On the Application of Artificial Intelligence in Local Legislation

[1] Suksi, M. (2020). The Constitutional Identity of Contemporary China–The Unitary System and its Internal Logic, written by Han, Zhai. International Journal on Minority and Group Rights, 28(1), 201-206. Search in Google Scholar

[2] Weiming, W. (2016). Does the Enlargement of Local Legislative Power challenge the Unitary System in China? Asia Policy, 20, 17-22. Search in Google Scholar

[3] Li, L. (2015). Chinese Characteristics of the “Socialist Rule of Law”. Asia Policy, (20), 17-22. Search in Google Scholar

[4] Zhu, H., & Peters, M. A. (2019). Social governance, education and socialist rule of law in China. Educational Philosophy and Theory, 51(7), 670-673. Search in Google Scholar

[5] Li, Y., & Ma, L. (2019). What drives the governance of ridesharing? A fuzzy-set QCA of local regulations in China. Policy Sciences, 52(4), 601-624. Search in Google Scholar

[6] Bao, Q., Shao, M., & Yang, D. (2021). Environmental regulation, local legislation and pollution control in China. Environment and Development Economics, 26(4), 321-339. Search in Google Scholar

[7] Ferry, L., & Ahrens, T. (2022). The future of the regulatory space in local government audit: A comparative study of the four countries of the United Kingdom. Financial Accountability & Management, 38(3), 376-393. Search in Google Scholar

[8] Pronko, L., Kolesnik, T., & Samborska, O. (2018). Activities of united territorial communities as a body of local government in the conditions of power decentralization in Ukraine. Baltic Journal of Economic Studies, 4(2), 184-190. Search in Google Scholar

[9] Li, R. (2019). Public participation and its limits in legislative consultation: A case study on local legislation in China. The Theory and Practice of Legislation, 7(1), 27-45. Search in Google Scholar

[10] Knight, J. T. (2019). Splitting Sovereignty: The Legislative Power and the Constitution’s Federation of Independent States. Geo. JL & Pub. Pol’y, 17, 683. Search in Google Scholar

[11] Nguyen, H., & Bui, X. N. (2019). Predicting blast-induced air overpressure: A robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research, 28(3), 893-907. Search in Google Scholar

[12] Carter, S. M., Rogers, W., Win, K. T., et al. (2020). The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. The Breast, 49, 25-32. Search in Google Scholar

[13] Schönberger, D. (2019). Artificial intelligence in healthcare: A critical analysis of the legal and ethical implications. International Journal of Law and Information Technology, 27(2), 171-203. Search in Google Scholar

[14] Greener, J. G., Kandathil, S. M., Moffat, L., et al. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1), 40-55. Search in Google Scholar

[15] Ye,T.,Wang,G. & Cai,C.(2023).Cyclic Convolutional Neural Network Model Based on Artificial Intelligence. Applied Mathematics and Nonlinear Sciences (aop). Search in Google Scholar

[16] Wei, J., Chu, X., Sun, X. Y., et al. (2019). Machine learning in materials science. InfoMat, 1(3), 338-358. Search in Google Scholar

[17] Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2021). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE transactions on knowledge and data engineering. Search in Google Scholar

[18] Cai, Z., Xiong, Z., Xu, H., et al. (2021). Generative adversarial networks: A survey toward private and secure applications. ACM Computing Surveys (CSUR), 54(6), 1-38. Search in Google Scholar

[19] Karras, T., Aittala, M., Laine, S., et al. (2021). Alias-free generative adversarial networks. Advances in Neural Information Processing Systems, 34, 852-863. Search in Google Scholar

[20] Caplin, A., Dean, M., Leahy, J. (2022). Rationally inattentive behavior: Characterizing and generalizing Shannon entropy. Journal of Political Economy, 130(6), 1676-1715. Search in Google Scholar

[21] Shternshis, A., Mazzarisi, P., Marmi, S. (2022). Measuring market efficiency: The Shannon entropy of high-frequency financial time series. Chaos, Solitons & Fractals, 162, 112403. Search in Google Scholar

[22] Song, Y., Fu, Q., Wang, Y. F., et al. (2019). Divergence-based cross entropy and uncertainty measures of Atanassov’s intuitionistic fuzzy sets with their application in decision making. Applied Soft Computing, 84, 105703. Search in Google Scholar