Detecting Multimodal Data in Information System

The steady growth of multimodal data requires effective methods for managing and processing this type of information, including focusing on developing a system for detecting anomalies. This research mainly focuses on detecting anomalies and improving data quality in multimodal datasets. By integrating different data types, such as text, metadata, and images, this research examines the effectiveness of three fusion methods: early, late, and hybrid. Through a comprehensive analysis, the study demonstrates that late fusion significantly improves the accuracy of data processing and achieves the highest processing accuracy of 0.947, which is, on average, 6% higher than the early and hybrid fusion systems. The findings emphasize the potential of multimodal data systems to improve information systems’ reliability, accuracy, and efficiency, thereby supporting more informed decision-making in dynamic environments.