Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Zheng, Xuana; b; * | Sun, Zhenga
Affiliations: [a] College of Medical Information Engineering, Gannan Medical University, Ganzhou, Jiangxi, China | [b] School of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
Correspondence: [*] Corresponding author: Xuan Zheng, College of Medical Information Engineering, Gannan Medical University, Ganzhou 314000, Jiangxi, China. E-mail: xuanzheng@gmu.edu.cn.
Abstract: With the rapid development of Internet of Things (IoT) technology, a large amount of sensor data, images, voice, and other data are being widely used, bringing new opportunities for intelligent and cross-domain information fusion. Effective feature extraction and accurate recognition remain urgent issues to be addressed. This article explores the application of deep learning (DL) in multimodal data recognition methods of the IoT and proposes path optimization for multimodal data recognition methods of the IoT under DL. This article also provides in-depth analysis and discussion on the optimization of multimodal data recognition models based on DL, as well as specific measures for optimizing the path of multimodal data recognition based on DL. In this paper, the long short-term memory (LSTM) technology is introduced, and the LSTM technology is used to optimize the multi-modal data recognition method. It can be seen from the comparison that the processing efficiency of data analysis, information fusion, speech recognition, and emotion analysis of the multimodal data recognition method optimized by LSTM technology is 0.29, 0.35, 0.31, and 0.24 higher, respectively, than that of data analysis, information fusion, speech recognition, and emotion analysis before optimization. Introducing DL methods in multimodal data recognition of the IoT can effectively improve the effectiveness of data recognition and fusion and achieve higher levels of recognition for speech recognition and sentiment analysis.
Keywords: Multimodal data recognition method, deep learning, internet of things, long short term memory network technology, data recognition and optimization processing
DOI: 10.3233/IDT-230267
Journal: Intelligent Decision Technologies, vol. 18, no. 2, pp. 759-767, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl