1. S. L. Ullo and G. R. Sinha, “Advances in smart environment monitoring systems using IoT and sensors,” Sensors, vol. 20, no. 11, p. 3113, 2020.
2. L. Xue, Y. Li, and Y. Deng, “Multicomponent porous metal oxide semiconductors for advanced gas sensing,” Micro Nano Sci., vol. 1, p. 2, 2025.
3. Pathania, N. Dhanda, R. Verma, A.-C. A. Sun, P. Thakur, and A. Thakur, “Review-Metal oxide chemoresistive gas sensing mechanism, parameters, and applications,” ECS Sensors Plus, 2024.
4. J. Fonollosa, I. Rodríguez-Luján, and R. Huerta, “Chemical gas sensor array dataset,” Data in Brief, vol. 3, pp. 85–89, 2015.
5. Shahid et al., “Carbon based sensors for air quality monitoring networks; middle east perspective,” Front. Chem., vol. 12, 2024.
6. W. Reimringer and C. Bur, “Promoting quality in low-cost gas sensor devices for real-world applications,” Frontiers in Sensors, vol. 4, 2023.
7. D. Degler, U. Weimar, and N. Barsan, “Current understanding of the fundamental mechanisms of doped and loaded semiconducting metal-oxide-based gas sensing materials,” ACS Sensors, 2019.
8. W. Wang, C. Wang, K. Li, J. Wang, and X. Wang, “Advances in functional guest materials for resistive gas sensors,” RSC Advances, vol. 12, pp. 24614–24632, 2022.
9. S. M. Scott, D. James, and Z. Ali, “Data analysis for electronic nose systems,” Microchimica Acta, vol. 156, no. 3, pp. 183–207, 2006
10. C. Distante, M. Leo, P. Siciliano, and K. C. Persaud, “On the study of feature extraction methods for an electronic nose,” Sensors and Actuators B: Chemical, vol. 87, no. 2, pp. 274-288, 2002.
11. Fonollosa, S. Sheik, R. Huerta, and S. Marco, “Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring,” Sensors and Actuators B: Chemical, vol. 215, pp. 618-629, 2015.
12. F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1251–1258.
13. Dang, P. Pang, and J. Lee, “Depth-Wise separable convolution neural network with residual connection for hyperspectral image classification,” Remote Sensing, vol. 12, no. 20, p. 3408, 2020.
14. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
15. S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,” International Conference on Learning Representations (ICLR), 2016.
16. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. CVPR, 2018, pp. 4510-4520.
17. Lane, S. Bhattacharya, A. Mathur, P. Georgiev, C. Forlivesi, and F. Kawsar, “Squeezing deep learning into mobile and embedded devices,” IEEE Pervasive Computing, vol. 16, no. 3, pp. 82-88, 2017.
18. J. X. Leon-medina et al., “Joint distribution adaptation for drift correction in electronic nose type sensor arrays,” IEEE Access, vol. 8, pp. 134413–134421, 2020.
19. Amiri, M. R. V. Narayana, et al., “Nanostructured metal oxide-based acetone gas sensors: A review,” Sensors, vol. 20, no. 11, p. 3096, 2020.
20. L. Zhang and D. Zhang, “Domain adaptation extreme learning machine for drift compensation in e-nose systems,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 9, pp. 2510–2520, 2015.
21. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in International Conference on Machine Learning (ICML), 2015, pp. 97–105.
22. Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” Journal of Machine Learning Research, vol. 17, no. 59, pp. 1-35, 2016.
23. E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 2962–2971
24. B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, “Unsupervised visual domain adaptation using subspace alignment,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 2013.
25. Y. Zhu, F. Zhuang, J. Wang, J. Chen, and D. Wang, “Deep subdomain adaptation network for image classification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 1–13, 2020.
26. Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in International Conference on Machine Learning (ICML), 2015, pp. 1180–1189.
27. B. Sun and K. Saenko, “Deep CORAL: Correlation alignment for deep domain adaptation,” in Proc. ECCV Workshops, 2016.
28. Y. H. Kim, J. H. Lee, and S. O. Park, “Deep learning-based calibration and drift compensation for metal oxide semiconductor gas sensors in environmental internet of things,” IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12041–12052, 2021.
29. M. Jose and J. G. J. Olivier, “Drift compensation of commercial water quality sensors using machine learning to extend the calibration lifetime,” Journal of Ambient Intelligence and Humanized Computing, 2020.
30. X. Zhao, P. Li, K. Xiao, X. Meng, L. Han, and C. Yu, “Sensor drift compensation based on the improved LSTM and SVM multi-class ensemble learning models,” Sensors, vol. 19, no. 18, p. 3844, 2019.
31. Vergara et al., “Chemical gas sensor drift compensation using classifier ensembles,” Sensors and Actuators B: Chemical, vol. 166–167, pp. 320–329, 2012.
