Bahria University

ENGR. DR. SYED MUHAMMAD USMAN

PERSONAL INFORMATION
Email	smusman.h11@bahria.edu.pk
Phone Ext.	3562
Research Areas / Expertise	Biomedical Signal Processing, Medical Imaging, Precision Agriculture

Publications

Journals & Conferences

• A Deep Learning based Ensemble Learning Method for Epileptic Seizure Prediction | Computers in Biology and Medicine
• Epileptic seizure prediction using scalp electroencephalogram signals. | Biocybernetics and Biomedical Engineering
• A Hybrid Approach of Vision Transformers and CNNs for Detection of Ulcerative Colitis | Scientific Reports
• Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images | Scientific Reports
• Advancing Emotional Health Assessments: A Hybrid Deep Learning Approach Using Physiological Signals for Robust Emotion Recognition | IEEE Access
• Automated Lesion Detection in Cotton leaf visuals using deep learning | PeerJ Computer Science
• Detection of preictal state in epileptic seizures using ensemble classifier | Epilepsy Research
• Epileptic seizures prediction using deep learning techniques. | IEEE Access
• Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies | Seizure
• Model Agnostic Meta-Learning (MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks | Computers Materials Continua
• Classification of EEG Signals for Prediction of Epileptic Seizures | Applied Sciences
• Principle components analysis for seizures prediction using wavelet transform | International journal of ADVANCED AND APPLIED SCIENCES
• An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications | Sensors
• An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals. | Sensors
• Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment | Electronics
• Malware Detection in Internet of Things (IoT) Devices Using Deep Learning | Sensors
• Epileptic Seizures Prediction Using Machine Learning Methods | Computational and Mathematical Methods in Medicine
• A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks | Wireless Communications and Mobile Computing
• Multimodal consumer choice prediction using EEG signals and eye tracking | Frontiers in Computational Neuroscience
• Unlocking the potential of EEG in Alzheimer’s disease research: Current status and pathways to precision detection | Brain Research Bulletin
• Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features | Computerized Medical Imaging and Graphics
• Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm | PLOS One
• Fusing Geometric and Temporal Deep Features for High-Precision Arabic Sign Language Recognition | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
• Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals | Frontiers in Medicine
• ADNET: A 1D-CNN Feature Fusion-based Method for Alzheimer’s Disease Detection Using EEG Signals | Journal of Disability Research
• Efficient Wheat Disease Identification Using Hybrid Swin-SHARP Vision Model | IEEE Access
• EgoVision a YOLO-ViT hybrid for robust egocentric object recognition | Scientific Reports
• Edge-Optimized CNNs: A Co-Designed Software-Hardware Framework for Lightweight Deep Learning | IEEE access
• Quantum Genetic Algorithm Based Ensemble Learning for Detection of Atrial Fibrillation Using ECG Signals | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
• NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer’s Disease detection | Scientific Reports