Global Covariance Pooling (GCP) improves deep networks by capturing second-order feature statistics, and is especially effective for fine-grained recognition. Because covariance matrices live on the Symmetric Positive Definite (SPD) manifold, a normalization step is required before the Euclidean classifier. The faithfu...
Md Rifat Ur Rahman, Md Raihan Khan, Md Sakib Hossain Shovon et al.· 0 citations
An explainable deep learning framework based on sparse projected residual networks to predict fluid, crystallized, and total intelligence from resting-state functional magnetic resonance imaging in 5,285 participants from the Adolescent Brain Cognitive Development study suggests that intelligence emerges from the inter...
Objective cognitive assessment from neural signals supports neurorehabilitation, but individual-level prediction from task-based fMRI (tfMRI) remains difficult because neural features coexist with substantial demographic and scanner-related variation. We present the Multi-task Activation and Contrast Network (MAC-Net),...
This study establishes a computationally efficient, transparent, and robust pathway for automated disease diagnosis in precision agriculture by introducing CNN-FusionViT-GNN, a explainable hybrid multi-branch framework that synergizes the fine-grained texture extraction of a DenseNet201 backbone, the global contextual...
M. Billah, Saifuddin Sagor, Shahariar Hossain et al.· Scientific Reports· 1 citation
Global Covariance Pooling (GCP) improves deep networks by capturing second-order feature statistics, and is especially effective for fine-grained recognition. Because covariance matrices live on the Symmetric Positive Definite (SPD) manifold, a normalization step is required before the Euclidean classifier. The faithfu...
Md Rifat Ur Rahman, Md Raihan Khan, Md Sakib Hossain Shovon et al.· 0 citations
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