Area of research
Artificial Intelligence · Cognitive Neuroscience
Research interest
Research interests include Computer science, Artificial intelligence, Electroencephalography, Pattern recognition (psychology), Deep learning, and Convolutional neural network.
Unlocking the potential of EEG in Alzheimer's disease research: Current status and pathways to precision detection
Multimodal consumer choice prediction using EEG signals and eye tracking
NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer’s Disease detection
EgoVision a YOLO-ViT hybrid for robust egocentric object recognition
ADNET: A 1D-CNN Feature Fusion-based Method for Alzheimer’s Disease Detection Using EEG Signals
Model Agnostic Meta-Learning (MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks
A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis
Automated lesion detection in cotton leaf visuals using deep learning
Classification of EEG Signals for Prediction of Epileptic Seizures
An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications
Wildfire detection in aerial images using deep learning
An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals
Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment
Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach
Image and command hybrid model for vehicle control using Internet of Vehicles
A generic methodology for geo‐related data semantic annotation