Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105397
DC Field | Value | Language |
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dc.contributor | School of Hotel and Tourism Management | - |
dc.creator | Gao, S | - |
dc.creator | Yang, X | - |
dc.creator | Long, H | - |
dc.creator | Zhang, F | - |
dc.creator | Xin, Q | - |
dc.date.accessioned | 2024-04-12T06:52:12Z | - |
dc.date.available | 2024-04-12T06:52:12Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105397 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Gao S, Yang X, Long H, Zhang F, Xin Q. The Sustainable Rural Industrial Development under Entrepreneurship and Deep Learning from Digital Empowerment. Sustainability. 2023; 15(9):7062 is available at https://doi.org/10.3390/su15097062. | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Digital development | en_US |
dc.subject | Entrepreneurship | en_US |
dc.subject | Industrial planning | en_US |
dc.subject | Neural network | en_US |
dc.subject | Sustainable development | en_US |
dc.title | The sustainable rural industrial development under entrepreneurship and deep learning from digital empowerment | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 9 | - |
dc.identifier.doi | 10.3390/su15097062 | - |
dcterms.abstract | This paper aims to realize the planning of resource utilization and development of rural industries endowed by digitalization under entrepreneurship. First, the global classic practical experience of digitizing rural industries is studied, and the development model of existing rural industries is captured from the perspective of entrepreneurship. Second, the influencing factors of rural industrial development are extracted, the structure of resource development is analyzed, and a Neural Network (NN) model of industrial development aiming at expected per capita annual income is established. In addition, a Genetic Algorithm (GA) is introduced to learn the weights of influencing factors in the model. The structure of the NN is determined through extensive experiments. Finally, conclusions are drawn through the simulation and experiment of NN and GA. Tourism, infrastructure, and transportation planning have weights of 7.79, 5.6, and 6.4, respectively, and these three sectors should be vigorously developed. In the future, the weight values of these factors can be used for reference, and the development of various aspects can be refined. This paper clarifies the core of industrial development in rural revitalization based on the perspective of entrepreneurship. The problem of how to realize the optimal utilization of resources is solved scientifically and rationally through the mathematical model. The introduction of deep learning algorithm models provides data support for resource allocation and industrial planning in the process of digital empowerment of traditional rural industries, which is of great value and significance for exploring digital models for rural industry development. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sustainability, May 2023, v. 15, no. 9, 7062 | - |
dcterms.isPartOf | Sustainability | - |
dcterms.issued | 2023-05 | - |
dc.identifier.scopus | 2-s2.0-85159280390 | - |
dc.identifier.eissn | 2071-1050 | - |
dc.identifier.artn | 7062 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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sustainability-15-07062.pdf | 5.44 MB | Adobe PDF | View/Open |
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