Digital Economy Oriented Tourism Industry Data Analysis in Semantic <scp>IoT</scp>

ABSTRACT The digital economy has revolutionized the tourism industry by integrating Semantic Internet of Things (IoT) technologies for enhanced data analysis. This paper proposes a comprehensive method for analyzing tourism flow using Semantic IoT. The method combines semantic data processing with advanced machine learning techniques, specifically stacked autoencoders and long short‐term memory (LSTM) models, to understand and predict changes in passenger flow. The approach addresses challenges of high dimensionality and complex temporal characteristics in tourism data, offering significant support for tourism management, improving operational efficiency, and enhancing the visitor experience. Experimental results demonstrate the effectiveness of the proposed method in analyzing and predicting tourism passenger flow data, providing accurate and insightful trends that are crucial for decision‐making in the tourism industry.

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Publication Details

Journal
Internet Technology Letters
Published
2024-10-23
DOI
https://doi.org/10.1002/itl2.597
Citations
3
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
3.40
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article

Digital Economy Oriented Tourism Industry Data Analysis in Semantic IoT

Junfeng Miao, B. D. Parameshachari, Wang Lu
3 citations
Internet Technology Letters
Digital Marketing and Social Media
3.40
article

Digital Economy Oriented Tourism Industry Data Analysis in Semantic IoT

Junfeng Miao, B. D. Parameshachari, Wang Lu
article en
3 citations

Abstract

ABSTRACT The digital economy has revolutionized the tourism industry by integrating Semantic Internet of Things (IoT) technologies for enhanced data analysis. This paper proposes a comprehensive method for analyzing tourism flow using Semantic IoT. The method combines semantic data processing with advanced machine learning techniques, specifically stacked autoencoders and long short‐term memory (LSTM) models, to understand and predict changes in passenger flow. The approach addresses challenges of high dimensionality and complex temporal characteristics in tourism data, offering significant support for tourism management, improving operational efficiency, and enhancing the visitor experience. Experimental results demonstrate the effectiveness of the proposed method in analyzing and predicting tourism passenger flow data, providing accurate and insightful trends that are crucial for decision‐making in the tourism industry.

Internet Technology LettersVol. 8(4)
Nitte University (IN), Shenyang University (CN), Henan Normal University (CN)
Openalex Percentile: Top 7%
Digital Marketing and Social Media
3.40
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