A new balanced Bangla handwritten character dataset paired with a multi-head attention hybrid model using EfficientNetB3, ViT, and Conformer achieves high accuracy and strong generalization.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
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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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Multi-Head Attention based interaction-aware architecture for Bangla Handwritten Character Recognition: Introducing a Primary Dataset
A new balanced Bangla handwritten character dataset paired with a multi-head attention hybrid model using EfficientNetB3, ViT, and Conformer achieves high accuracy and strong generalization.
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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.