Multimodal Data Fusion and Decision Algorithms in Deep Learning-Based Intelligent Systems: A Comprehensive Study
Year: 2025
Type: conference-paper
Abstract: The swift advancement of artificial intelligence has initiated an opportunity for automated intelligent systems that can manage the vast amount of multimodal (visual, text, auditory, etc.) data generated across mission-critical domains, such as healthcare and autonomous vehicles. However, the current state-of-the-art methods for multimodal determinism remain without solutions to two fundamental issues: (1) poor integration of heterogeneous modalities within real-world noise, and (2) fixed conditional decision-making, incapable of adapting to changes in reliability among modalities. In contrast, our study proposes a hierarchical fusion architecture characterized by (1) dynamic cross-modal attention mechanisms for adaptable alignment of multimodal features, (2) gated residual connections to enhance robustness against missing modalities, and (3) uncertainty-aware decision algorithms capable of accounting for epistemic and aleatoric confidence. Our experimental contributions document substantial gains in minimized error rates, cross-modal retrieval of 87.2%, with a 23.1% increase over unimodal baselines while still enabling real-time processing, 45 ms latency from the time of query. The system maintains an impressive degree of resilience to loss of modality, achieving 72% accuracy while processing full retrieval with two-thirds of the modality missing. In a task-specific medical diagnosis retrieval, misinformation was reduced by 31% with multimodal consideration. Collectively, this work establishes new metrics for reliable and adaptable multimodal intelligence to promote their adoption in safety-critical environments. The modularity of the framework serves as a mechanism scientists can easily adapt, while the evaluation protocols contribute substantially to the development of reliable multimodal technology, both characterized by the adoption of reproducible prototyping procedures conducive to comprehensive deployments in our society.
Authors: Haoyang Chen, Dong Tang
Institution: Chongqing Chemical Industry Vocational College
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414307496