“…In many gas sensing applications, supervised learning methods showed tremendous success in improving the performance, robustness, and device reliability. 105 Different supervised algorithms such as support vector machine (SVM), random forest, XGBoost, Knearest neighbor (KNN), different neural networks are widely being implemented to address challenges like drifting, fault detection, calibration, and classification etc. [106][107][108][109][110] Models like SVM and KNN provide expected performance in online active learning applications even when encountering sensor drifting challenges.…”