Blockchain-Enabled Smart Contract Design and Development with Hybrid Deep Learning Model

Abstract

Blockchain-enabled smart contracts have revolutionized secure, automated, and decentralized transaction handling across various industries. However, they face limitations in complex decision-making due to their rigid execution and predefined rules. This paper explores the integration of a hybrid deep learning model with blockchain-enabled smart contracts to enhance their functionality and decision-making capabilities. By embedding deep learning layers within the smart contract framework, this approach enables real-time data analysis, predictive analytics, and adaptive decision-making, fostering a more robust and dynamic contract execution. Through this integration, the hybrid model can analyse transaction data, external conditions, and contextual parameters, improving contract outcomes in applications like finance, supply chain management, and healthcare. Experimental evaluations demonstrated that the proposed model achieved 98% accuracy, with a precision of 97.65%, a recall of 97.4%, and an F1-score of 97.5%, significantly enhancing smart contract flexibility and resilience while maintaining security and transparency.