Experimental Evaluation of Student Certificate Validation System using Improved Deep Learning Strategy with Prediction Principles

The validation of student certificates is essential for ensuring the integrity and credibility of educational qualifications. In this study, we present an innovative approach, the “Improved Deep Learning Strategy with Prediction (IDLSP),” designed to automate the certificate validation process. By integrating advanced deep learning techniques such as Natural Language Processing (NLP) and Multi-Layer Perceptron (MLP), the IDLSP system effectively processes and validates certificate data. Through a comprehensive pipeline encompassing data acquisition, preprocessing, feature extraction, and model training, the IDLSP model learns intricate patterns indicative of genuine certificates. Performance evaluation reveals that the IDLSP model achieves an impressive accuracy of $92 \%$, outperforming baseline and alternative deep learning models. The system’s scalability, efficiency, and user-friendliness are further demonstrated through extensive experimentation and validation. The IDLSP system represents a robust, automated solution for student certificate validation, offering significant advancements in educational credential verification processes.