An Efficient Blockchain-Based Active Learning Semi-Supervised Smart Contracts in Ethereum Blockchain Traceable Food Supply Chain - Jaison, Feon; K., Janaki (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The lack of food traceability has resulted in significant issues such as product recalls, public discontent, and concerns about contamination in previous instances. This study examines the traceability process using efficient stochastic mathematical frameworks and the concept of transparency using forthcoming Blockchain technology. Due to the rising popularity of blockchain technology, there has been a rapid growth in the number of smart contracts. Implementing efficient smart contract vulnerability detection is hindered by a significant obstacle: the need for more labeled data in the present domain. Classical active learning relies on a small quantity of tagged information for model training. This research proposes Confidence Interval Ensemble Learning Traceability (CI-ELT) to address these problems. CI-ELT introduced a new approach called the Active and Semi-Supervised Network framework. This structure addresses the limited labeled code information found in real-world smart contract threat detection jobs. In stochastic optimization, CI-ELT demonstrates a comprehensive system in a theoretical manner. CI-ELT used Blockchain technology to securely record the data of manufacturers, merchants, and suppliers, ensuring openness and traceability throughout the whole supply chain. The findings indicate that CI-ELT improves the effectiveness of the most advanced Intrusion Detection Systems (IDS). Compared to the existing Intrusion Detection Systems (IDSs), it attains a detection accuracy of 93.68%, a false alarm rate of 1.25%, and an F1-measure of 2.52%.