32. Dey, “Semiconductor metal oxide gas sensors: A review,” Materials Science and Engineering: B, vol. 229, pp. 206–217, 2018.
33. D. J. Blackwood, “An overview of gas sensing in semiconducting metal oxides,” Sensors and Actuators B: Chemical, vol. 147, no. 1, pp. 13–20, 2010.
34. Barsan and U. Weimar, “Conduction model of metal oxide gas sensors,” Journal of Electroceramics, vol. 7, no. 3, pp. 143-167, 2001.
35. Wang, L. Yin, Zhang L, D. Xiang, and R. Gao, “Metal oxide gas sensors: sensitivity and influencing factors,” Sensors, vol. 10, no. 3, pp. 2088–2106, 2010.
36. M. Kanan, O. M. El-Kadri, I. A. Abu-Yousef, and M. C. Kanan, “Semiconducting metal oxide based sensors for selective gas pollutant detection,” Sensors, vol. 9, no. 10, pp. 8158–8196, 2009.
37. Gurlo, “Interplay between O2 and SnO2, ZnO, and In2O3 surface: A key for understanding gas sensing mechanisms,” ChemPhysChem, vol. 7, no. 10, pp. 2041–2052, 2006.
38. D. Bartholomew and J. W. Morris, “Surface-to-volume ratios in nanostructured semiconductor metal oxides,” Journal of Physical Chemistry C, vol. 112, no. 14, pp. 5312–5319, 2008.
39. Romano-Filho, L. A. E. M. S. Santos, and R. T. G. de Oliveira, “Long-term drift and instability in metal oxide semiconductor gas sensors: A review of mitigation strategies,” Sensors and Actuators Reports, vol. 3, p. 100034, 2021.
40. G. F. Fine, L. M. Cavanagh, A. Afonja, and R. Binions, “Metal oxide semi-conductor gas sensors in environmental monitoring,” Sensors, vol. 10, no. 6, pp. 5469–5502, 2010.
41. S. R. Morrison, The Chemical Physics of Surfaces, 2nd ed. Plenum Press, 1990.
42. N. Yamazoe, “Toward new sensors for environmental monitoring and health care,” Sensors and Actuators B: Chemical, vol. 108, no. 1-2, pp. 2–14, 2005.
43. M. Batzill and U. Diebold, “The surface and materials science of tin oxide,” Progress in Surface Science, vol. 79, no. 2-4, pp. 47–154, 2005.
44. Z. L. Wang, “Nanostructures of zinc oxide,” Materials Today, vol. 7, no. 6, pp. 26–33, 2004
45. L. Linsebigler, G. Lu, and J. T. Yates Jr, “Photocatalysis on TiO2 surfaces: principles, mechanisms, and selected results,” Chemical Reviews, vol. 95, no. 3, pp. 735–758, 1995.
46. K. Varghese, D. Gong, M. Paulose, K. G. Ong, and C. A. Grimes, “Hydrogen sensing using titania nanotubes,” Sensors and Actuators B: Chemical, vol. 93, no. 1-3, pp. 338–344, 2003.
47. J. Ma, Y. Ren, X. Zhou, L. Liu, Y. Zhu, X. Cheng, P. Xu, X. Li, Y. Deng, and D. Zhao, “Silica-templated synthesis of ordered mesoporous tungsten oxide for highly sensitive and selective nitrogen dioxide detection,” Advanced Functional Materials, vol. 28, no. 6, p. 1705268, 2018.
48. Cantalini, H. T. Sun, M. Faccio, M. Pelino, S. Santucci, L. Lozzi, and M. Passacantando, “NO2 sensitivity of WO3 thin film sensors prepared by hot filament vacuum deposition,” Sensors and Actuators B: Chemical, vol. 31, no. 1-2, pp. 81–87, 1996.
49. M. J. S. P. L. Kumar and V. R. K. M. Rao, “Indium oxide nanostructures for low-temperature gas sensing applications: A review,” Ceramics International, vol. 47, no. 1, pp. 15–38, 2021.
50. Rudnitskaya, “Calibration update and drift correction for electronic noses and tongues,” Frontiers in Chemistry, vol. 6, p. 433, 2018.
51. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436-444, 2015
52. S. De Vito, E. Massera, M. Piga, L. Martinotto, and G. Di Francia, “On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring station,” Sensors and Actuators B: Chemical, vol. 129, no. 2, pp. 750–757, 2008.
53. G. Brereton and G. R. Lloyd, “Support vector machines for classification and regression,” Analyst, vol. 135, no. 2, pp. 230–267, 2010
54. O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci. Rep., vol. 14, no. 1, p. 6086, Mar. 2024
55. M. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011.
56. M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Information Processing & Management, vol. 45, no. 4, pp. 427–437, 2009.
57. T. Cover and J. Thomas, Elements of Information Theory, 2nd ed. Hoboken, NJ, USA: Wiley-Interscience, 2006.
58. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
