JOURNAL ARTICLE
Robot Translation Based on Computer Vision for Cultural Psychology of English Culture Education.
Published In: International Journal of Humanoid Robotics, 2023, v. 20, n. 2/3. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Zheng, Xue 3 of 3
Abstract
Individuals use language in a wide range of contexts. It is a major part of the culture. Teaching students how to speak English in a different manner requires adopting cultural attitudes and behaviors. This learning style has a tremendous sense of belonging, community, and intent. In addition, it motivates learners to create a difference in their neighborhoods and communities around the world. A simple way to incorporate culture into the curriculum is to use the abilities and narratives of the wider community. Multilingual classrooms present an incredible task for English teachers because of the students' wide range of linguistic backgrounds. Because they are afraid of committing mistakes, the students in multilingual classrooms lack self-confidence to communicate in English. Therefore, in this paper, Robot Interaction for Social Cultural Education (RI-SCE) method is proposed to overcome the challenges mentioned above. It uses Deep Machine language and Artificial Intelligence to interact with robots-based computer vision for cultural psychology of English cultural education. As a result, the simulation shows the importance of robot translation in performance, accuracy, efficiency, security, and flexibility compared to the other available models. The model proposed here achieves standard accuracy of 95.2%. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Humanoid Robotics. 2023/04, Vol. 20, Issue 2/3, p1
- Document Type:Article
- Subject Area:Education
- Publication Date:2023
- ISSN:02198436
- DOI:10.1142/S0219843622500062
- Accession Number:164629560
- Copyright Statement:Copyright of International Journal of Humanoid Robotics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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