Bøger i Data-Intensive Research serien

The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.

This book discusses the best research roadmaps, strategies, and challenges in data-centric approach of artificial intelligence (AI) in various domains. It presents comparative studies of model-centric and data-centric AI. It also highlights different phases in data-centric approach and data-centric principles. The book presents prominent use cases of data-centric AI. It serves as a reference guide for researchers and practitioners in academia and industry.

The book is a comprehensive guide that explores the concept of data economy and its implications in today's world. The book discusses the principles and components of the ecosystem, the challenges and opportunities presented by data monetization, and the potential risks related to data privacy. Real-life examples and case studies are included to understand the concepts better. The book is suitable for individuals in data science, economics, business, and technology and for students, academics, and policymakers. It is an excellent read for anyone interested in the data economy.