Future perspectives on artificial intelligence-driven translation and standardization of English biosensor terminology across cultures
Abstract
Standardized English language and efficient translation techniques are required to guarantee global usability and cultural relevance because of the growing dependence on biosensors in communication technologies. In particular, the research aims to understand the role of biosensors, particularly wearable sensors, in enabling communication in other cultures, like using a sign language (SL) translator system. The information would be recorded through motion sensors designed to determine hand movements and gestures based on any recorded movement. Raw data would need bandpass filtering of both extraneous artifacts and the surrounding noise. Visual Geometry Group 16 (VGG16) is applied for feature extraction from biosensor data. A novel Adjustable Moth Flame-Tuned Efficient Recurrent Neural Network (AMF-ERNN) transformer model that is used to achieve translations from sensor signals to text sentences is introduced. AMF is used to select and optimize the features of an ERNN model to enhance its efficiency and performance while translating sensor signals to text. Results show that the suggested model outperformed traditional algorithms by achieving accuracy (98.5%), recall (98.3%), precision (98.2%), F1-score (98.4%), and WER (35.5%). These results demonstrate the way biosensors can promote accurate and culturally aware translations. The study concludes by emphasizing the importance of English terminology standardization to enhance the accessibility and effectiveness of biosensor-based translation systems across diverse cultural contexts.
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