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Research on the method to enhance the transparency of financial transactions by integrating blockchain and smart contracts

  
04 oct 2024

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Kim, H., & Laskowski, M. (2017, July). A perspective on blockchain smart contracts: Reducing uncertainty and complexity in value exchange. In 2017 26th International conference on computer communication and networks (ICCCN) (pp. 1-6). IEEE. Search in Google Scholar

Staples, M., Chen, S., Falamaki, S., Ponomarev, A., Rimba, P., Tran, A. B., ... & Zhu, J. (2017). Risks and opportunities for systems using blockchain and smart contracts. Data61. CSIRO), Sydney. Search in Google Scholar

Sedlmeir, J., Lautenschlager, J., Fridgen, G., & Urbach, N. (2022). The transparency challenge of blockchain in organizations. Electronic Markets, 32(3), 1779-1794. Search in Google Scholar

Bocek, T., & Stiller, B. (2017). Smart contracts–blockchains in the wings. In Digital marketplaces unleashed (pp. 169-184). Berlin, Heidelberg: Springer Berlin Heidelberg. Search in Google Scholar

Omar, I. A., Hasan, H. R., Jayaraman, R., Salah, K., & Omar, M. (2021). Implementing decentralized auctions using blockchain smart contracts. Technological Forecasting and Social Change, 168, 120786. Search in Google Scholar

Fauziah, Z., Latifah, H., Omar, X., Khoirunisa, A., & Millah, S. (2020). Application of blockchain technology in smart contracts: A systematic literature review. Aptisi Transactions on Technopreneurship (ATT), 2(2), 160-166. Search in Google Scholar

Ante, L. (2021). Smart contracts on the blockchain–A bibliometric analysis and review. Telematics and Informatics, 57, 101519. Search in Google Scholar

Brammertz, W., & Mendelowitz, A. I. (2018). From digital currencies to digital finance: the case for a smart financial contract standard. The Journal of Risk Finance, 19(1), 76-92. Search in Google Scholar

Shojaei, A., Flood, I., Moud, H. I., Hatami, M., & Zhang, X. (2020). An implementation of smart contracts by integrating BIM and blockchain. In Proceedings of the Future Technologies Conference (FTC) 2019: Volume 2 (pp. 519-527). Springer International Publishing. Search in Google Scholar

Wang, H., Guo, C., & Cheng, S. (2019). LoC—A new financial loan management system based on smart contracts. Future Generation Computer Systems, 100, 648-655. Search in Google Scholar

Shermin, V. (2017). Disrupting governance with blockchains and smart contracts. Strategic change, 26(5), 499-509. Search in Google Scholar

Dal Mas, F., Dicuonzo, G., Massaro, M., & Dell’Atti, V. (2020). Smart contracts to enable sustainable business models. A case study. Management Decision, 58(8), 1601-1619. Search in Google Scholar

Nzuva, S. (2019). Smart contracts implementation, applications, benefits, and limitations. Journal of Information Engineering and Applications, 9(5), 63-75. Search in Google Scholar

Fiorentino, S., & Bartolucci, S. (2021). Blockchain-based smart contracts as new governance tools for the sharing economy. Cities, 117, 103325. Search in Google Scholar

Raj, P. V. R. P., Jauhar, S. K., Ramkumar, M., & Pratap, S. (2022). Procurement, traceability and advance cash credit payment transactions in supply chain using blockchain smart contracts. Computers & Industrial Engineering, 167, 108038. Search in Google Scholar

Schär, F. (2021). Decentralized finance: On blockchain-and smart contract-based financial markets. FRB of St. Louis Review. Search in Google Scholar

Baidakova N. V. & Subbotin Yu. N. (2024). Approximation to the Derivatives of a Function Definedon a Simplex under Lagrangian Interpolation. Mathematical Notes(1-2),3-11. Search in Google Scholar

Sheriff Fareed.(2024).ELMOPP: an application of graph theory and machine learning to traffic light coordination.Applied Computing and Informatics(3-4),217-230. Search in Google Scholar

Tan Runnan,Tan Qingfeng,Zhang Qin,Zhang Peng,Xie Yushun & Li Zhao. (2023). Ethereum fraud behavior detection based on graph neural networks. Computing(10),2143-2170. Search in Google Scholar

Zi Wenjie,Xiong Wei,Chen Hao & Chen Luo. (2021). TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network. Information Sciences274-285. Search in Google Scholar