Dr.ijaz ahmad
Shenzhen University, Shenzhen Institutes of Advanced Technology
Research areas: Computer Science · Artificial Intelligence · EEG and Brain-Computer Interfaces · Emotion and Mood Recognition · ECG Monitoring and Analysis
0000-0003-3974-2207
About
Ijaz Ahmad is a highly motivated researcher and experienced AI scientist with a strong background in biomedical signal and image processing with advanced machine learning and deep learning in healthcare technologies. He received his PhD in Pattern Recognition and Intelligent Systems from the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), where he developed advanced AI pipelines for EEG-based epileptic seizure detection and prediction. His research also included deep learning, multi-modal data fusion, IoMT, and explainable AI (XAI), with a focus on clinical applications and real-world deployment. He has published extensively in top-tier journals, presented at global conferences, and contributed to several high-impact research projects funded by the National Key R&D Program of China and the Shenzhen Government. Ijaz is passionate about precision medicine, digital health innovation, and advancing AI for healthcare systems.
Employment
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Shenzhen university Reasercher2025 - 2026
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Shenzhen Institutes of Advanced Technology Reaserch associate2025 - 2025
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Shenzhen University Reasercher
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (41)
- Correction: Khan et al. Smart Android Based Home Automation System Using Internet of Things (IoT). Sustainability 2022, 14, 10717 Save
- DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization Save
- Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions Save
- Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science Save
- An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection Save
- A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG signals Save
- A Secure and Interpretable AI for Smart Healthcare System: A Case Study on Epilepsy Diagnosis Using EEG Signals Save
- Adaptive Stepwise Feature Selection Approach for EEG-Based Epileptic Seizure Classification Save
- An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection Save
- An Intelligent and Interpretable Intrusion Detection System for Unmanned Aerial Vehicles Save
- Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding Save
- Feature Fusion of Gramian Angular Field Deep Learning EEG-Based Epileptic Seizure Classification Save
- Multi-Feature Fusion-Based Convolutional Neural Networks for EEG Epileptic Seizure Prediction in Consumer Internet of Things Save
- Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach Save
- A Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis Save
- An Intelligent Intrusion Detection System for Smart Consumer Electronics Network Save
- Correction: An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms(Biology, (2022), 11, (1125), 10.3390/biology11081125) Save
- DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs Save
- Experimental Analysis to Detect Corona COVID-19 Virus Symptoms in Male Patients through Breath Pattern Using Machine Learning Algorithms Save
- Latency Localization of Auditory Brainstem Response by Different Deep Learning Methods Save