Prescriptive Analytics on Cloud Based Systems Using Deep Learning Techniques

Abstract

Today's cloud-based systems, often called Cloud Computing, require robust Prescriptive Analytics to optimize the efficacy of Decision-Making processes. This study addresses the problems facing Prescriptive Analytics by implementing a fresh approach that utilizes Deep Auto Encoder Optimisation. The lack of approaches integrating Cloud Computing - Prescriptive Analytics systems with Deep Autoencoder optimization creates a field of study. Such methods make this an issue. Our strategy leverages Deep Autoencoders to uncover complex patterns and correlations from various datasets, improving the precision and effectiveness of Cloud Computing - Prescriptive Analytics systems. The outcome indicates that the DM procedures were greatly improved because of the substantial quantity of research and validation that has to be performed, which proves that the technique is effective. In addition to resolving the problems with Cloud Computing- Prescriptive Analytics, Decision-Making process optimization paves the way for potential improvements in Artificial Intelligence-powered decision support systems.