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Full Description
Cognitive and Meta Learning Strategies in Biomedical Research and Healthcare examines the dynamic intersection of cognitive science and meta-learning within the realm of biomedical research. It addresses how to overcome the complexities of contemporary health challenges by harnessing the power of advanced learning methodologies, such as cognitive processes and meta learning.
Contents
1. Smartphone-based Human Activity Recognition for Healthcare Service with Meta Learning
2. Cognitive Meta Learning-Based AI Models for Improved Detection of Neuropathology
3. Revolutionising Brain Tumour Detection: Integrating AI and Machine Learning for Enhanced Diagnostic Accuracy and Healthcare Efficiency
4. Integrating meta-learning into Biomedical Diagnostics
5. Meta Reinforcement Learning in Health Informatics: A Meta Reinforcement Learning Framework for Blood Glucose Level Control in Type 1 Diabetes
6. Cognitive Meta-Learning Techniques for Uncovering Hidden Patterns in protein Information: A Gender-Based Analysis of Undergraduate Biochemistry Students in Pakistan
7. Hip Exoskeleton Controller Design: A Comprehensive Review for People with Leg Deformities
8. Explainable AI for Epileptic Neonatal EEG Classification
9. An AI-enabled Meta Learning Approach towards Prediction of Cardiological Disorders in Healthcare Sector
10. Cognitive Meta Learning-based AI models for Multimodal Signals
11. Cognitive Meta-Learning Techniques for Uncovering Hidden Patterns in Biomedical Information
12. A Cognitive Learning Approach for Severity Classification of Diabetic Retinopathy Using Voting Based Selection of Deep Models
13. Challenges and Mitigating Strategies for AI based Meta Learning with Multimodal signals
14. Revolutionizing Healthcare with the Cognitive Internet of Medical Things: AI-Driven Connectivity and Smart Systems for Personalized Care
