MFCC CNN-LSTM model on TENG-based sensor glove data achieves 93.33% accuracy across 11 sign classes, outperforming random forest by 23 percentage points.
Scikit-learn: Machine learning in Python,
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Classical ML models outperform transformers in bilingual sentiment classification of government banking app reviews, revealing user dissatisfaction with transaction speed and interfaces.
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Development of ML model for triboelectric nanogenerator based sign language detection system
MFCC CNN-LSTM model on TENG-based sensor glove data achieves 93.33% accuracy across 11 sign classes, outperforming random forest by 23 percentage points.
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A Multi-Model Approach to English-Bangla Sentiment Classification of Government Mobile Banking App Reviews
Classical ML models outperform transformers in bilingual sentiment classification of government banking app reviews, revealing user dissatisfaction with transaction speed and interfaces.