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.