Citation :

Faisal Masood, Ashraf Ali Khan, Abbas Ali Akbar, 2026. "Adaptive Knowledge Integration for Multi-Source Predictive Intelligence Systems" ESP International Journal of Artificial Intelligence & Data Science [IJAIDS]  Volume 2, Issue 2: 1-14.

Abstract :

Adaptive Knowledge Integration (AKI) for Multi-Source Predictive Intelligence Systems is one of the important paradigms within modern AI, where sources of data are obtained from disparate heterogeneous sources and used to assist decision-making. For healthcare, finance and smart infrastructure, the majority of data are distributed across many platforms and formats (structured databases, unstructured text, sensor streams and realtime inputs) in non-trivial ways. Since this is multi-source and dynamic data, traditional predictive models which are designed for specific cases where the dataset being used in the model is static or reused for multiple runs fail to capture diverse informative features leading to lower accuracy and less adaptability.

References :

[1] Bagnio, Y., 2023. Deep learning and representation learning. MIT Press.

[2] Bidet, A. and Zavala, R., 2007. Learning from time-changing data with adaptive windowing. SIAM International Conference on Data Mining.

[3] Cabaña, O. et al., 2025. Intelligent information system for knowledge integration into AI models. Research Gate.

[4] Gawlikowski, J., et al. (2023). A Survey of Uncertainty in Deep Neural Networks. Artificial Intelligence Review.

[5] Dumpster, A.P., 1967. Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38(2), pp.325–339.

[6] Fee, L., Li, T. and Ding, W., 2026. Adaptive multi-source information fusion. Information Fusion.

[7] Gama, J. et al., 2014. A survey on concept drifts adaptation. ACM Computing Surveys, 46(4).

[8] Good fellow, I., Bagnio, Y. and Carville, A., 2016. Deep Learning. MIT Press.

[9] Hall, D.L. and Llamas, J., 2022. Handbook of multisensory data fusion. CRC Press.

[10] Jib, S. et al., 2022. Knowledge graph embedding: A survey. IEEE Transactions on Knowledge and Data Engineering.

[11] Julian, J. et al., 2025. Adaptive knowledge bases for continual learning. Nature AI.

[12] Khaleghi, B. et al., 2021. Multisensory data fusion: A review. Information Fusion, 14(1), pp.28–44.

[13] Kirkpatrick, J. et al., 2017. Overcoming catastrophic forgetting. PNAS, 114(13), pp.3521–3526.

[14] Koru, V. and Desponded, B., 2019. Predictive analytics and data mining. Morgan Kaufmann.

[15] Li, Z. and Howie, D., 2017. Learning without forgetting. IEEE TPAMI, 40(12), pp.2935–2947.

[16] Lopez-Paz, D. and Renato, M., 2017. Gradient episodic memory. Neutrals.

[17] Nickel, M. et al., 2023. Relational machine learning. Proceedings of the IEEE.

[18] Pan, S.J. and Yang, Q., 2010. A survey on transfer learning. IEEE TKDE, 22(10), pp.1345–1359.

[19] Paris, G.I. et al., 2019. Continual lifelong learning. Neural Networks, 113, pp.54–71.

[20] Perry, J. et al., 2025. Dynamic knowledge integration in AI systems. AI Journal.

[21] Provost, F. and Fawcett, T., 2013. Data science for business. O’Reilly.

[22] Ruse, A.A. et al., 2016. Progressive neural networks. Arrive preprint.

[23] Russell, S. and Nerving, P., 2021. Artificial Intelligence: A Modern Approach. Pearson.

[24] Shafer, G., 1976. A mathematical theory of evidence. Princeton University Press.

[25] Samuel, G. and Copies, O., 2011. Predictive analytics in IS research. MIS Quarterly, 35(3).

[26] Strielkowski, W., 2025. AI-driven adaptive learning systems. Sustainable Development Journal.

[27] Wooldridge, M., 2009. An introduction to multi-agent systems. Wiley.

[28] Ismailia, P., et al. (2018). Averaging Weights Leads to Wider Optima and Better Generalization.

[29] Wu, Y. and Xian, L., 2024. Multi-source data integration for predictive modelling. IEEE Access.

[30] Sade, L.A., 1996. Fuzzy logic and information fusion. IEEE Transactions.

[31] Amoroso, E., 2025. AI-driven predictive supply chains. IJSRA.

[32] Gutiérrez, E. et al., 2025. Multi-source predictive intelligence systems. Applied Sciences.

[33] Jennings, N.R., 2000. On agent-based systems. Artificial Intelligence Journal.

[34] Chen, T. and Gastrin, C., 2016. Boost: Scalable tree boosting. KDD.

[35] Bremen, L., 2001. Random forests. Machine Learning, 45(1), pp.5–32.

[36] Bishop, C.M., 2006. Pattern Recognition and Machine Learning. Springer.

[37] Murphy, K.P., 2012. Machine Learning: A Probabilistic Perspective. MIT Press.

[38] Sutton, R.S. and Barton, A.G., 2018. Reinforcement Learning. MIT Press.

[39] Zhou, Z.H., 2012. Ensemble methods. CRC Press.

[40] Vapid, V., 1998. Statistical Learning Theory. Wiley.

Keywords :

Adaptive Knowledge Integration, Multi-Source Data, Predictive Intelligence Systems, Data Fusion, Machine Learning, Knowledge Representation, Heterogeneous Data, Dynamic Learning, Uncertainty Handling, and Cross-Domain items of interest learning; Introduction The world around us is rapidly evolving.