Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning
High-dimensional deep learning has been applied in all walks of life at present, among which the most representative one is the logistics path optimization combining multimedia with high-dimensional deep learning. Using multimedia logistics to explore and operate the best path can make the whole log...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2021/1474341 |
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| _version_ | 1832546006253699072 |
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| author | Huiying Zhang Jinjin Guo Guie Sun |
| author_facet | Huiying Zhang Jinjin Guo Guie Sun |
| author_sort | Huiying Zhang |
| collection | DOAJ |
| description | High-dimensional deep learning has been applied in all walks of life at present, among which the most representative one is the logistics path optimization combining multimedia with high-dimensional deep learning. Using multimedia logistics to explore and operate the best path can make the whole logistics industry get innovation and leap forward. How to use high-dimensional deep learning to conduct visual logistics operation management is an opportunity and a problem facing the whole logistics industry at present. The application of high-dimensional deep learning technology can help logistics enterprises improve their management level, realize intelligent decision-making, and enable accurate prediction. Starting from the total amount of logistics, regional layout, enterprise scale, and high-dimensional deep learning algorithm, this paper analyzes the current situation of China’s logistic development through multiweight analysis and explores the best path for multimedia logistics. |
| format | Article |
| id | doaj-art-a89605690e17445d9414c395025687a1 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-a89605690e17445d9414c395025687a12025-02-03T07:24:14ZengWileyAdvances in Multimedia1687-56992021-01-01202110.1155/2021/1474341Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep LearningHuiying Zhang0Jinjin Guo1Guie Sun2Chongqing Vocational College of TransportationChongqing Vocational College of TransportationChongqing Vocational College of TransportationHigh-dimensional deep learning has been applied in all walks of life at present, among which the most representative one is the logistics path optimization combining multimedia with high-dimensional deep learning. Using multimedia logistics to explore and operate the best path can make the whole logistics industry get innovation and leap forward. How to use high-dimensional deep learning to conduct visual logistics operation management is an opportunity and a problem facing the whole logistics industry at present. The application of high-dimensional deep learning technology can help logistics enterprises improve their management level, realize intelligent decision-making, and enable accurate prediction. Starting from the total amount of logistics, regional layout, enterprise scale, and high-dimensional deep learning algorithm, this paper analyzes the current situation of China’s logistic development through multiweight analysis and explores the best path for multimedia logistics.http://dx.doi.org/10.1155/2021/1474341 |
| spellingShingle | Huiying Zhang Jinjin Guo Guie Sun Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning Advances in Multimedia |
| title | Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning |
| title_full | Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning |
| title_fullStr | Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning |
| title_full_unstemmed | Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning |
| title_short | Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning |
| title_sort | multiweight cross multimedia logistics optimal path exploration by integrating high dimensional deep learning |
| url | http://dx.doi.org/10.1155/2021/1474341 |
| work_keys_str_mv | AT huiyingzhang multiweightcrossmultimedialogisticsoptimalpathexplorationbyintegratinghighdimensionaldeeplearning AT jinjinguo multiweightcrossmultimedialogisticsoptimalpathexplorationbyintegratinghighdimensionaldeeplearning AT guiesun multiweightcrossmultimedialogisticsoptimalpathexplorationbyintegratinghighdimensionaldeeplearning |