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Full Description
Artificial intelligence (AI) is impacting industries worldwide by introducing new methods and altering traditional practices. This book examines AI's diverse effects, providing insights into its applications, challenges, and future prospects across education, healthcare, finance, and more.
The chapters explore how AI technologies, such as large language models, enhance feedback in higher education and influence legal studies while upholding academic integrity. A review of key technical approaches—knowledge-based systems, machine learning, and intelligent optimization—lays the groundwork for understanding AI's potential. Real-world examples illustrate AI's role in medical imaging, presenting new diagnostic methods and the use of language models for image interpretation. The book also discusses financial applications, including techniques for credit card fraud detection and forecasting natural gas prices using innovative models. Additionally, it covers personalized federated learning models, highlighting the importance of data privacy and security in AI's evolution.
This comprehensive guide is valuable for educators, researchers, practitioners, and students interested in AI's current and future developments. By combining theory with practical examples, the book offers readers a clear understanding of how AI affects various sectors, enabling them to engage effectively with this rapidly evolving field.
Contents
1 Utilising Large Language Models for Feedback Generation in Higher Education: Insights from Student Perceptions 2 Balancing Innovation and Integrity: The Integration and Implications of Generative Artificial Intelligence in Legal Education 3 Three Technical Routes of AI 4 Multi-Criteria Decision-Making for Operating ATM Systems 5 FFP: A Robust, Interpretable, and Lightweight Framework for Medical Image Diagnosis 6 Grey Prediction Model Based on Fixed-Point Accumulation and Its Application to Predict Natural Gas Futures Contract Price 7 Large Language Models in Medical Image Understanding 8 Enhancing Credit Card Fraud Detection with Spatial-Temporal Analysis and Balanced Learning Approaches
