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| Hlavní autoři: | , , , , |
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| Médium: | Recurso digital |
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Zenodo
2026
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| Témata: | |
| On-line přístup: | https://doi.org/10.5281/zenodo.19914836 |
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Obsah:
- Abstract - Traditional task management applications primarily rely on text-based task entries and manual categorization, which often leads to inconsistent organization and limited search efficiency. This paper presents WRAP, an AI-enhanced visual task management application that integrates document attachment support and intelligent on-device automatic tagging. Unlike conventional to-do list applications, WRAP enables users to create tasks with images and documents while automatically generating relevant tags using Natural Language Processing techniques. The system employs Google ML Kit Entity Extraction combined with heuristic keyword analysis to identify meaningful contextual tags directly on the device without requiring cloud-based services. The application follows a layered architecture consisting of presentation, application, data, and file storage layers. Generated tags enhance task discoverability, improve search precision, and reduce manual categorization effort. Experimental evaluation demonstrates improved contextual organization and retrieval efficiency compared to manual tagging approaches. WRAP highlights the practical integration of on-device Artificial Intelligence techniques in modern productivity applications.