Deep learning (DL) has significantly advanced Raman spectra analysis, achieving high accuracy and efficiency. However, their complexity and opacity limit their application in areas where understanding and transparency are essential. To address this, we present XAI‐2DCOS, an innovative eXplainable Artificial…
Explainable artificial intelligence (XAI) has gained significant attention in various domains, including natural and medical image analysis. However, its application in spectroscopy remains relatively unexplored. This systematic review aims to fill this gap by providing a comprehensive overview of the…
Interpretability is just as important as accuracy when it comes to complex models, especially in the context of deep learning models. Explainable artificial intelligence (XAI) approaches have been developed to address this problem. The literature on XAI for spectroscopy mainly emphasizes independent…
Columbus, Ohio: American Chemical Society, 2024-09-18
Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural…