Cognitive and Meta Learning Strategies in Biomedical Research and Healthcare

1. Smartphone-based human activity recognition for healthcare service with meta learning

1.1 Introduction

1.2 Related work

1.3 Methodology

1.4 Experiment

1.5 Discussion

1.6 Future research directions

1.7 Conclusion
Acknowledgment
References

2. Cognitive metalearning-based artificial intelligence models for improved detection of neuropathology

2.1 Introduction

2.2 Key concept

2.3 Current achievements in cognitive metalearning

2.4 Role of metalearning in neuropathology

2.5 Challenges in implementing metalearning

2.6 Future horizons: toward improved neuropathology detection

2.7 Conclusion
AI disclosure
References

3. Revolutionising Brain Tumour Detection: Integrating AI and Machine Learning for Enhanced Diagnostic Accuracy and Healthcare Efficiency

3.1 Introduction

3.2 Effect on patient care and recovery

3.3 Emergence of AI and ML in brain tumour detection

3.4 Implication of AI-based detection for fast and accurate diagnosis

3.5 Methodology

3.6 Results

3.7 Conclusions
References

4. Integrating metalearning into biomedical diagnostics

4.1 Introduction

4.2 Understanding metalearning

4.3 Challenges in biomedical diagnostics

4.4 Applications of metalearning in biomedical diagnostics

4.5 Integration into healthcare systems

4.6 Future directions

4.7 Conclusion
References

5. Metareinforcement learning in health informatics: a metareinforcement learning framework for blood glucose level control in Type 1 diabetes

5.1 Introduction

5.2 Related work

5.3 Proposed methodology

5.4 Results

5.5 Conclusions and future work
Declarations
Funding
Conflicts of interest
References

6. Cognitive metalearning techniques for uncovering hidden patterns in protein information: a gender-based analysis of undergraduate biochemistry students in Pakistan

6.1 Introduction

6.2 Methodology

6.3 Results and discussion

6.4 Conclusions
References

7. Hip exoskeleton controller design: a comprehensive review for people with leg deformities

7.1 Introduction

7.2 Literature review

7.3 Methodology

7.4 Result and discussion

7.5 Conclusion
Acknowledgment
References

8. Explainable artificial intelligence for epileptic neonatal electroencephalography classification

8.1 Introduction

8.2 Dataset

8.3 Preprocessing of neonatal electroencephalography

8.4 Feature extraction

8.5 Deep neuro‑fuzzy system

8.6 Explainable artificial intelligence

8.7 Performance metrics

8.8 Results and discussion

8.9 Conclusions

8.10 Future work
AI disclosure
References

9. An artificial intelligence–enabled meta-learning approach toward prediction of cardiological disorders in healthcare sector

9.1 Introduction

9.2 Related work

9.3 Proposed method

9.4 Result analysis

9.5 Conclusion
References

10. Cognitive Meta-Learning-Based AI Models for Multimodal Signals

10.1 Introduction

10.2 Literature review

10.3 Brief review of machine learning

10.4 Cognitive computing–based deep learning in biomedical data analysis

10.5 Future work

10.6 Conclusion
References

11. Cognitive meta-learning techniques for uncovering hidden patterns in biomedical information

11.1 Introduction

11.2 Cognitive science and meta-learning: an overview

11.3 Cognitive meta-learning techniques

11.4 Applications in biomedical research

11.5 Challenges and future directions

11.6 Conclusion
References

12. A cognitive learning approach for severity classification of diabetic retinopathy using voting-based selection of deep models

12.1 Introduction

12.2 Literature review

12.3 Methodology

12.4 Dataset

12.5 Data preprocessing

12.6 DarkNet53

12.7 ResNet18

12.8 EfficientNetB0

12.9 Transfer learning

12.10 Feature extraction

12.11 Results and discussion

12.12 Classification results for ResNet18

12.13 Classification results for EfficientNetB0

12.14 Classification results after feature fusion

12.15 Conclusion
Conflict of interest
References

13. Challenges and mitigating strategies for artificial intelligence–based meta-learning with multimodal signals

13.1 Introduction to meta-learning

13.2 Approaches used in meta-learning

13.3 Multimodal learning

13.4 Challenges in multimodal meta-learning and solutions

13.5 Dataset management in meta-learning

13.6 Data distribution across tasks for meta-learning

13.7 Data‑driven multimodal fusion

13.8 Examples and test cases

13.9 Conclusions and future scope
References

14. Revolutionizing healthcare with the cognitive internet of medical things: artificial intelligence–driven connectivity and smart systems for personalized care

14.1 Introduction to the Cognitive Internet of Medical Things

14.2 Key technologies driving Cognitive Internet of Medical Things

14.3 Advanced technologies

14.4 Cognitive computing in Cognitive Internet of Medical Things: simulating human-like reasoning

14.5 Applications: remote monitoring, telemedicine, chronic disease management

14.6 Challenges: privacy, security, interoperability

14.7 Cost and investment considerations

14.8 Conclusion and future work
References