Kamal Poon
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Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Kamal Poon's research employs deep learning algorithms for health-related predictions and monitoring. His work includes the development of deep learning networks for detecting and predicting hypoglycemia in premature infants, utilizing dual smart sensor data. Additionally, he has investigated multi-sensor monitoring for paralyzed individuals, incorporating optimization algorithms and deep neural networks. Poon has also explored deep learning approaches for predicting epileptic seizures using the Cramer distance metric. His research has extended into educational technology, focusing on edge computing with deep learning and the Internet of Things to recognize and predict student emotions and mental health.
Metrics
- h-index: 2
- Publications: 4
- Citations: 9
Selected Publications
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection (2025)
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Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network (2025)
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Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health (2024)
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Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction (2024)
Collaboration Network
Top Collaborators
- Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health
- Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction
- Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection
- Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
- Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection
- Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
- Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection
- Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
- Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection
- Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
- Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction
- Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction
- Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction
- Cramer Distance: A Deep Learning Approach for Better Epileptic Seizure Prediction
- Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health
- Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health
- Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health
- Edge Computing with Deep Learning and Internet of Things for Recognising and Predicting Students Emotions and Mental Health
- Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
- Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
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