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@article{174401,
        author = {DHANUSH K A and AKILESH A and ARAVIND R and VINOTH KUMAR S},
        title = {AUTOMATED AUTHENTICATION SERVICES USING MACHINE LEARNING IN TRANSPORTATION SERVICES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4425-4428},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174401},
        abstract = {The “Automated Authentication Services Using Machine Learning in Transportation Services" system integrates an online booking platform with automated authentication to enhance bus ticketing and boarding. Users book tickets by selecting routes and providing details, including a photo, which is securely stored for verification. After booking, a QR code is generated containing the user’s information and photo for authentication during boarding. The system employs Convolutional Neural Networks (CNNs) for face recognition. During boarding, the user scans their QR code, extracting stored details for verification. A live image is captured and compared with the embedded photo using CNN-based facial analysis, ensuring precise identity verification. CNNs accurately detect facial features, preventing fraudulent access while eliminating manual checks, improving security and efficiency. Additionally, QR codes streamline authentication by securely encoding user data, preventing ticket duplication and unauthorized use. This automated approach reduces boarding time and enhances operational efficiency. By integrating an online booking system, QR code verification, and CNN-powered face recognition, the system ensures a secure and seamless experience for passengers and service providers.},
        keywords = {Machine Learning, Transportation Security, Convolutional Neural Networks, QR code Verification.},
        month = {March},
        }