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May 6, 20260 citationsOpen Access

Experimental Evaluation of AI-Driven Sustainable Intelligent Cloud Optimization Framework

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PPPriyanka PurohitSCShambhuraj ChavanOPOmkar Patil

Key Points

  • This research aims to develop an AI-driven framework for optimizing personal cloud environments by integrating multiple AI functionalities.
  • Developed a multi-modal AI framework incorporating file prioritization and content understanding.
  • Evaluated system performance in managing cloud storage compared to traditional platforms.
  • Utilized vision-language processing and large language models for summarization tasks.
  • Demonstrated effective file prioritization and reduced redundant storage.
  • Achieved high efficiency in video summarization and retrieval accuracy.
  • Confirmed the feasibility of creating context-aware digital workspaces from conventional cloud storage.

Abstract

This paper presents an integrated multi-modal Artificial Intelligence (AI) framework that uses intelligent file prioritization, redundant storage reduction, and automated content understanding to improve personal cloud environments. The system makes use of AI-driven categorization, vision-language processing for image title suggestion, and large language models (LLMs) for hierarchical document and video summarization. The suggested framework integrates multiple AI services into a single architecture, in contrast to traditional storage platforms like Dropbox and Google Drive. Effective file prioritization, high summarization compression efficiency, and enhanced retrieval accuracy are demonstrated by experimental evaluation across several modules. The outcomes confirm that creating an intelligent, context-aware digital workspace from traditional cloud storage is feasible.

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Cite This Study

Purohit et al. (2026) studied this question.

synapsesocial.com/papers/69faa25e04f884e66b533092https://doi.org/10.5281/zenodo.20022366
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