Utilizing Artificial neural networks (ANN) to regulate Smart cities for sustainable Urban Development and...

The advent of smart cities has brought about a paradigm shift in urban management and citizen engagement. By leveraging technological advancements, cities are now able to collect and analyze extensive data to optimize service delivery, allocate resources efficiently, and enhance the overall well-being of residents. However, as cities become increasingly interconnected and data-dependent, concerns related to data privacy and security, as well as citizen participation and representation, have surfaced. This article emphasizes the significance of regulating smart cities to foster a culture of citizenship and safeguard civic rights, security, and privacy. Effective regulatory frameworks are crucial in striking a balance between technological innovation and the protection of fundamental rights. The article examines various pivotal policies and regulations concerning data protection, cybersecurity, and citizen involvement in decision-making processes. Data protection regulations play a vital role in safeguarding individuals' personal information and ensuring its collection, processing, and usage comply with legal requirements. Similarly, cybersecurity regulations are indispensable in fortifying smart city systems against cyber threats. Regulations promoting citizen engagement and participation can cultivate a sense of ownership and responsibility among citizens, contributing to the creation of more inclusive and democratic urban environments. This study used an artificial neural network (ANN) to examine the effects of cyber security regulations, citizen participation, information sharing laws, and data protection on smart city development. Implementing these policies led to a 30% increase in citizen participation over 4 years, which continued even when regulations were temporarily suspended. However, unchanged or reduced regulations led to declines in information sharing. The neural network's predictions showed acceptable error compared to experimental results.