17 citations as recorded by crossref.

1. A review on preparation methods and measurement techniques of reference gas mixtures . Komal & D. Soni 10.1016/j.microc.2023.109742
2. Integrating Dynamic State Information to Enhance Gas Concentration Modeling Accuracy T. Wang et al. 10.1109/JSEN.2024.3388208
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4. Measuring Hydrogen in Indoor Air with a Selective Metal Oxide Semiconductor Sensor C. Schultealbert et al. 10.3390/atmos12030366
5. Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air T. Baur et al. 10.3390/atmos12050647
6. Assessment of harmful gases emission and its impact using IoT and geospatial technology U. Elangovan et al. 10.1016/j.measurement.2024.115966
7. Comparison of Transfer Learning and Established Calibration Transfer Methods for Metal Oxide Semiconductor Gas Sensors Y. Robin et al. 10.3390/atmos14071123
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9. High-Performance VOC Quantification for IAQ Monitoring Using Advanced Sensor Systems and Deep Learning Y. Robin et al. 10.3390/atmos12111487
10. Studying the effects of siloxane poisoning on a SnO2 metal oxide semiconductor gas sensor in temperature cycled operation enabling self-monitoring and self-compensation C. Schultealbert et al. 10.1515/teme-2023-0065
11. A deep learning approach for gas sensor data regression: Incorporating surface state model and GRU-based model Y. Zhuang et al. 10.1063/5.0160983
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13. Qualification and optimisation of a gas mixing apparatus for complex trace gas mixtures D. Arendes et al. 10.1515/teme-2023-0075
14. Global calibration models for temperature-modulated metal oxide gas sensors: A strategy to reduce calibration costs A. Miquel-Ibarz et al. 10.1016/j.snb.2021.130769
15. Local dynamic neural network for quantitative analysis of mixed gases J. Li et al. 10.1016/j.snb.2023.135230
16. Deep Learning Based Calibration Time Reduction for MOS Gas Sensors with Transfer Learning Y. Robin et al. 10.3390/atmos13101614
17. Review on Algorithm Design in Electronic Noses: Challenges, Status, and Trends T. Liu et al. 10.34133/icomputing.0012