28 citations · 94 across the 23 of their papers we have counts for
26 papers
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…
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…
Hypergraph Neural Network with State Space Models for Node Classification
A. Quadir, M. Tanveer
In recent years, graph neural networks (GNNs) have gained significant attention for node classification tasks on graph-structured data. However, traditional GNNs primarily focus on…
Randomized based restricted kernel machine for hyperspectral image classification
A. Quadir, M. Tanveer
In recent years, the random vector functional link (RVFL) network has gained significant popularity in hyperspectral image (HSI) classification due to its simplicity, speed, and st…
One Class Restricted Kernel Machines
A. Quadir, M. Sajid, M. Tanveer
Restricted kernel machines (RKMs) have demonstrated a significant impact in enhancing generalization ability in the field of machine learning. Recent studies have introduced variou…
TRKM: Twin Restricted Kernel Machines for Classification and Regression
A. Quadir, M. Tanveer
Restricted kernel machines (RKMs) have considerably improved generalization in machine learning. Recent advancements explored various techniques within the RKM framework, integrati…
Flexi-Fuzz least squares SVM for Alzheimer's diagnosis: Tackling noise, outliers, and class imbalance
Mushir Akhtar, A. Quadir, M. Tanveer +1
Alzheimer's disease (AD) is a leading neurodegenerative condition and the primary cause of dementia, characterized by progressive cognitive decline and memory loss. Its progression…
Robust Universum Twin Support Vector Machine for Imbalanced Data
M. Tanveer, A. Quadir
One of the major difficulties in machine learning methods is categorizing datasets that are imbalanced. This problem may lead to biased models, where the training process is domina…
Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing
M. Tanveer, R. K. Sharma, A. Quadir +1
In the domain of machine learning, least square twin support vector machine (LSTSVM) stands out as one of the state-of-the-art models. However, LSTSVM suffers from sensitivity to n…
Enhanced Feature Based Granular Ball Twin Support Vector Machine
A. Quadir, M. Sajid, M. Tanveer +1
In this paper, we propose enhanced feature based granular ball twin support vector machine (EF-GBTSVM). EF-GBTSVM employs the coarse granularity of granular balls (GBs) as input ra…
Granular Ball Twin Support Vector Machine
A. Quadir, M. Sajid, M. Tanveer
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture ModelsTwin support vector machine (TSVM) is an emerging machine learning model wi…
Ophthalmic Biomarker Detection with Parallel Prediction of Transformer and Convolutional Architecture
Md. Touhidul Islam, Md. Abtahi Majeed Chowdhury, Mahmudul Hasan +2
Ophthalmic diseases represent a significant global health issue, necessitating the use of advanced precise diagnostic tools. Optical Coherence Tomography (OCT) imagery which offers…
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning
M. Tanveer, R. K. Sharma, M. Sajid +1
The classification performance of the random vector functional link (RVFL), a randomized neural network, has been widely acknowledged. However, due to its shallow learning nature,…
Multiview Random Vector Functional Link Network for Predicting DNA-Binding Proteins
A. Quadir, M. Sajid, M. Tanveer
The identification of DNA-binding proteins (DBPs) is essential due to their significant impact on various biological activities. Understanding the mechanisms underlying protein-DNA…
Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles
A. Quadir, Mushir Akhtar, M. Tanveer
Multiview learning (MvL) is an advancing domain in machine learning, leveraging multiple data perspectives to enhance model performance through view-consistency and view-discrepanc…
Multiview learning with twin parametric margin SVM
A. Quadir, M. Tanveer
Multiview learning (MVL) seeks to leverage the benefits of diverse perspectives to complement each other, effectively extracting and utilizing the latent information within the dat…