Data Centric Artificial Intelligence For Multidisciplinary Applications
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Data-Centric Artificial Intelligence for Multidisciplinary Applications
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
Data Centric Artificial Intelligence: A Beginner’s Guide
Author: Parikshit N. Mahalle
language: en
Publisher: Springer Nature
Release Date: 2023-10-10
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.
Blockchain and Digital Twin Applications in Smart Agriculture
Blockchain and Digital Twin Applications in Smart Agriculture presents scenarios, solutions, applications to optimize crop management, improve resource allocation, and enhance sustainability in modern farming practices. It examines how blockchain and digital twin technology could enable farmers and stakeholders to create virtual, data-driven replicas of their agricultural operations while leveraging the security and transparency of blockchain technology. Highlights of the book include: An immutable blockchain ledger for storing and sharing agricultural data securely Decentralized data management to share data among such stakeholders as farmers, suppliers, distributors, and consumers Supply chain transparency to ensure product authenticity, reduce food fraud, and protect consumers Solutions to improve the allocation of such resources as water, fertilizer, and labor to reduce costs and improve productivity. The book helps researchers and academics develop solutions that can transition the agricultural industry into a more data-driven, sustainable, and transparent future. It explains how creating digital twins of agricultural operations and securing data through blockchain can enhance the ability to make informed decisions, reduce waste, and ensure that agriculture practices are more efficient and environmentally responsible. Featuring practical applications and case studies, the book provides a roadmap to farming that is smart, data-driven, and more.