Evidence map›Paper›PMID 39738236›Full record

ArticleScientific reports2024

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

Zhen Kuang, Junyu Su, Ahmad Latifian, Sanli Eshraghi, Alireza Ghafari

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhen KuangFaculty of Data Science, City University of Macau, Macau, 999078, China.
Junyu SuFaculty of Data Science, City University of Macau, Macau, 999078, China. sjy21666@sina.com.
Ahmad LatifianDepartment of Management, Faculty of Economics and Administrative sciences, Ferdowsi University of Mashhad, Mashhad, Iran.
Sanli EshraghiDepartment of geography, Faculty of planning and environmental sciences, University of Tabriz, Tabriz, Iran.
Alireza GhafariDepartment of Architecture, Rafsanjan Branch, Islamic Azad University, Rafsanjan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

CitiesComputer SecurityNeural Networks, ComputerCommunity ParticipationHumansPrivacySustainable DevelopmentUrban RenewalCivic rightsCybersecurityData protectionEmerging technologiesSmart buildingSmart citiesUrban governance

Identifiers

PMID39738236
PMCPMC11685571

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.