Cognitive and Meta Learning Strategies in Biomedical Research and Healthcare

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.

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1. Smartphone-based human activity recognition for healthcare service with meta learning
2. Cognitive metalearning-based artificial intelligence 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 metalearning into biomedical diagnostics
5. Metareinforcement learning in health informatics: a metareinforcement learning framework for blood glucose level control in Type 1 diabetes
6. Cognitive metalearning 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 artificial intelligence for epileptic neonatal electroencephalography classification
9. An artificial intelligence-enabled meta-learning approach toward 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 artificial intelligence-based meta-learning with multimodal signals
14. Revolutionizing healthcare with the cognitive internet of medical things: artificial intelligence-driven connectivity and smart systems for personalized care

Authors

Chinmay Chakraborty Associate Professor and Head, Centre of Innovation & Research (COIR) in Medical Technology, KIIT Deemed to be University, India. Chinmay Chakraborty is an Associate Professor and Head, Centre of Innovation & Research (COIR) in Medical Technology, KIIT Deemed to be University, India. His main research interests include the Internet of Medical Things, Medical technology, m-Health/e-health, and AI-ML. He is an Editorial Board Member of various different journals and conferences. Subhendu Kumar Pani Department of Computer Science and Engineering, Krupajal Engineering College, Bhubaneswar, Odisha, India. Subhendu Kumar Pani received his Ph.D. from Utkal University Odisha, India. He has more than 16 years of teaching and research experience. His research interests include data mining, big data analysis, web data analytics, fuzzy decision making and computational intelligence. He is a fellow in SSARSC and life member in IE, ISTE, ISCA, OBA.OMS, SMIACSIT, SMUACEE, CSI. Sayonara Barbosa Professor, University of Cincinnati, USA. Dr. Sayonara F. F. Barbosa is a Professor at the University of Cincinnati, USA. Professor Barbosa is a member of the Editorial Board of the International Journal of Medica Informatics and the Journal of Nursing Scholarship. From 2016 to 2020, at the International Medical Informatics Association, she was Vice-Chair of Nursing Informatics Special Interest Group, Brazil Representative. Her experience includes nursing in intensive care and information technology in healthcare, health information technology, healthcare technology, patient safety and donation of organs and transplants

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