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E. M. Ali Qhal

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Review Open access Aug 2026

AI-Driven Unsupervised Framework for Detecting and Analyzing Anomalous Cryptocurrency Transactions Across Blockchain Networks Using Multi-Feature Analysis

The rapid growth of cryptocurrency networks has increased the risk of fraudulent transactions, necessitating effective and scalable detection methods. This study presents an AI-driven framework for detecting anomalous transactions in cryptocurrency datasets using unsupervised learning techniques, specifically the Isola...

E. M. Ali Qhal · 0 citations

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