Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{177873,
        author = {VINITH S and DR.K.JAGADEESH and DR.P.SUMATHI and THARUN T and THIWIN KUMAR M and THOUSIF MOHAMED A},
        title = {AIVORA},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3710-3714},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177873},
        abstract = {Aivora is a cutting-edge platform designed to revolutionize fraud detection and content verification in live streaming and video uploads. By addressing common challenges like inaccurate detection, delayed analysis, and operational inefficiencies, Aivora provides a secure, scalable, and user-friendly solution for modern content creators, security teams, and administrators. The system empowers users with advanced capabilities in real-time fraud detection, video analysis, content reporting, and anomaly identification. Real-time fraud detection is a cornerstone feature of S, enabling users to analyze streams and video uploads for deceptive content with unparalleled accuracy. The system reduces the risk of false positives, ensures seamless detection, and alerts users to suspicious activities, deceptive patterns, or content inconsistencies. The content tracking module offers live updates on video details, such as format, subscriber count, views, and publishing date, allowing administrators and security teams to proactively address fraudulent activities and optimize content integrity.},
        keywords = {AI-driven automation, IoT connectivity, Predictive analytics, Warehouse optimization.},
        month = {May},
        }