The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs Archives
Because of the unique attributes of archive information, it is challenging to manage and effectively retrieve archive information in the archive information management practice. This paper designs and develops the first general higher-order Neural Network Model for archives. Based on the analysis of...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9159557/ |
| _version_ | 1828908582448398336 |
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| author | Meng Wang Lilan Chen |
| author_facet | Meng Wang Lilan Chen |
| author_sort | Meng Wang |
| collection | DOAJ |
| description | Because of the unique attributes of archive information, it is challenging to manage and effectively retrieve archive information in the archive information management practice. This paper designs and develops the first general higher-order Neural Network Model for archives. Based on the analysis of the correlation, the relevance of the weight model, the study of technical methods about the core weight, the direction weight retrieval, and the statistical ranking of the results, this paper designs a corresponding archive information analysis system. Finally, this paper adopts the B/S development model by applying the relevance ranking weight algorithm into the comprehensive archive retrieval activities, which not only enhances the intelligence and efficiency of the archive retrieval, but also can act as a standard example to demonstrate informatization construction for archive management. This paper compares this algorithm with two other existing retrieval algorithms and verifies the practicability of the relevance algorithm by evaluating the algorithm and the default retrieval algorithm using the NDCG evaluation method. |
| first_indexed | 2024-12-13T18:12:08Z |
| format | Article |
| id | doaj.art-9df2302e09f84e1c9d8f6c112866687d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:12:08Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9df2302e09f84e1c9d8f6c112866687d2022-12-21T23:35:56ZengIEEEIEEE Access2169-35362020-01-01814529014529610.1109/ACCESS.2020.30143539159557The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs ArchivesMeng Wang0https://orcid.org/0000-0002-8411-2717Lilan Chen1https://orcid.org/0000-0002-3609-1325School of Information Management, Sun Yat-Sen University, Guangzhou, ChinaSchool of Foreign Languages, Guangdong Pharmaceutical University, Guangzhou, ChinaBecause of the unique attributes of archive information, it is challenging to manage and effectively retrieve archive information in the archive information management practice. This paper designs and develops the first general higher-order Neural Network Model for archives. Based on the analysis of the correlation, the relevance of the weight model, the study of technical methods about the core weight, the direction weight retrieval, and the statistical ranking of the results, this paper designs a corresponding archive information analysis system. Finally, this paper adopts the B/S development model by applying the relevance ranking weight algorithm into the comprehensive archive retrieval activities, which not only enhances the intelligence and efficiency of the archive retrieval, but also can act as a standard example to demonstrate informatization construction for archive management. This paper compares this algorithm with two other existing retrieval algorithms and verifies the practicability of the relevance algorithm by evaluating the algorithm and the default retrieval algorithm using the NDCG evaluation method.https://ieeexplore.ieee.org/document/9159557/Higher-order neural network modelcore weightdirection weightevaluation methodarchive informationmodern Guangdong customs archives |
| spellingShingle | Meng Wang Lilan Chen The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs Archives IEEE Access Higher-order neural network model core weight direction weight evaluation method archive information modern Guangdong customs archives |
| title | The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs Archives |
| title_full | The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs Archives |
| title_fullStr | The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs Archives |
| title_full_unstemmed | The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs Archives |
| title_short | The General Higher-Order Neural Network Model and Its Application to the Archive Retrieval in Modern Guangdong Customs Archives |
| title_sort | general higher order neural network model and its application to the archive retrieval in modern guangdong customs archives |
| topic | Higher-order neural network model core weight direction weight evaluation method archive information modern Guangdong customs archives |
| url | https://ieeexplore.ieee.org/document/9159557/ |
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