Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{175443,
        author = {Dr. Kayapati Rajagopal},
        title = {Predictive Budget Allocation in Cloud-Based Bidding Using XAI models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2948-2954},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175443},
        abstract = {Budget allocation in cloud-based bidding environments is a critical decision-making process that directly affects the cost-effectiveness and resource utilization of cloud services. However, conventional machine learning models often act as black-box predictors, limiting their adoption due to a lack of transparency. This paper proposes the integration of Explainable Artificial Intelligence (XAI) techniques with predictive budget allocation models to improve interpretability, trustworthiness, and decision reliability. We developed a predictive framework incorporating XAI methods such as SHAP and LIME to explain model behavior during budget allocation. Experiments demonstrate that the proposed system not only improves the prediction accuracy but also provides meaningful insights for cloud users to adjust their bidding strategies effectively.},
        keywords = {XAI, Predictive Budget Allocation, Cloud Bidding, SHAP, LIME, Explainable AI},
        month = {April},
        }