Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates

Zhang, Wenkai; Chen, Wei; Pan, Hongjing; Sanaeifar, Alireza; Hu, Yan; Shi, Wanghong; Guo, Jie; Ding, Lejia; Zhou, Jihong; Li, Xiaoli; He, Yong

doi:10.1016/j.ijbiomac.2024.134569

International Journal of Biological Macromolecules

2024

DOI: 10.1016/j.ijbiomac.2024.134569

|View full text |Cite

Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates

Wenkai Zhang,

Wei Chen,

Hongjing Pan

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Exploring Semantic Understanding and Generative Modeling in Speech-Text Multimodal Data Fusion

Yu,

Wang,

Sun

et al. 2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Accurate semantic understanding is crucial in the field of human-computer interaction, and it can also greatly improve the comfort of users. In this paper, we use semantic emotion recognition as the research object, collect speech datasets from multiple domains, and extract their semantic features from natural language information. The natural language is digitized using word embedding technology, and then machine learning methods are used to understand the text’s semantics. The attention mechanism is included in the construction of a multimodal Attention-BiLSTM model. The model presented in this paper convergence is achieved in around 20 epochs of training, and the training time and effectiveness are better than those of the other two models. The model in this paper has the highest recognition accuracy. Compared to the S-CBLA model, the recognition accuracy of five semantic emotions, namely happy, angry, sad, sarcastic, and fear, has improved by 24.89%, 15.75%, 1.99%, 2.5%, and 8.5%, respectively. In addition, the probability of correctly recognizing the semantic emotion “Pleasure” in the S-CBLA model is 0.5, while the probability of being recognized as “Angry” is 0.25, which makes it easy to misclassify pleasure as anger. The model in this paper, on the other hand, is capable of distinguishing most semantic emotion types. To conclude, the above experiments confirm the superiority of this paper’s model. This paper’s model improves the accuracy of recognizing semantic emotions and is practical for human-computer interaction.

show abstract

Exploring Semantic Understanding and Generative Modeling in Speech-Text Multimodal Data Fusion

Yu,

Wang,

Sun

et al. 2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Rapid identification of the aging time of Liupao tea using AI-multimodal fusion sensing technology combined with analysis of tea polysaccharide conjugates

Cited by 1 publication

References 46 publications

Exploring Semantic Understanding and Generative Modeling in Speech-Text Multimodal Data Fusion

Exploring Semantic Understanding and Generative Modeling in Speech-Text Multimodal Data Fusion

Contact Info

Product

Resources

About