38 citations · 89 across the 28 of their papers we have counts for
12 papers · 1 filter
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,…
GL-TSVM: A robust and smooth twin support vector machine with guardian loss function
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Twin support vector machine (TSVM), a variant of support vector machine (SVM), has garnered significant attention due to its times lower computational complexity compared to…
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…
Wave-RVFL: A Randomized Neural Network Based on Wave Loss Function
M. Sajid, A. Quadir, M. Tanveer
The random vector functional link (RVFL) network is well-regarded for its strong generalization capabilities in the field of machine learning. However, its inherent dependencies on…
Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficien…
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function
Mushir Akhtar, M. Tanveer, Mohd. Arshad
Support vector regression (SVR) has garnered significant popularity over the past two decades owing to its wide range of applications across various fields. Despite its versatility…