
Data-Centric Artificial Intelligence for Multidisciplinary Applications
- 308 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Data-Centric Artificial Intelligence for Multidisciplinary Applications
About this book
This book explores the need for a data?centric AI approach and its application in the multidisciplinary domain, compared to a model?centric approach. It examines the methodologies for data?centric approaches, the use of data?centric approaches in different domains, the need for edge AI and how it differs from cloud?based AI. It discusses the new category of AI technology, "data?centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data?centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods.
• Includes a collection of case studies with experimentation results to adhere to the practical approaches
• Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways
• Discusses methodologies to achieve accurate results by improving the quality of data
• Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Table of Contents
- Editors
- List of Contributors
- Section I Recent Developments in Data-Centric AI
- Section II Data-Centric AI in Healthcare and Agriculture
- Section III Building AI with Quality Data for Multidisciplinary Domains
- Index