
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
AIMAR-2025
Conferences Published ↓
ICCE (2025)
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 7 Issue 4
July-August 2025
Indexing Partners



















Explainable AI (XAI) for Cloud Resource Forecasting in E-Commerce Environments
Author(s) | Mr. Sudheer Singh, Mr. Vasudev Karthik Ravindran, Mr. Sambhav Patil |
---|---|
Country | India |
Abstract | The rapid growth of e-commerce has intensified the demand for accurate and trustworthy cloud resource forecasting to ensure seamless service delivery during volatile and unpredictable workload fluctuations. Traditional black-box machine learning models, while powerful in prediction, often fail to provide the transparency necessary for stakeholders to trust and effectively manage automated resource allocation decisions. This paper proposes a novel hybrid framework that integrates Explainable Artificial Intelligence (XAI) techniques into cloud resource forecasting for e-commerce environments, combining the predictive strength of advanced sequential models like LSTM with interpretable surrogate models and post-hoc explanation methods such as SHAP and LIME. Using real-world workload data from a leading e-commerce platform, the study demonstrates that the hybrid model achieves superior predictive performance—reflected by lower RMSE, MAE, and MAPE values—while delivering clear, actionable explanations that align with stakeholders’ operational knowledge. Experimental results under realistic scenarios, including high-demand events like flash sales, confirm that embedding explainability into forecasting pipelines enhances operational trust, supports proactive resource provisioning, and aligns with emerging requirements for AI transparency and accountability. The findings advocate for a shift from opaque forecasting systems to transparent, interpretable frameworks that bridge the gap between technical accuracy and responsible AI governance, laying a robust foundation for future advancements in intelligent, ethical cloud resource management in dynamic digital commerce landscapes. |
Keywords | Explainable AI, Cloud Resource Forecasting, E-Commerce Workloads, Interpretability, Hybrid Machine Learning |
Field | Computer > Logic |
Published In | Volume 7, Issue 4, July-August 2025 |
Published On | 2025-07-25 |
DOI | https://doi.org/10.36948/ijfmr.2025.v07i04.51796 |
Short DOI | https://doi.org/g9vpj9 |
Share this

E-ISSN 2582-2160

CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.
