# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 4 Results and Discussion  
The multimodal AI-based fusion model for gas detection and is presented in this work. Two modalities, namely, thermal images and gas sensor measurements, are considered in this work of gas detection. The CNN architecture is applied for extracting features from the thermal images, whereas, LSTM framework is used for extracting features from the sequences of gas sensor measurements. The implementation of the proposed model in done using the Python 3 using Keras framwork on TensorFlow platform. Open source Google Colab GPU is used for training and testing of the proposed model. It is based on Intel Xeon Processor with 13 GB RAM. The CNN model starts converging at around the 20 th epoch, whereas LSTM reaches convergence at around the 90 th epoch. It was observed that the fused model stabilizes at around the 20 th epoch itself. The accuracy of the gas sensor array is comparatively lower since the outcome of one sensor (out of 7 sensors considered) is typically not very accurate due to the mixing of gases in the air. The thermal camera-based model individually performs comparatively better; however, in the air, the thermal signature of gaseous emissions may be generated due to multiple gases or multiple sources of exhausts, and having a gas sensor to validate the type of gas is extremely helpful in identification. It was noticed and observed that the individual models are underperforming compared to the fusion models. In the fusion models, the individual modalities either collaborate or oppose the outcomes of the individual modality, thereby making the system more reliable and accurate. By performing regularization techniques on individual models, namely CNN and LSTM, testing accuracy of 93% with Thermal Images and 82% for Gas Sequences is achieved. However, the early fusion of features from both CNN and LSTM has provided the testing accuracy of 96%, which is greater than individual models accuracies. In the case of late fusion (max fusion and average fusion) the accuracy was observed to be around 96%.  
Table 3 shows the individual training and testing accuracy, loss, precision, recall, and F1 scores for all four classes considered in this study. The accuracy comparison for the individual models is shown in Figure 8. It can be observed that the fusion models outperform the individual models as the predictions in the individual models are based on  
Figure 7: Framework for the proposed Late fusion model.  
both the modality data. It can also be noticed that the accuracy of early fusion is slightly higher than the late fusion models as in this case the fusion happens at the feature level which allows the interaction amongst the modalities. The confusion matrices are also plotted for all the frameworks and are shown in Figure 9.  
The fusion model is trained for 300 epochs, and accuracy and loss curves are analyzed and provides better performance than individual models.  
It is evident from the confusion matrices that the false positives and false negatives obtained from the fusion models are considerably lower than the individual models. Hence it can be concluded that the fusion models are outperforming the individual models. Also, the higher testing accuracy of the fusion models demonstrates that the resultant fusion system is more robust and reliable than individual models and performs the task of gas identification and classification with superiority. Analytically, false positives and false negatives appear due to various aspects of the model and data. Primary reason could be the mixing of gases to an extent which makes it difficult for the model to clearly classify. The majority of the false predictions are arriving because of moderate probability of predicting a class. A model can be trained rigorously using the more and varied data samples to solve the false prediction due to boundary line probabilities.  
Figure 8: Accuracy comparison of different models.