Foundation Model-Based Predictive Analytics for Multi-Domain Decision Intelligence

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2024PII6L1A

    Published 11-04-2024

  • Foundation Models, Predictive Analytics, Multi-Domain Decision Intelligence, Artificial Intelligence, Large Language Models, Transformer Networks, Transfer Learning, Explainable Artificial Intelligence (XAI), Multimodal Learning, Knowledge Graphs, Enterprise Intelligence, Decision Support Systems

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    N. Seshagiri, “Foundation Model-Based Predictive Analytics for Multi-Domain Decision Intelligence”, IJMLPA, vol. 7, no. 2, pp. 01–16, Nov. 2024, doi: 10.67228/3142788X/IJMLPA-2024PII6L1A.

  • Abstract

    Predictive analytics is rapidly evolving through Artificial Intelligence (AI), particularly with the emergence of foundation models that enable scalable, transferable, and context-aware intelligence across multiple domains. Unlike traditional machine learning models, foundation models leverage large-scale multimodal pretraining and efficient task-specific adaptation, enabling superior reasoning, zero-shot learning, and cross-domain knowledge transfer. This paper proposes the Foundation Model-Based Predictive Analytics Framework for Multi-Domain Decision Intelligence (FMPA-MDI), an integrated architecture that combines heterogeneous data acquisition, multimodal preprocessing, semantic representation learning, transformer-based predictive reasoning, retrieval-augmented learning, knowledge graph integration, explainable AI (XAI), and intelligent decision optimization. The framework supports structured and unstructured data while incorporating transfer learning, attention mechanisms, semantic embeddings, and continuous feedback for adaptive decision-making. Mathematical formulations model feature representation, semantic similarity, predictive confidence, and optimization. The proposed framework enhances prediction accuracy, scalability, interpretability, and computational efficiency, providing a robust foundation for next-generation intelligent decision support across healthcare, finance, manufacturing, smart cities, and other enterprise domains.

  • References

    [1] T. B. Brown et al., “Language Models are Few-Shot Learners,” Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2021.

    [2] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” IEEE Intelligent Systems, vol. 36, no. 4, pp. 104–112, 2021.

    [3] A. Bommasani et al., “On the Opportunities and Risks of Foundation Models,” arXiv:2108.07258, 2021.

    [4] Y. Liu et al., “Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,” ACM Computing Surveys, vol. 55, no. 9, pp. 1–35, 2023.

    [5] W. Fan, H. Liu, J. Wang, and P. S. Yu, “Graph Neural Networks for Social Recommendation: A Survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 5, pp. 4219–4238, 2023.

    [6] P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 2, pp. 1182–1196, 2024.

    [7] S. Raschka, “Large Language Models and Foundation Models: From Theory to Enterprise Applications,” IEEE Computer, vol. 57, no. 3, pp. 54–63, 2024.

    [8] X. Wang, Y. Li, and J. Zhang, “Multimodal Foundation Models: Recent Advances and Future Directions,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 2, pp. 356–372, 2024.

    [9] D. Gunning and D. Aha, “DARPA's Explainable Artificial Intelligence Program: Recent Progress and Future Directions,” AI Magazine, vol. 42, no. 3, pp. 44–58, 2021.

    [10] S. M. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 120–134, 2024.

    [11] M. T. Ribeiro, S. Singh, and C. Guestrin, “Local Interpretable Model-Agnostic Explanations for Machine Learning Systems,” IEEE Intelligent Systems, vol. 37, no. 6, pp. 88–98, 2022.

    [12] A. Adadi and M. Berrada, “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges,” IEEE Access, vol. 10, pp. 84135–84158, 2022.

    [13] H. Chen, Z. Wu, and Y. Chen, “Knowledge Graph Enhanced Artificial Intelligence: Recent Advances and Applications,” IEEE Access, vol. 11, pp. 51248–51269, 2023.

  • Downloads