OPTIMIZING CLOUD, FINANCE, AND E-COMMERCE WITH AI: ADVANCING DECISION-MAKING USING SNNS, CMA-ES, AND HESN FOR SCALABLE AND ADAPTIVE SYSTEMS

Abstract

Background: The integration of Artificial Intelligence (AI) in cloud computing, finance, and e-commerce offers the greatest promise for changing decision-making processes through its potential to enhance the scale, adaptation, and efficiency of transactions. It further becomes worthwhile considering AI models as efficiency-oriented tools when these sectors, which deal with complex, dynamic environments, require high-optimizing intelligent systems in optimizing operations and resource management.
Methods: This research uses Self-Organizing Neural Networks (SNNS) for pattern recognition, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for optimization tasks, and Hierarchical Event-Driven Stochastic Networks (HESN) for event-driven decision-making in cloud, finance, and e-commerce. These models are integrated to enhance the adaptability and scalability of the decision-making process.
Objectives: Optimize the decision-making processes of cloud computing, finance, and e-commerce systems using AI-driven approaches. The research work will enhance the scalability, adaptability, and efficiency in the management of huge data and further business decision making across these domains.
Empirical Results: Integration of SNNS, CMA-ES, and HESN improved operational efficiency with 92% of the decision accuracy in finance, while 87% in the case of e-commerce. High scalability of the system reduces the processing time up to 30%.
Conclusion: An amalgamation of SNNS, CMA-ES, and HESN creates cloud, finance, and e-commerce with the inclusion of the superior decision-making mechanism. AI scalabilities by approaching this improves operation efficiency in turn enhances general performance of systems leading to values on business.