Shiguo Lian, Zhaoxiang Liu

Artificial Intelligence Paradigms for Application Practice (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for real-world implementation.

Over the past decade, deep neural networks have made significant progress. However, effectively applying these networks to solve various practical problems remains challenging, which has limited the widespread application of artificial intelligence. Artificial Intelligence Paradigms for Application Practice is the first to comprehensively address implementation paradigms for deep neural networks in practice. The authors begin by reviewing the development of artificial neural networks and provide a systematic introduction to the tasks, principles, and architectures of deep neural networks. They identify the practical limitations of deep neural networks and propose guidelines and strategies for successful implementation. The book then examines 14 representative applications in urban planning, industrial production, and transportation. For each case, the authors present a landing paradigm that effectively addresses practical challenges supported by illustrations, background information, related work, methods, experiments, and conclusions. The experimental results validate the effectiveness of the proposed implementation approaches.

The book will benefit researchers, engineers, undergraduate, and graduate students interested in artificial intelligence, deep neural networks, large models, stable diffusion models, video surveillance, smart cities, intelligent manufacturing, intelligent transportation, and other related areas.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 205
• Erscheinungstermin: 15. August 2025
• Englisch
• ISBN-13: 9781040395783
• Artikelnr.: 74414949

Autorenporträt

Shiguo Lian received his Ph.D. from Nanjing University of Science and Technology, China. He currently serves as Chief Scientist at the Data Science & Artificial Intelligence Research Institute and Chief Engineer of AI Technology, China Unicom. He is a member of the IEEE Multimedia Communications and Computational Intelligence Technical Committees. His research focuses on visual recognition, multimodal large models, robotics, and multimodal interactions.

Zhaoxiang Liu received his Ph.D. from the College of Information and Electrical Engineering at China Agricultural University, China. He currently serves as Director of AI Research at the Data Science & Artificial Intelligence Research Institute, China Unicom. His research interests include artificial intelligence, large language models, multimodal large models, deep learning, computer vision, and embodied AI.

Inhaltsangabe

1. Introduction to Artificial Intelligence for Practical Application 2.
Fine-grained Dataset Design Paradigm for Mask-Wearing Recognition 3.
Hand-Held Action Detection Paradigm for Smoking Detection 4. Human Action
Recognition Paradigm for Person Safety Supervision in Various Practical
Scenarios 5. Person Counting Paradigm for Intelligent Video Surveillance in
Various Practical Scenarios 6. Operation Procedure Detection Paradigm for
Noncompliant Operations Detection of Oil Unloading 7. Object Measurement
Paradigm for Length Measurement of Iron Chains 8. Quality Estimation
Paradigm for Copper Scrap Granules Recycling 9. Human-in-the-Loop Learning
Paradigm for Fabric Anomaly Detection 10. Supervised Learning Paradigm for
Edible Oil Anomaly Detection 11. Unsupervised Learning Paradigm for
Industrial Visual Anomaly Detection 12. Object Identity Recognition
Paradigm for Fishing Boat Recognition 13. Image Editing Paradigm for
Clothing Fashion Customization 14. Retrieval-Augmented Generation Paradigm
for Professional Knowledge Acquisition Applications 15. Multimodal
Generation Paradigm for Visualizing Historical Artifacts