38 citations · 89 across the 28 of their papers we have counts for
31 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…
IFW-BLS: Dual-Robust Broad Learning System with Intuitionistic Fuzzy Wave Loss
Mushir Akhtar, M. Tanveer
Broad Learning System is an efficient randomized learning model that expands network width through feature and enhancement nodes and estimates the output weights without deep backp…
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…
Do Existing Preconditioners Improve Biomedical Tabular Foundation Learning? An Empirical Study on TabPFN Optimization
M. Sajid, Pinki Khatun, M. Tanveer
Tabular foundation models have recently shown strong potential for structured biomedical data analysis. Among them, TabPFN has emerged as an effective approach for low-data tabular…
Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift
Mushir Akhtar, M. Tanveer
Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class. The affected c…
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…
AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
M. Sajid, Deepanshu Gupta, Yash Modi +7
In this paper, we propose AUREXA-SE (Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement), a progressive bimodal…
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…
CI-RKM: A Class-Informed Approach to Robust Restricted Kernel Machines
Ritik Mishra, Mushir Akhtar, M. Tanveer
Restricted kernel machines (RKMs) represent a versatile and powerful framework within the kernel machine family, leveraging conjugate feature duality to address a wide range of mac…
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…
LSTMSE-Net: Long Short Term Speech Enhancement Network for Audio-visual Speech Enhancement
Arnav Jain, Jasmer Singh Sanjotra, Harshvardhan Choudhary +6
In this paper, we propose long short term memory speech enhancement network (LSTMSE-Net), an audio-visual speech enhancement (AVSE) method. This innovative method leverages the com…
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,…