AI-Enabled Data-Driven Decision Frameworks for Enterprise Platforms and Tactical Defense Wireless Networks
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I4P104Keywords:
Cloud-Native Data Architectures, Real-Time Decision Systems, Learning-Assisted Decision Making, Intelligent Analytics, Deep Learning For High-Dimensional Data, Multi-Modal Data Fusion, Bayesian Inference And Uncertainty Modeling, Explainable AI (XAI)Abstract
The use of Artificial Intelligence (AI) has become an enabler of transformation when it comes to making data-driven decisions on the enterprise platforms and the tactical networks of defense wireless networks. The growing complexity, magnitude and heterogeneity of the contemporary data ecosystems require smart systems that could encompass real-time analytics, uncertainty modeling and adaptive decision making. The paper includes a detailed AI-powered data-driven decision framework incorporating cloud-native data structures, real-time decision platforms, learning-assisted intelligence, and explainable AI technologies to enable the mission-critical enterprise and defense missions. The high-dimensional data processing, multi-mode data fusion, dynamic network conditions, and uncertainty in adversarial environments are some of the issues that are tackled in the proposed framework. With the use of deep learning models, Bayesian inference, and reinforcement learning, the framework is capable of predictive, prescriptive, and autonomous decisions and retains interpretability and trust. Special priority is given to tactical defense wireless networks, as latency requirements, disconnected connectivity, and security threats impose resilient and adaptive AI solutions. It is a research study that gives a descriptive architecture design, methodological establishment and analytical assessment of the AI-based decision systems. The outcomes show the essential increases in the accuracy of decisions, the decrease in the latency, and the system robustness in contrast to the traditional rule-based and non-adaptable analytics solutions. The results provide the basis of implementing smart, scaled, and interpretable decision schemes in the enterprise and defense settings.
References
[1] Davenport, T., & Harris, J. (2017). Competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Business Press.
[2] Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188.
[3] Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ... & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
[4] Kreutz, D., Ramos, F. M., Verissimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2014). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14-76.
[5] Haykin, S. (2005). Cognitive radio: brain-empowered wireless communications. IEEE journal on selected areas in communications, 23(2), 201-220.
[6] Mitchell, R., & Chen, I. R. (2014). A survey of intrusion detection techniques for cyber-physical systems. ACM Computing Surveys (CSUR), 46(4), 1-29.
[7] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
[8] Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2, pp. 1-800). Cambridge: MIT press.
[9] Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2018). Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence, 41(2), 423-443.
[10] Hall, D. L., & Llinas, J. (2002). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6-23.
[11] Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
[12] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
[13] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
[14] Symon, P. B., & Tarapore, A. (2015). Defense intelligence analysis in the age of big data. Joint Force Quarterly, 79(4), 04-11.
[15] Muñoz, E., Capón, E., Laínez, J. M., Moreno-Benito, M., Espuña, A., & Puigjaner, L. (2012). Operational, tactical and strategical integration for enterprise decision-making. In Computer Aided Chemical Engineering (Vol. 30, pp. 397-401). Elsevier.
[16] Johnson, S. E., Libicki, M. C., & Treverton, G. F. (Eds.). (2003). New challenges, new tools for defense decisionmaking (No. 1576). Rand Corporation.
[17] Rahman, M. M., & Ashfaq, S. (2021). Data-driven decision support in information systems: Strategic applications in enterprises. International Journal of Scientific Interdisciplinary Research, 2(2), 01-33.
[18] Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. (2018). A survey on security threats and defensive techniques of machine learning: A data driven view. IEEE access, 6, 12103-12117.
[19] Li, X., Yu, Q., Alzahrani, B., Barnawi, A., Alhindi, A., Alghazzawi, D., & Miao, Y. (2021). Data fusion for intelligent crowd monitoring and management systems: A survey. IEEE Access, 9, 47069-47083.
[20] Oaksford, M., & Chater, N. (2007). Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.
[21] Ley, T., Tammets, K., Pishtari, G., Chejara, P., Kasepalu, R., Khalil, M., ... & Wasson, B. (2023). Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analytics. Journal of Computer Assisted Learning, 39(5), 1397-1417.
[22] Liu, S., Wang, X., Liu, M., & Zhu, J. (2017). Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics, 1(1), 48-56.
