Prescriptive Analytics on Cloud Based Systems Using Deep Learning Techniques

Authors

  • Srinivas Naveen Dolu-Surabhi General Motors
  • Manikandan Rajagopal CHRIST Bangalore
  • M. Hema Kumar Sona College of Technology
  • S Srividhya KPR College of Arts Science and Research

DOI:

https://doi.org/10.13053/cys-28-3-5179

Keywords:

Cloud-Based Systems or Cloud Computing, Prescriptive Analytics, Decision-Making, Deep Autoencoder Optimization, Artificial Intelligence

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.

Downloads

Published

2024-09-17

Issue

Vol. 28 No. 3 (2024): 28(3) 2024

Section

Articles of the Thematic Section

License 

Hereby I transfer exclusively to the Journal "Computación y 
Sistemas", published by the Computing Research Center (CIC-IPN),
the Copyright of the aforementioned paper. I also accept that these 
rights will not be transferred to any other publication, in any other 
format, language or other existing means of developing.
I certify that the paper has not been previously disclosed or simultaneo
usly submitted to any other publication, and that it does not contain 
material whose publication would violate the Copyright or other 
proprietary rights of any person, company or institution. I certify that 
I have the permission from the institution or company where I work or 
study to publish this work.
The representative author accepts the responsibility for the publication
of this paper on behalf of each and every one of the authors. 
This transfer is subject to the following conditions:
  • The authors retain all ownership rights (such as patent rights) of this work, except for the publishing rights transferred to the CIC, through this document.
  • Authors retain the right to publish the work in whole or in part in any book they are the authors or publishers. They can also make use of this work in conferences, courses, personal web pages, and so on.
  • Authors may include working as part of his thesis, for non-profit distribution only.