Data-Centric Artificial Intelligence for Multidisciplinary Applications

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
List of Contributors
Section I: Recent Developments in Data-Centric AI
	Chapter 1 Advancements in Data-Centric AI Foundations, Ethics, and Emerging Technology
	Chapter 2 Emerging Development and Challenges in Data-Centric AI
	Chapter 3 Unleashing the Power of Industry 4.0: A Harmonious Blend of Data-Centric and Model-Centric AI
	Chapter 4 Data-Centric AI Approaches for Machine Translation
Section II: Data-Centric AI in Healthcare and Agriculture
	Chapter 5 Case Study Medical Images Analysis and Classification with Data-Centric Approach
	Chapter 6 Comparative Analysis of Machine Learning Classification Techniques for Kidney Disease Prediction
	Chapter 7 Fusion of Multi-Modal Lumber Spine Scans Using Convolutional Neural Networks
	Chapter 8 Medical Image Analysis and Classification for Varicose Veins
	Chapter 9 Brain Tumor Detection Using CNN
	Chapter 10 Explainable Artificial Intelligence in the Healthcare: An Era of Commercialization for AI Solutions
	Chapter 11 Role of Data-Centric Artificial Intelligence in Agriculture
	Chapter 12 Detection and Classification of Mango Fruit-Based on Feature Extraction Applying Optimized Hybrid LA-FF Algorithms
Section III: Building AI with Quality Data for Multidisciplinary Domains
	Chapter 13 Guiding Your Way: Solving Student Admission Woes
	Chapter 14 Melodic Pattern Recognition for Ornamentation Features in Music Computing
	Chapter 15 Content Analysis Framework for Skill Assessment
	Chapter 16 Machine-Learning Techniques for Effective Text Mining
	Chapter 17 Emails Classification and Anomaly Detection using Natural Language Processing
Index