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Related Experiment Video

Updated: Sep 15, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance.

Bala Subramanian C1, Bharathi St1, Shanmugapriya S1

  • 1Computer Science and Engineering, Kalasalingam Academy of Research and Education, Srivilliputhur, India.

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|July 18, 2025
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Summary

This study introduces AdaPCA, a novel method for cloud resource management. AdaPCA enhances trust-based access control and resource allocation, achieving 99.8% accuracy with reduced latency.

Keywords:
AdaBoostAdaBoost with Principal Component AnalysisCloud computingDimensionality reductionMachine learningPrincipal Component AnalysisResource optimizationTrust assessmentTrust-based access control

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Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Cloud computing growth necessitates intelligent, rapid, and secure resource management.
  • High-dimensional trust data poses challenges for traditional systems.
  • Existing methods like Decision Trees, Random Forests, and Gradient Boosting have limitations.

Purpose of the Study:

  • To introduce AdaPCA, a novel hybrid method integrating AdaBoost and PCA.
  • To enhance trust-based access control and cloud resource allocation decisions.
  • To maintain a minimal computational burden while improving system performance.

Main Methods:

  • Developed AdaPCA, combining AdaBoost's adaptive capabilities with PCA's dimensionality reduction.
  • Conducted simulations comparing AdaPCA against Decision Trees, Random Forests, and Gradient Boosting.
  • Evaluated performance based on execution time, resource utilization, latency, and trust accuracy.

Main Results:

  • AdaPCA achieved 99.8% trust score prediction accuracy.
  • Demonstrated 95% resource utilization efficiency.
  • Reduced cloud resource allocation time to 140 ms.
  • Outperformed benchmark models across all evaluated parameters.

Conclusions:

  • AdaPCA offers superior performance in cloud resource management, including expedited decision-making and optimized utilization.
  • Represents a significant advancement for intelligent, secure, and adaptable cloud systems.
  • Provides a scalable architecture for efficient and secure cloud resource management.