28 citations · 94 across the 23 of their papers we have counts for
8 papers · 1 filter
GBTSVR: Twin Support Vector Regression via Granular Ball Computing
A. Quadir, M. Tanveer
Regression models used in real-world applications are often challenged by noise, outliers, data heterogeneity, and asymmetric error characteristics, which severely limit the effect…
GBFRVFL: Granular-Ball Computing-Based Fuzzy Random Vector Functional Link Network
A. Quadir, A. Rahaman, P. N. Suganthan +1
In practical machine learning tasks, data are often contaminated with noise, outliers, and class imbalance, which can degrade the performance of conventional models. While random v…
Robust Broad Learning System with Wave Loss for Classification under Data Uncertainty
Mushir Akhtar, A. Varshney, A. Quadir +3
Broad Learning System (BLS) offers an efficient alternative to deep architectures by enabling fast learning through randomized feature mapping and closed-form solutions. However, i…
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…
RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network
A. Rahaman, A. Quadir, M. Tanveer
The dominance of majority classes in real-world datasets poses a fundamental challenge to randomized neural networks, often biasing decision boundaries and overlooking critical min…
Robust Dual-Model Collaborative Random Vector Functional Link Network
A. Quadir, A. Rahaman, Mushir Akhtar +1
Random vector functional link (RVFL) networks are lightweight and fast neural models that offer efficient training and strong generalization through randomized hidden-layer weights…
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…