A Unified Framework for Multi-Modal Data Analytics Using Deep Learning, Bayesian Inference, and Graph-Based Representations
Keywords:
Multi-modal analytics, Deep learning, Bayesian inference, Graph-based representation, Data fusionAbstract
The integration of heterogeneous data modalities remains a central challenge in modern data analytics. This paper proposes a unified framework that combines deep learning architectures, Bayesian inference, and graph-based representations to improve multi-modal data integration, interpretability, and predictive performance. Deep learning provides hierarchical feature extraction, Bayesian inference quantifies uncertainty and robustness, while graph-based methods capture relational structures across modalities. We present the conceptual model, potential application domains, and comparative evaluations, demonstrating the framework’s capacity to enhance both interpretability and accuracy in complex data environments.
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Copyright (c) 2023 Praveen Venkata Nandan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
