The role of English in promoting international cultural exchange in the context of big data and deep learning

Article type: Research Article

Authors: Wei, Tingting

Affiliations: School of Foreign Studies, Huanggang Normal University, Huanggang, Hubei, China | E-mail: wei_tingting@outlook.com

Correspondence: [*] Corresponding author: School of Foreign Studies, Huanggang Normal University, Huanggang, Hubei, China. E-mail: wei_tingting@outlook.com.

Abstract: English is a common global communication medium for exchanging diverse cultural elements between countries/people. The role of language is significant in developing political and economic aspects between nations. Such developments rely on voluptuous data from the past to the present happenings, reasoning, and conversations. Considering the significance of the English language in international cultural exchange and developments, this article introduces a Harmonious Data Analytical Scheme (DAS)-processed by Deep Learning (DL) paradigm. This scheme analyzes the available and accumulated data for cultural improvements and exchanges between diverse countries. The DL process identifies the matching aspects between the country’s culture and the accumulated data. Identifying such a point is repeatedly verified for the developments from the beginning to the current level of cultural improvements. The process discards the obsolete cultural data that are less considerable for exchanges and developments in the past. This process refines precise data to be utilized in further cultural exchanges reducing the data handling time and complexity. Finally, the proposed scheme is reliable in identifying the cultural development-based data through the common English language aspects. The DAS-DL method attains Identification rate by 0.98s, refining rate by 0.79% and data accumulation rate by 95.2% compared to existing methods.

Keywords: Big data, cultural exchange, data analytical scheme, deep learning, English language

DOI: 10.3233/JCM-237021

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 1, pp. 369-384, 2024