Multimodal Content Analysis for Enhanced User Experience

The aim of the study was to develop methods of multimodal content analysis to improve the user experience in interactive systems. The study reviewed existing approaches to multimodal content analysis, established criteria for developing new methods, and provided specific examples of practical application of the developed methods in various fields, demonstrating their effectiveness and potential in real-world conditions. The main results consisted of the development of methods, in particular, integration and synchronization of modalities, which can demonstrate high efficiency in medical diagnostics. In turn, the personalization of user experience in streaming services increases user satisfaction through relevant recommendations, while ensuring data privacy and security meet modern regulatory requirements in the healthcare sector. The paper also examines the integration of modalities, which includes convolutional neural networks for image and video processing, recurrent neural networks for text and audio processing, and attention mechanisms for highlighting important parts of the data. Multimodal analysis involves the processing and integration of data from different sources, which provides a complete picture to improve the analysis. The developed methods outperform most existing approaches, providing higher accuracy, speed, robustness to noise, and incomplete data.