Optimizing Smart Contracts with Machine Learning Techniques in Blockchain
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Abstract
Smart contracts play a pivotal role in the functionality and automation of transactions within blockchain networks. However, their efficiency and optimization remain ongoing challenges, particularly in handling complex and dynamic conditions. This research explores the integration of machine learning (ML) techniques to enhance the performance and optimization of smart contracts in blockchain systems. By leveraging ML algorithms, such as reinforcement learning and neural networks, this study aims to improve the adaptability, scalability, and predictive capabilities of smart contracts. The research investigates the potential of ML in automating contract execution, optimizing gas usage, mitigating vulnerabilities, and dynamically adjusting contract parameters based on real-time data inputs. Through empirical evaluations and case studies, this paper highlights the feasibility and effectiveness of using ML techniques to optimize smart contracts in diverse blockchain applications.
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