When explainable artificial intelligence meets data governance: Enhancing trustworthiness in multimodal gas classification

Abstract

In the domain of artificial intelligence, the incorporation of multimodal data has become increasingly popular as a method to improve the performance of models by offering a more comprehensive range of information for learning. The gas classification, which is of utmost importance in multiple fields like industry, security, and healthcare, has received considerable interest. Nevertheless, a review of current research indicates that numerous studies suffer from inadequate data governance, resulting in subpar performance despite how complicated their proposed methodologies are. Although explainable artificial intelligence (XAI) techniques are gaining recognition for their ability to assist researchers in analyzing and enhancing model performance, their use in multimodal gas classification is still limited. This research presents a method that integrates strong data governance practices with XAI to improve the accuracy of models in classifying different types of gases using multimodal input. Our approach enhances data quality and offers a very efficient model architecture with a minimal parameter number of 0.8 million. The proposed model attains testing accuracies of 98.49% for the sensor modality, 96.48% for the image modality, and 99.4% for the fusion modality, surpassing the maximum accuracy achieved by existing state-of-the-art models, which stands at 99.2% with 106 million parameters. Notably, our model is 132.5 times smaller than the most accurate model currently used in multimodal gas classification studies. The proposed model’s robustness and trustworthiness are confirmed by extensive testing. The results demonstrate that our approach makes a significant contribution to the field of multimodal classification.