1 citations · 2 across the 11 of their papers we have counts for
17 papers · 1 filter
ECA-BLS: An Efficient Complex-Augmented Broad Learning System
A. Rahaman, A. Quadir, M. Sajid +2
Broad Learning System (BLS) is an efficient alternative to deep architectures due to its fast training, analytical learning, and strong generalization under limited data. However,…
GraM-Diff: A Unified Graph-Mamba Diffusion Framework for EEG-Based Alzheimer's Disease Data Generation and Diagnosis
M. Tanveer, Ayush Singh Rana, Sanskriti Jain +5
Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer's disease (AD) detection, but deep learning methods are limited by small and im…
Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification
M. Sajid, A. Quadir, A. Rahaman +2
The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL), treat all training samples…
Physics-Informed Broad Learning System: An Efficient Backpropagation-Free Framework for Solving Partial Differential Equations
Pinki Khatun, M. Sajid, Abhinav Jha +1
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs) by embedding governing physical laws into deep neural…
Twin Restricted Kernel Machines for Multiview Classification
A. Quadir, M. Sajid, Mushir Akhtar +1
Multi-view learning (MVL) is an emerging field in machine learning that focuses on improving generalization performance by leveraging complementary information from multiple perspe…
RVFL-X: A Novel Randomized Network Based on Complex Transformed Real-Valued Tabular Datasets
M. Sajid, Mushir Akhtar, A. Quadir +1
Recent advancements in neural networks, supported by foundational theoretical insights, emphasize the superior representational power of complex numbers. However, their adoption in…