7 papers · 1 filter
Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
Vrushank Ahire, Yogesh Kumar, M. A. Ganaie
Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving ge…
Graph-Guided Universum Learning in Generalized Eigenvalue Proximal SVMs for Alzheimer's Disease Classification
Yogesh Kumar, Vrushank Ahire, Mudasir Ganaie
Early and accurate detection of Alzheimer's disease (AD) is important for timely intervention and disease management. Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM…
MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening
Vrushank Ahire, Yogesh Kumar, Anouck Girard +1
Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) precedes dementia. Structural MRI provides biomarkers but requires costly i…
A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine
Yogesh Kumar, Vrushank Ahire, M. A. Ganaie
The paper presents novel Universum-enhanced classifiers: the Universum Generalized Eigenvalue Proximal Support Vector Machine (U-GEPSVM) and the Improved U-GEPSVM (IU-GEPSVM) for E…
MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network
Vrushank Ahire, Kunal Shah, Mudasir Nazir Khan +4
Dynamic emotion recognition in the wild remains challenging due to the transient nature of emotional expressions and temporal misalignment of multi-modal cues. Traditional approach…
Granular Ball K-Class Twin Support Vector Classifier
M. A. Ganaie, Vrushank Ahire, Anouck Girard
This paper introduces the Granular Ball K-Class Twin Support Vector Classifier (GB-TWKSVC), a novel multi-class classification framework that combines Twin Support Vector Machines…
Granular Ball Twin Support Vector Machine with Universum Data
M. A. Ganaie, Vrushank Ahire
Classification with support vector machines (SVM) often suffers from limited performance when relying solely on labeled data from target classes and is sensitive to noise and outli…