Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

enhancing the learning process. Nevertheless, there is room for further improvement in the realm of reputation calculation. c) Smart Contracts and Cognitive Computing Cognitive computing represents an advanced field of AI research that aims to replicate human thinking within computer systems. By adopting human thinking patterns and limitations in its execution, cognitive computing achieves notably higher accuracy than other AI techniques. Integrating blockchain based smart contracts into cognitive computing can potentially enhance service values across various application scenarios. Blockchain-based smart contracts bring essential features to the forefront within the realm of cognitive computing, including data transparency, decentralized access control capabilities, and decentralized trust. These attributes significantly enhance the applicability of cognitive computing in the healthcare domain. Nonetheless, as emphasized in a study by Daniel et al. [42], implementing blockchain for healthcare is a complex undertaking. It necessitates meticulous consideration of compliance requirements to ensure the utmost data privacy and security. d) Smart Contracts with Tensor Networks Smart contracts integrated with tensor networks present a compelling fusion of blockchain technology and quantum computing. Tensor networks, rooted in mathematical constructs from quantum physics, hold the promise of quantum-enhanced computing within smart contracts. This potential allows for the execution of more intricate calculations and simulations than classical computers can handle, offering transformative applications in data analysis, optimization, and cryptography within blockchain-based systems. Further- more, tensor networks enable secure multiparty computations, facilitating collaborative efforts without compromising sensitive information. Promising areas for advancement encompass quantum machine learning, quantum randomness generation, and decentralized optimization. However, while this integration holds great promise, it faces challenges related to quantum hardware and scalability, demanding careful consideration for its full realization. Charlie et al. [43], in their research, contribute valuable insights and solutions to address some of these challenges, further advancing the field. iv. C omparison of ai B ased S mart- C ontract V ulnerability D etection tools This table serves as a valuable reference, guiding readers to relevant materials for further exploration. It provides insights into the AI methods employed by each tool, ranging from supervised learning to reinforcement learning and semi-supervised learning. Additionally, the table offers information about dataset sizes used in the research, allowing readers to gauge the impact of data scale on model performance and reliability. Furthermore, the table outlines the AI classification approaches utilized by these tools, elucidating the distinctions between different methods. By analyzing this comprehensive table, readers can gain a deeper understanding of various AI-based smart contract vulnerability detection tools. It serves as an indispensable resource for both further research and practical applications in this domain. Table 1: Comparison of AI Based Smart-Contract Vulnerability Detection Tools References Classification Dataset Size Adopted Technique Contribution [1] Supervised Learning More than 50,932 DL, Modular and Systematic Vulnerability Detection Framework DeeSCVHunter is a proposed deep learning-based framework for detecting vulnerabilities such as re entrancy and time dependence in a systematic and modular manner. It offers an innovative approach to identifying and addressing these types of vulnerabilities. [2] Supervised Learning 7000 LSTM, ANN, GRU GRU, ANN, and LSTM, were trained and utilized to predict the presence of vulnerabilities in smart contracts. This approach offers a new way to identify and address potential vulnerabilities in a more efficient and effective manner. Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 64 ( ) Year 2023 D Strengthening Smart Contracts: An AI-Driven Security Exploration © 2023 Global Journals In summary, the integration of federated learning and blockchain presents exciting research opportunities for enhancing privacy and efficiency across various domains, such as healthcare and industrial IoT. However, certain issues, particularly those related to resource constraints and reputation calculation, warrant additional attention and development.