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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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 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.
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
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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