Learning Approach for the Classification of Refrigerant Gases |
| Year |
2024 |
| Published |
Applied Sciences-Basel, 14, 14 |
| DOI |
10.3390/app14146230 |
| Abstract |
Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios. |
| Author Keywords |
machine learning; refrigerant gases; classification; environmental sustainability; gas detection |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001276598000001 |
| WoS Category |
Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied |
| Research Area |
Chemistry; Engineering; Materials Science; Physics |
| PDF |
https://doi.org/10.3390/app14146230
|