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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
List of contents
I) Section I Recent developments in data-centric AI: 1. Advancements in Data-Centric AI Foundations, Ethics, and Emerging Technology 2. Emerging Development and Challenges in Data-Centric AI 3. Unleashing the Power of Industry 4.0: A Harmonious Blend of Data-Centric and Model- Centric AI 4. Data centric AI approaches for machine translation II) Section II Data Centric AI in Healthcare and Agriculture: 5. Case Study Medical Images Analysis and Classification with Data Centric Approach 6. Comparative Analysis of Machine Learning Classification Techniques for Kidney Disease Prediction 7. Fusion of Multi Modal Lumber Spine Scans Using Convolutional Neural Networks 8. Medical Image Analysis and Classification for Varicose Veins 9. Brain Tumor Detection using CNN 10. Explainable Artificial Intelligence in the Healthcare: An Era of Commercialization for AI Solutions 11. Role of Data centric artificial intelligence in agriculture 12. Detection and Classification of Mango Fruit based on Feature extraction applying optimized hybrid LA-FF algorithms III) Section III Building AI with quality Data for multidisciplinary domains: 13 Guiding Your Way: Solving Student Admission Woes 14. Melodic pattern recognition for ornamentation features in music computing 15. Content Analysis Framework for Skill Assessment 16. Machine learning techniques for effective text mining 17. Emails Classification and Anomaly Detection using Natural Language Processing
