Automated Certificate Generation Using Advanced Transformer-based Deep Learning, Blockchain, and Cloud Technologies for Secure Document Processing
- DOI
- 10.2991/978-94-6463-858-5_14How to use a DOI?
- Keywords
- Blockchain; Deep Learning; Certificate Verification; Fraud Detection; Smart Contracts
- Abstract
Certificate generation is essential in education, corporate training, and events, yet traditional manual methods are slow and error-prone. This paper presents an automated system utilizing blockchain for secure verification and transformer-based deep learning models for fraud detection. Techniques such as Python scripting, cloud-based automation, and AI-driven validation streamline the process, enhancing efficiency and accuracy. Experimental results show a significant reduction in processing time and over 98% accuracy in fraud detection, ensuring secure and tamper-proof certificates. Blockchain integration strengthens trust by enabling real-time verification, eliminating forgery risks. The proposed approach minimizes human intervention while maintaining scalability and security, making it an effective solution for automated certificate management.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - S. Susidra AU - M. Sivaraj AU - M. Mohanbabu AU - M. Naveen AU - S. Nishanth PY - 2025 DA - 2025/11/04 TI - Automated Certificate Generation Using Advanced Transformer-based Deep Learning, Blockchain, and Cloud Technologies for Secure Document Processing BT - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025) PB - Atlantis Press SP - 146 EP - 156 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-858-5_14 DO - 10.2991/978-94-6463-858-5_14 ID - Susidra2025 ER -