Volume 16 , Issue 1 , PP: 252-268, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohanaprakash T. A. 1 * , Muthalakshmi M. 2 , Vijaya A. 3 , Selvakumari S. 4 , E. Ajitha 5 , Naveen P. 6
Doi: https://doi.org/10.54216/JCIM.160118
The exponential growth of the Internet, distributed computing, and search engines has led to a steady improvement in the quality of Natural languge processing translation platforms that rely on these technologies. However, reusing the corpus is a challenge in the conventional translation setting. Other issues that translators frequently face include a tight cycle, challenging software manipulation, difficult internal and external cooperation, and inconsistent translation style. From this, the Natural languge processing Translation System (ETS) emerges incognito, with the primary goal of assisting all users in increasing translation efficiency and decreasing translation costs. This work uses research on Intelligent Big Data systems and Edge Computing to an Natural languge processing Translation System (BD-EC-ETS), which significantly advances the field of Natural languge processing translation with higher accuracy. With the Internet of Things and big data techniques, this article will examine a cutting-edge system for Natural languge processing translation software, identify its flaws and shortcomings, and provide data research to inform a system upgrade.The study focuses on Natural languge processing translation systemsto enhance the quality of the system's output translations. This paperexamines the current interactive language translation systems, focusing on those that use phrase models and get their information from edge computing enabled by the Internet of Things. Machine-efficient and cost-effective translation has emerged as a solution to such problems; researchers have focused on enhancing the Natural languge processing translation system's output quality via BD-EC-ETS. The system's outstanding performance in improving Natural languge processing translation accuracy and recall rate has been shown. Compared to the current Natural languge processing translation system, the accuracy improves by over 22% with fewer iterations and by as much as 100% with 80 iterations.
Natural languge processing Translation , Language , Big Data (BD) , Edge Computing (EC) , IoT
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