Multimodal AI Frameworks for Decision Intelligence Systems

  • Authors

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

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2019PI9B5S

    Published 06-04-2019

  • Multimodal AI, Decision Intelligence, Machine Learning, Data Fusion, Artificial Intelligence, Explainable AI, Enterprise Analytics, Deep Learning

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    S. N, “Multimodal AI Frameworks for Decision Intelligence Systems”, IJAIDT, vol. 2, no. 1, pp. 01–14, Jun. 2019, doi: 10.67228/30713315/IJAIDT-2019PI9B5S.

  • Abstract

    Decision Intelligence (DI) combines Artificial Intelligence (AI), Machine Learning (ML), analytics, and domain expertise to improve organizational decision-making. However, the growing volume of multimodal data, including text, images, sensor data, and numerical information, presents significant challenges for conventional decision support systems. This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency. The proposed architecture consists of four layers: data ingestion, multimodal processing, fusion intelligence, and decision orchestration. It employs Natural Language Processing (NLP), Computer Vision (CV), time-series analytics, and transformer-based fusion techniques to generate predictive insights, automated recommendations, and explainable decisions. The framework is applicable across healthcare, finance, manufacturing, retail, and intelligent governance. Performance evaluation demonstrates that multimodal AI significantly outperforms traditional unimodal systems by improving prediction accuracy, reducing decision latency, and enhancing contextual awareness. The proposed framework supports faster, more reliable, and explainable decision-making, providing a scalable solution for next-generation enterprise decision intelligence and data-driven strategic planning.

  • References

    [1] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

    [2] Ashish Vaswani et al., “Attention is All You Need,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017.

    [3] Jacob Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proc. NAACL-HLT, 2019.

    [4] Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proc. NeurIPS, 2012.

    [5] Sepp Hochreiter and Jürgen Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

    [6] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin, “Why Should I Trust You? Explaining the Predictions of Any Classifier,” in Proc. ACM SIGKDD, 2016.

    [7] Scott Lundberg and Su-In Lee, “A Unified Approach to Interpreting Model Predictions,” in Proc. NeurIPS, 2017.

    [8] Diederik P. Kingma and Jimmy Ba, “Adam: A Method for Stochastic Optimization,” in Proc. ICLR, 2015.

    [9] Rizk, Y., Awad, M., & Tunstel, E. W. (2018). Decision making in multiagent systems: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10(3), 514–529.

    [10] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

    [11] Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.

    [12] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

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