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Consumption Structure Optimization Strategy for Scenic Spots Using the Deep Learning Model under Digital Economy.

Computational intelligence and neuroscience
The purpose is to find out the problems existing in the consumption economy structure of the scenic spots and to promote the rationalization of the consumption economy of the scenic spots. Based on the analysis of the applicability of the backpropaga...

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions.

Sensors (Basel, Switzerland)
The recent upsurge of smart cities' applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged ...

Interpretable machine learning approach to analyze the effects of landscape and meteorological factors on mosquito occurrences in Seoul, South Korea.

Environmental science and pollution research international
Mosquitoes are the underlying cause of various public health and economic problems. In this study, patterns of mosquito occurrence were analyzed based on landscape and meteorological factors in the metropolitan city of Seoul. We evaluated the influen...

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

Computational intelligence and neuroscience
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 chang...

Facility Layout Optimization of Urban Public Sports Services under the Background of Deep Learning.

Computational intelligence and neuroscience
The spatial layout and optimization of social facilities for sports are related to many factors such as urban economy, transportation, population, and urban planning. With the rapid development of artificial intelligence today, deep learning came int...

Deep Learning-Based Artificial Neural Network-Cellular Automata Model in Constructing Landscape Gene in Shaanxi Ancient Towns under Rural Revitalization.

Computational intelligence and neuroscience
With the development of modern industrialization, the rational planning of land resources, especially rural settlements (RSs), has become an important part of rural revitalization. Optimizing the RS spatial layout and enhancing its evolution simulati...

Innovative Design of a Cloud Tour Guide Robot for Smart Cities under the Principle of System Innovation Design.

Computational intelligence and neuroscience
In light of the ongoing occurrence of epidemics, the general populace frequently makes the decision to curtail their nomadic lifestyle in order to protect both their health and their safety. This has resulted in a number of issues, the most notable o...

Event-level prediction of urban crime reveals a signature of enforcement bias in US cities.

Nature human behaviour
Policing efforts to thwart crime typically rely on criminal infraction reports, which implicitly manifest a complex relationship between crime, policing and society. As a result, crime prediction and predictive policing have stirred controversy, with...

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach.

Computational intelligence and neuroscience
In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inc...

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction.

International journal of environmental research and public health
The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influen...