  Gas recognition technology has received considerable attention from
researchers in recent years. Nevertheless, the gas recognition area has faced
obstacles in implementing deep learning-based recognition solutions due to the
absence of standardized protocols. To tackle this problem, we suggest using two
sets of specialized evaluation measures for gas recognition algorithms. These
metrics will make it easier to examine the performance of these algorithms on
various datasets. In addition, we provide a new model called the Wavelet
Attention GRU (WAG), which is based on the wavelet attention mechanism. This
method facilitates the more efficient retrieval of sensor signals. Compared to
other models, WAG significantly decreases the number of sensors needed by 75%
while obtaining an identification accuracy of 98.33%. This suggests that WAG is
a potential approach for advancing gas recognition algorithms.
