Data-Centric Artificial Intelligence for Multidisciplinary Applications
eBook - ePub

Data-Centric Artificial Intelligence for Multidisciplinary Applications

  1. 308 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

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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Yes, you can access Data-Centric Artificial Intelligence for Multidisciplinary Applications by Parikshit N Mahalle,Namrata Nishant Wasatkar,Gitanjali R. Shinde in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Table of Contents
  6. Editors
  7. List of Contributors
  8. Section I Recent Developments in Data-Centric AI
  9. Section II Data-Centric AI in Healthcare and Agriculture
  10. Section III Building AI with Quality Data for Multidisciplinary Domains
  11. Index