Regional tourism in Inter-Governmental Authority on Development: a comparative policy and institutional best practice approach

Ayorekire, Jim; Obua, Joseph; Manyara, Geoffrey

doi:10.1504/ijtp.2019.10021711

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in edge computing, there are differences in data structure and the number of types collected by edge devices. If we design a neural network for the data of each network edge device, the workload is huge [25][26]. At the same time, the model can only be applied to a single node or the edge device with the same data characteristics as the node, the universality is not high, and it can not give full play to the value of other heterogeneous data in the Internet of things [26].…”

Section: Tourism Data Processing and Multi-source Data Fusionmentioning

confidence: 99%

“…If we design a neural network for the data of each network edge device, the workload is huge [25][26]. At the same time, the model can only be applied to a single node or the edge device with the same data characteristics as the node, the universality is not high, and it can not give full play to the value of other heterogeneous data in the Internet of things [26]. To solve the problem of multisource heterogeneous data fusion without disclosing user privacy in edge computing, this paper proposes a multi-source heterogeneous data fusion algorithm based on multi-source data fusion [27][28][29][30].…”

Section: Tourism Data Processing and Multi-source Data Fusionmentioning

confidence: 99%

See 1 more Smart Citation

Tourism Information Data Processing Method Based on Multi‐Source Data Fusion

Gan

2021

Journal of Sensors

View full text Add to dashboard Cite

Urban social civilization and the quality of life of residents are gradually improved, and the development scale and trend of the leisure tourism industry have been growing. This paper constructs a multi-source data fusion model based on an ensemble learning algorithm, uses Ctrip 2020 open data set to train the model, and then obtains the tourism information data processing and prediction results. This paper takes the data of Ctrip as the training set and compares the trained model with the data of tunic and Feizhu. In this paper, sensor detection technology is used to analyze many famous scenic spots in China, including tourist type, gender, and location. The results show that tourism feature extraction results are consistent with data from trending flying bamboo, tunics, and other websites, according to the results of a multi-source fusion of tourism information. Among them, in the data of the first half of 2020, the prediction accuracy of the model after data processing is about 62%. Affected by the epidemic situation, the accuracy of the model is low. In the second half of the year, the prediction accuracy is 78%, which can be used to fuse tourism information in a short time. Therefore, the data show that the model has high learning ability and high trend prediction ability in tourism data processing, which can provide necessary information support for tourists.

show abstract

Section: Tourism Data Processing and Multi-source Data Fusionmentioning

confidence: 99%