2 citations · 2 across the 5 of their papers we have counts for
5 papers
Maximizing Generalization: The Effect of Different Augmentation Techniques on Lightweight Vision Transformer for Bengali Character Classification
Rafi Hassan Chowdhury, Naimul Haque, Kaniz Fatiha
Deep learning models have proven to be highly effective in computer vision, with deep convolutional neural networks achieving impressive results across various computer vision task…
BornoViT: A Novel Efficient Vision Transformer for Bengali Handwritten Basic Characters Classification
Rafi Hassan Chowdhury, Naimul Haque, Kaniz Fatiha
Handwritten character classification in the Bengali script is a significant challenge due to the complexity and variability of the characters. The models commonly used for classifi…
SpectroFusion-ViT: A Lightweight Transformer for Speech Emotion Recognition Using Harmonic Mel-Chroma Fusion
Faria Ahmed, Rafi Hassan Chowdhury, Fatema Tuz Zohora Moon +1
Speech is a natural means of conveying emotions, making it an effective method for understanding and representing human feelings. Reliable speech emotion recognition (SER) is centr…
Bi-cLSTM: Residual-Corrected Bidirectional LSTM for Aero-Engine RUL Estimation
Rafi Hassan Chowdhury, Nabil Daiyan, Faria Ahmed +2
Accurate Remaining Useful Life (RUL) prediction is a key requirement for effective Prognostics and Health Management (PHM) in safety-critical systems such as aero-engines. Existing…
MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classification
Rafi Hassan Chowdhury, Sabbir Ahmed
Ensuring food safety is critical due to its profound impact on public health, economic stability, and global supply chains. Cultivation of Mango, a major agricultural product in se…