The analysis of rural tourism image optimization under the internet of things and deep learning
Abstract This study aims to utilize deep learning technology to optimize rural tourism image, enhance visitor experience, and promote sustainable development. By deploying sensors for real-time monitoring of the environment and visitor flow in rural scenic areas, combined with a Dense Convolutional...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81868-z |
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| author | Xinghua Wang |
| author_facet | Xinghua Wang |
| author_sort | Xinghua Wang |
| collection | DOAJ |
| description | Abstract This study aims to utilize deep learning technology to optimize rural tourism image, enhance visitor experience, and promote sustainable development. By deploying sensors for real-time monitoring of the environment and visitor flow in rural scenic areas, combined with a Dense Convolutional Neural Network (DenseNet), automatic identification and analysis of rural landscapes are achieved. Using rural tourism along the Yellow River as a case study, this study constructs a tourism image evaluation and optimization model based on big data. The results indicate that the model performs excellently in terms of accuracy and robustness, significantly improving the presentation of rural tourism images. The study shows that realism and service facilities have the greatest impact on rural tourism image, underscoring the value of technological means in optimizing the rural tourism image. |
| format | Article |
| id | doaj-art-2934b93cf8514b9999064a4b78e9bac2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2934b93cf8514b9999064a4b78e9bac22024-12-08T12:26:12ZengNature PortfolioScientific Reports2045-23222024-12-0114112510.1038/s41598-024-81868-zThe analysis of rural tourism image optimization under the internet of things and deep learningXinghua Wang0The Tourism College of Changchun UniversityAbstract This study aims to utilize deep learning technology to optimize rural tourism image, enhance visitor experience, and promote sustainable development. By deploying sensors for real-time monitoring of the environment and visitor flow in rural scenic areas, combined with a Dense Convolutional Neural Network (DenseNet), automatic identification and analysis of rural landscapes are achieved. Using rural tourism along the Yellow River as a case study, this study constructs a tourism image evaluation and optimization model based on big data. The results indicate that the model performs excellently in terms of accuracy and robustness, significantly improving the presentation of rural tourism images. The study shows that realism and service facilities have the greatest impact on rural tourism image, underscoring the value of technological means in optimizing the rural tourism image.https://doi.org/10.1038/s41598-024-81868-zRural tourism imageInternet of thingsDeep learningDense convolutional neural networkIntegrated development |
| spellingShingle | Xinghua Wang The analysis of rural tourism image optimization under the internet of things and deep learning Scientific Reports Rural tourism image Internet of things Deep learning Dense convolutional neural network Integrated development |
| title | The analysis of rural tourism image optimization under the internet of things and deep learning |
| title_full | The analysis of rural tourism image optimization under the internet of things and deep learning |
| title_fullStr | The analysis of rural tourism image optimization under the internet of things and deep learning |
| title_full_unstemmed | The analysis of rural tourism image optimization under the internet of things and deep learning |
| title_short | The analysis of rural tourism image optimization under the internet of things and deep learning |
| title_sort | analysis of rural tourism image optimization under the internet of things and deep learning |
| topic | Rural tourism image Internet of things Deep learning Dense convolutional neural network Integrated development |
| url | https://doi.org/10.1038/s41598-024-81868-z |
| work_keys_str_mv | AT xinghuawang theanalysisofruraltourismimageoptimizationundertheinternetofthingsanddeeplearning AT xinghuawang analysisofruraltourismimageoptimizationundertheinternetofthingsanddeeplearning |