Indexing Metadata

1 Title of the Article Cross-Media Data Fusion and Intelligent Analytics Framework for Comprehensive Information Extraction and Value Mining
2 Author's name Yuping Yuan: Information and Network Institute, Radio, Film and Television Design and Research Institute Co., Ltd, Beijing, China
3 Author's name Haozhong Xue
4 Subject Computer Science
5 Keyword(s) Cross-Media Data Fusion, Intelligent Analytics, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM)
6 Abstract

The core content of this paper is to analyze the integration of cross-media data and artificial intelligence. The core of the whole paper is to analyze different types of media data such as text, image and video. With the increasing complexity and quantity of multimedia data, it can be seen that traditional methods can no longer meet the current data needs. Therefore, some advanced technologies need to be integrated, such as convolutional neural network (CNN) Long and short memory network (LSTM) and graph neural network (GNN) to extract the data content. Therefore, the core of this paper emphasizes the centralized extraction of innocuous complex data through mixed and multi-transport facilities, and proposes that the framework can enhance information extraction and value mining, and this method can be more applied to the media medical and security fields.

7 Publisher Innovative Research Publication
8 Journal Name; vol., no. International Journal of Innovative Research in Computer Science & Technology (IJIRCST); Volume-13 Issue-1
9 Publication Date January 2025
10 Type Peer-reviewed Article
11 Format PDF
12 Uniform Resource Identifier https://ijircst.org/view_abstract.php?title=Cross-Media-Data-Fusion-and-Intelligent-Analytics-Framework-for-Comprehensive-Information-Extraction-and-Value-Mining&year=2025&vol=13&primary=QVJULTEzMzk=
13 Digital Object Identifier(DOI) 10.55524/ijircst.2025.13.1.7   https://doi.org/10.55524/ijircst.2025.13.1.7
14 Language English
15 Page No 50-57

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