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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Preface - p. xvii 8. Chapter One. Smartphone-based human activity recognition for healthcare service with meta learning - p. 1 9. Chapter Two. Cognitive metalearning-based artificial intelligence models for improved detection of neuropathology - p. 29 10. Chapter Three. Revolutionising Brain Tumour Detection: Integrating AI and Machine Learning for Enhanced Diagnostic Accuracy and Healthcare Efficiency - p. 63 11. Chapter Four. Integrating metalearning into biomedical diagnostics - p. 89 12. Chapter Five. Metareinforcement learning in health informatics: a metareinforcement learning framework for blood glucose level control in Type 1 diabetes - p. 131 13. Chapter Six. Cognitive metalearning techniques for uncovering hidden patterns in protein information: a gender-based analysis of undergraduate biochemistry students in Pakistan - p. 159 14. Chapter Seven. Hip exoskeleton controller design: a comprehensive review for people with leg deformities - p. 181 15. Chapter Eight. Explainable artificial intelligence for epileptic neonatal electroencephalography classification - p. 213 16. Chapter Nine. An artificial intelligence–enabled meta-learning approach toward prediction of cardiological disorders in healthcare sector - p. 243 17. Chapter Ten. Cognitive Meta-Learning-Based AI Models for Multimodal Signals - p. 257 18. Chapter Eleven. Cognitive meta-learning techniques for uncovering hidden patterns in biomedical information - p. 283 19. Chapter Twelve. A cognitive learning approach for severity classification of diabetic retinopathy using voting-based selection of deep models - p. 311 20. Chapter Thirteen. Challenges and mitigating strategies for artificial intelligence–based meta-learning with multimodal signals - p. 345 21. Chapter Fourteen. Revolutionizing healthcare with the cognitive internet of medical things: artificial intelligence–driven connectivity and smart systems for personalized care - p. 381 22. Index - p. 413
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. Preface - p. xvii 8. Chapter One. Smartphone-based human activity recognition for healthcare service with meta learning - p. 1 9. Chapter Two. Cognitive metalearning-based artificial intelligence models for improved detection of neuropathology - p. 29 10. Chapter Three. Revolutionising Brain Tumour Detection: Integrating AI and Machine Learning for Enhanced Diagnostic Accuracy and Healthcare Efficiency - p. 63 11. Chapter Four. Integrating metalearning into biomedical diagnostics - p. 89 12. Chapter Five. Metareinforcement learning in health informatics: a metareinforcement learning framework for blood glucose level control in Type 1 diabetes - p. 131 13. Chapter Six. Cognitive metalearning techniques for uncovering hidden patterns in protein information: a gender-based analysis of undergraduate biochemistry students in Pakistan - p. 159 14. Chapter Seven. Hip exoskeleton controller design: a comprehensive review for people with leg deformities - p. 181 15. Chapter Eight. Explainable artificial intelligence for epileptic neonatal electroencephalography classification - p. 213 16. Chapter Nine. An artificial intelligence–enabled meta-learning approach toward prediction of cardiological disorders in healthcare sector - p. 243 17. Chapter Ten. Cognitive Meta-Learning-Based AI Models for Multimodal Signals - p. 257 18. Chapter Eleven. Cognitive meta-learning techniques for uncovering hidden patterns in biomedical information - p. 283 19. Chapter Twelve. A cognitive learning approach for severity classification of diabetic retinopathy using voting-based selection of deep models - p. 311 20. Chapter Thirteen. Challenges and mitigating strategies for artificial intelligence–based meta-learning with multimodal signals - p. 345 21. Chapter Fourteen. Revolutionizing healthcare with the cognitive internet of medical things: artificial intelligence–driven connectivity and smart systems for personalized care - p. 381 22. Index - p. 413
