The decomposition-free logarithm is both faster and more accurate than the spectral logarithm and than the square-root approximations it replaces, and at matched basis and degree the log target beats the square-root target, confirming that the gain comes from the faithful Riemannian map rather than from a better polyno...
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...
It is demonstrated that covariate-aware multi-task modeling yields reproducible cognitive-function estimations, establishing a robust neural engineering framework for clinical translation.
The results provide exploratory cross-dataset evidence for stain-aware, parameter-efficient federation; they do not establish formal privacy, patient-level independence, prospective deployment, or clinical validation.
Muhammad Muhtasim Shahriar, M. M. Golam Hafiz, Saad Aloteibi et al.· 0 citations
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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