Predictive Budget Allocation in Cloud-Based Bidding Using XAI models

Dr. Kayapati Rajagopal

Predictive Budget Allocation in Cloud-Based Bidding Using XAI models

Authors: Dr. Kayapati Rajagopal

Unique Paper ID: 175443
Volume: 11
Issue: 11
PageNo: 2948-2954

Keywords: XAI Predictive Budget Allocation Cloud Bidding SHAP LIME Explainable AI

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 2948-2954

Predictive Budget Allocation in Cloud-Based Bidding Using XAI models

Available:https://ijirt.org/Article?manuscript=175443

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UGC Approved
Journal no 47859

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