Abstract

This review paper addresses the critical issue of document irregularity management within university academic decision-making processes, highlighting the increasing reliance on data integrity in higher education. Document irregularities, like wrong Senate decision extracts, grade changes, and fake transcripts, can pose significant threats to the credibility and efficiency of academic institutions. Current methods to detect these irregularities are usually done by hand, take a lot of time, and are prone to human error. So, there's a need for better, automated solutions. Neural Networks (NNs) are a good technology for anomaly detection and classification tasks. This paper reviews how different Neural Network architectures, like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), can be used to identify and mitigate document irregularities. The paper looks at what's already been written, common methods, and the problems with using NN-based solutions in academic environments. Key findings indicate a growing trend towards leveraging AI for document verification and fraud detection, with NNs doing better at recognizing patterns than traditional methods. But issues like getting enough data, labeling problems, and understanding how the models work are still big challenges. The results suggest that Neural Networks could change how academic integrity is protected and how decisions are made easier. Future research could look at using combined AI models, Explainable AI (XAI) to build trust, and adding blockchain to NNs for secure academic records.

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