Analysis System of MICE Tourism Economic Development Strategy Based on Machine Learning Algorithm

The environmental effect of the meetings, incentives, conventions, and exhibitions (MICE) industry is as extensive as its economic impact. Visitors attending events use a wide range of service providers, including airline car rental firms, restaurants, hotels, theaters, and tour operators. Tradition...

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Veröffentlicht in:Mobile information systems Jg. 2022; S. 1 - 16
Hauptverfasser: Hu, Bin, Yan, Bin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Hindawi 30.08.2022
John Wiley & Sons, Inc
Schlagworte:
Accuracy
Algorithms
Back propagation
Back propagation networks
Bias
Economic analysis
Economic development
Economic forecasting
Economic impact
Environmental effects
Exhibitions
Hotels
Impact analysis
Incentives
Machine learning
Mathematical functions
Neural networks
Optimization
Population
Regression models
Supply & demand
System effectiveness
Time series
Tourism
Variables
Thailand
ISSN:1574-017X, 1875-905X
Online-Zugang:Volltext
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Zusammenfassung:The environmental effect of the meetings, incentives, conventions, and exhibitions (MICE) industry is as extensive as its economic impact. Visitors attending events use a wide range of service providers, including airline car rental firms, restaurants, hotels, theaters, and tour operators. Traditionally used tourism demand forecasting approaches rely heavily on univariate time series and multivariate regression models. Although these function-based prediction systems have demonstrated some effectiveness in forecasting tourism, they are unable to accurately capture the link between tourist demand and supply as a feed-forward neural network does (FFNN). Research has shown that an FFNN can outperform regression and time-series algorithms when it comes to forecasting tourism data. This research, for the first time, expands the use of neural networks in tourist demand creation by combining a hybrid FFNN and chimp optimization learning algorithm (i.e., FFNN-ChOA) into a nonlinear tourism demand dataset. In terms of predicting accuracy, FFNN-ChOA surpasses traditional backpropagation neural networks, regression models, and time-series models.
Bibliographie:ObjectType-Article-1
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ISSN:1574-017X
1875-905X
DOI:10.1155/2022/1283040