INTEGRATION OF HETEROGENEOUS DATA USING ARTIFICIAL INTELLIGENCE METHODS

Abstract

Modern AI development and multimodal data analysis methods are gaining critical importance due to their ability to integrate information from diverse sources, including text, audio, sensor signals, and images. Such integration enables systems to form a richer and more context-aware understanding of complex environments, which is essential for domains such as healthcare diagnostics, adaptive education technologies, intelligent security systems, autonomous robotics, and various forms of human-computer interaction. Multimodal approaches also enable AI models to compensate for the limitations inherent in individual modalities, thereby enhancing robustness and resilience to noise or incomplete data. The study employs theoretical analysis of scientific literature, comparative classification of multimodal architectures, systematization of fusion techniques, and formal generalization of model design principles. Additionally, attention is given to evaluating emerging paradigms powered by large-scale foundation models and transformer-based architectures. The primary methods and models for processing multimodal data are summarized, covering both classical and state-of-the-art approaches. Architectures of early (feature-level), late (decision-level), and hybrid (intermediate) fusion are described and compared in terms of flexibility, computational complexity, interpretability, and accuracy. Emerging solutions based on large multimodal transformer models, contrastive learning, and unified embedding spaces are also analyzed. Special attention is paid to cross-modal attention mechanisms that enable dynamic weighting of modalities depending on task context. The study determines that multimodal systems achieve significantly higher accuracy, stability, and semantic coherence in classification, detection, and interpretation tasks when modalities are properly synchronized and fused using adaptive strategies. These findings underscore the promise of further research toward scalable architectures capable of real-time multimodal reasoning, improved cross-modal transfer, and context-aware attention mechanisms.