Evidence map›Paper›PMID 35909860›Full record

ArticleComputational intelligence and neuroscience2022

Research on Architectural Planning and Landscape Design of Smart City Based on Computational Intelligence.

Nan Shao

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. 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

1 author.

Nan ShaoDepartment of Civil Engineering & Architectural, Nanyang Normal University, Nanyang 473061, China.ORCID https://orcid.org/0000-0002-8807-0374

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

City brain is a complex system, including online center, server network, and system with given algorithm. The core of the city brain is the intelligent system. After putting the urban brain into the intelligent nerve center, on the basis of not changing its original data structure, combining its own characteristics for design and then integrating into application, it can intelligently change the urban management mode. Urban planning leads the development of smart cities on a certain meaning, and smart city planning must have scientific and rational urban planning. The intelligent model is used to make urban planning form a more modern, convenient, and reasonable urban architectural planning. Some influential books on classical architectural theory are the theoretical basis of intelligent urban planning and even the trend and implementation blueprint of how smart cities will develop in the future. In this paper, four algorithms, ant colony algorithm, particle swarm optimization algorithm, genetic algorithm, and improved ant colony algorithm, are proposed to optimize the characteristics of urban architectural planning and landscape design; especially the security research of architecture and landscape characteristics is very important. The improved ant colony algorithm has the shortcoming of insufficient optimization ability in the face of complex path selection. By improving the influencing factors, a new ant colony algorithm is created. The improved ant colony algorithm achieves the best in security features, so it is advocated to use this algorithm for planning and design. The urban form in smart city aims to create a beautiful and comfortable urban environment, improve the competitiveness of cities in the rapid urbanization process, improve the living standards of the public, and shape the image of this beautiful city.

Indexed as

Artificial IntelligenceCity PlanningAlgorithmsCities

Identifiers

PMID35909860
PMCPMC9325592

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.