RICHARD, Daniel
Faculty Of Computing, Department Of Computer Science, Modibbo Adama University, Yola, Nigeria
From Rules To Transformers: A Review Of Spam Detection Techniques, Research Gaps And The Promise Of Bert-ensemble Frameworks
Spam has grown from a minor nuisance into one of the most persistent threats in digital communication, yet existing detection systems continue to struggle with balancing accuracy and computational efficiency. This paper reviews the evolution of spam detection approaches from early rule-based filters and traditional machine learning classifiers to deep learning models and transformer-based architectures with particular attention to the

RICHARD, Daniel
Faculty Of Computing, Department Of Computer Science, Modibbo Adama University, Yola
A Review Of Deep Learning Approaches To Real-time Financial Fraud Detection, Research Gaps And The Path Forward
Financial fraud costs the global economy over $40 billion annually, yet existing detection systems continue to struggle at the intersection of accuracy, latency, interpretability, and fairness. This paper reviews the evolution of fraud detection methods from early rule-based systems and classical machine learning through recurrent and convolutional deep learning architectures, to transformer models, graph neural networks, and federated learning with

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