Implementing an Information Verification System to Prevent Academic Fraud by Employees Using a Hybrid of ANN and RF Algorithms

Abstract

Academic fraud, particularly the falsification of qualifications, poses a growing threat to organizational integrity and professional credibility. This study proposes an Information Verification System (IVS) to combat employee credential fraud using a hybrid of Artificial Neural Network (ANN) and Random Forest (RF) algorithms. The method follows a two-step process: first, ANN extracts key certificate features, such as digital signatures, logos, and serial numbers, then RF classifies the certificate as authentic or fraudulent based on these features. Tested on 4,830 certificates from Mopani TVET College, alongside 1500 replicas, the system achieved near-perfect results: 98.90% accuracy, 96.75% precision, 99.33% recall, and a 98.03% F1-score, outperforming Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Logistic Regression models. By integrating with institutional databases, the IVS offers a scalable, secure solution to automate verification processes so that only legitimate qualifications are accepted. These results suggest that the proposed IVS offers a scalable and secure solution for institutions and employers, significantly improving the efficiency and reliability of academic credential verification.