FUSION NET: Multi-Modal Deep Fusion Learning For Heterogeneous Big Data Analytics

Abstract

The rapid growth of heterogeneous big data generated from diverse sources such as text, images, sensor streams, and transactional logs poses significant challenges for effective representation learning and analytics. Conventional machine learning approaches often process individual modalities independently, leading to fragmented insights and limited contextual understanding. To address these limitations, this paper proposes FusionNet, a multi-modal deep fusion learning framework designed for unified and scalable heterogeneous big data analytics. FusionNet integrates modality-specific feature extractors with a deep fusion mechanism that captures both intra-modal and inter-modal correlations across heterogeneous data sources. The proposed architecture employs hierarchical fusion layers and attention-based weighting to adaptively learn the relative importance of each modality, enabling robust feature alignment and representation learning. Extensive experimental evaluation on multi-modal big data benchmarks demonstrates that FusionNet significantly outperforms existing single-modal and shallow fusion approaches in terms of prediction accuracy, robustness, and scalability. The results highlight the effectiveness of deep multi-modal fusion in extracting comprehensive knowledge from heterogeneous big data, making FusionNet a promising solution for intelligent data analytics in large-scale, real-world applications.