Probabilistic Artificial Intelligence for Reliable Decision Making in Edge–Cloud Intelligent Systems

International Journal of Computer Science and Engineering

© 2025 by SSRG - IJCSE Journal

Volume 12 Issue 11

Year of Publication : 2025

Authors : Rocky Kumar, Adarsh Kumar Pandey, Gaurav Kumar, Sakshi Sharma

10.14445/23488387/IJCSE-V12I11P101

How to Cite?

Rocky Kumar, Adarsh Kumar Pandey, Gaurav Kumar, Sakshi Sharma, "Probabilistic Artificial Intelligence for Reliable Decision Making in Edge–Cloud Intelligent Systems," SSRG International Journal of Computer Science and Engineering , vol. 12,  no. 11, pp. 1-8, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I11P101

Abstract:

The increasing integration of Edge-Cloud environments with Artificial Intelligence (AI) has made it possible to process data faster and make decisions in real-time; however, deterministic AI models are not well-suited to manage the uncertainties, and this can be a source of unreliable behavior in dynamic network environments. This paper proposes a framework of Probabilistic Artificial Intelligence to enhance reliability, trust, and explainability in distributed Edge-Cloud intelligent systems. The proposed model applies Bayesian inference and uncertainty quantification techniques to provide confidence levels of AI predictions, which minimizes erroneous decisions in serious applications. The framework incorporates probabilistic reasoning at the edge and cloud layers for adaptive learning, low latency, and efficient resource allocation. Comparative results show that the probabilistic AI model is superior to traditional deterministic methods in terms of accuracy, reliability, and belief in the decision obtained using heterogeneous data collections. The novelty of this study is bringing together probabilistic modeling and Edge-Cloud synergy to increase reliability in intelligent computing systems. The results represent the importance of uncertainty-aware artificial intelligence models in the development of trustworthy and autonomous systems for the next generation of intelligent systems. Novelty in integrating the probabilistic reasoning in an Edge-Cloud architecture. 18-25% improvement in the reliability and 20% reduction in the latency compared to the existing deterministic frameworks.

Keywords:

Bayesian Inference, Edge–Cloud Computing, Intelligent Systems, Probabilistic Artificial Intelligence, Uncertainty Quantification.

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