Cloud Storage With AI-based Intelligent File Management
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| Natura: | Recurso digital |
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2026
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| _version_ | 1866901379261399040 |
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| author | Achyuth Totteti Mokshagna Ponnada Manirathnam Dornala D. Swaroopa |
| author_facet | Achyuth Totteti Mokshagna Ponnada Manirathnam Dornala D. Swaroopa |
| contents | The rapid growth of digital data and cloud-based applications has increased the need for intelligent file management and efficient storage optimization. Traditional cloud storage systems rely on manual organization and keyword-based search, which often results in inefficient file retrieval and redundant storage usage. This paper presents an AI-driven personal cloud storage framework for intelligent file management and semantic search. The proposed system incorporates file preprocessing, content extraction, and embedding-based feature generation to capture contextual relationships between stored files. A semantic similarity engine is used to retrieve contextually related files, while a duplicate detection module identifies redundant data using hash comparison and cosine similarity. Additionally, the framework includes a storage optimization and analytics module to monitor usage patterns and improve storage efficiency. The system also provides visual insights and recommendations to enhance user understanding and data organization. Experimental evaluation demonstrates improved retrieval efficiency and reduced redundant storage compared to conventional cloud storage methods. The proposed approach offers a scalable and reliable solution for intelligent personal cloud storage, semantic search, and data management applications. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19705923 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Cloud Storage With AI-based Intelligent File Management Achyuth Totteti Mokshagna Ponnada Manirathnam Dornala D. Swaroopa AI-driven Cloud Storage Semantic Search Duplicate Detection File Optimization Embedding Techniques Storage Analytics Artificial Intelligence Data Management The rapid growth of digital data and cloud-based applications has increased the need for intelligent file management and efficient storage optimization. Traditional cloud storage systems rely on manual organization and keyword-based search, which often results in inefficient file retrieval and redundant storage usage. This paper presents an AI-driven personal cloud storage framework for intelligent file management and semantic search. The proposed system incorporates file preprocessing, content extraction, and embedding-based feature generation to capture contextual relationships between stored files. A semantic similarity engine is used to retrieve contextually related files, while a duplicate detection module identifies redundant data using hash comparison and cosine similarity. Additionally, the framework includes a storage optimization and analytics module to monitor usage patterns and improve storage efficiency. The system also provides visual insights and recommendations to enhance user understanding and data organization. Experimental evaluation demonstrates improved retrieval efficiency and reduced redundant storage compared to conventional cloud storage methods. The proposed approach offers a scalable and reliable solution for intelligent personal cloud storage, semantic search, and data management applications. |
| title | Cloud Storage With AI-based Intelligent File Management |
| topic | AI-driven Cloud Storage Semantic Search Duplicate Detection File Optimization Embedding Techniques Storage Analytics Artificial Intelligence Data Management |
| url | https://doi.org/10.5281/zenodo.19705923 |