LNAI 2926 Adding AI to Web Services 1st Edition by Charles Petrie, Michael Genesereth, Hans Bjornsson, Rada Chirkova, Martin Ekstrom, Hidehito Gomi, Tim Hinrichs, Rob Hoskins, Michael Kassoff, Daishi Kato, Kyohei Kawazoe, Jung Ung Min, Waqar Mohsin ISBN 9783540206460 354020646X

LNAI 2926 Adding AI to Web Services 1st Edition by Charles Petrie, Michael Genesereth, Hans Bjornsson, Rada Chirkova, Martin Ekstrom, Hidehito Gomi, Tim Hinrichs, Rob Hoskins, Michael Kassoff, Daishi Kato, Kyohei Kawazoe, Jung Ung Min, Waqar Mohsin – Ebook PDF Instant Download/Delivery. 9783540206460 ,354020646X
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Product details:
ISBN 10: 354020646X
ISBN 13: 9783540206460
Author: Charles Petrie, Michael Genesereth, Hans Bjornsson, Rada Chirkova, Martin Ekstrom, Hidehito Gomi, Tim Hinrichs, Rob Hoskins, Michael Kassoff, Daishi Kato, Kyohei Kawazoe, Jung Ung Min, Waqar Mohsin

The FX-Project consisted of members of the Stanford Logic Group industrial visitors from NEC and Intec Web & Genome working together to develop a new technologies based upon the combination of web services and techniques from artificial intelligence using our experience in AI-based software agents. This two-year project ran from April of 2001 until the end of March 2002 and explored the then emerging functionality of web services. This paper is a result of our findings.

LNAI 2926 Adding AI to Web Services 1st Edition Table of contents:

Chapter 1: Introduction to Web Services and AI

• 1.1. What are Web Services?
• 1.2. The Rise of Artificial Intelligence in Web Development
• 1.3. The Need for Intelligence in Web Services
• 1.4. Key Challenges and Opportunities in Combining AI and Web Services
• 1.5. Overview of the Book Structure

Chapter 2: Foundations of Web Services

• 2.1. Web Service Architecture: SOAP, REST, and GraphQL
• 2.2. Web Service Protocols and Standards
• 2.3. Service Discovery and Service Composition
• 2.4. Security and Authentication in Web Services
• 2.5. Performance Considerations in Web Services

Chapter 3: Introduction to AI Technologies

• 3.1. Overview of AI Concepts: Machine Learning, Natural Language Processing, Computer Vision
• 3.2. Core Techniques in AI: Supervised vs. Unsupervised Learning
• 3.3. Deep Learning and Neural Networks
• 3.4. Reinforcement Learning and Its Applications in Web Services
• 3.5. Explainable AI: Why It Matters for Web Services

Chapter 4: Integrating AI with Web Service Architecture

• 4.1. Architecture of AI-Enabled Web Services
• 4.2. Data Pipelines for AI in Web Services
• 4.3. Using AI for Dynamic Web Service Discovery and Composition
• 4.4. Leveraging Cloud and Edge Computing for AI-Enhanced Web Services
• 4.5. Case Study: Building an AI-Enabled Service-Oriented Architecture

Chapter 5: Enhancing Web Services with Natural Language Processing (NLP)

• 5.1. Understanding the Role of NLP in Web Services
• 5.2. NLP for Intelligent Chatbots and Virtual Assistants
• 5.3. Using NLP for Information Retrieval and Document Summarization
• 5.4. Sentiment Analysis for Customer Feedback
• 5.5. Case Study: Adding NLP to Web Services for Customer Support

Chapter 6: AI-Powered Personalization in Web Services

• 6.1. The Importance of Personalization in Modern Web Applications
• 6.2. Collaborative Filtering and Recommender Systems
• 6.3. Personalized Content Delivery Using AI
• 6.4. User Behavior Prediction and Targeted Marketing
• 6.5. Case Study: Personalizing E-Commerce with AI Web Services

Chapter 7: Enhancing Web Services with Computer Vision

• 7.1. The Role of Computer Vision in AI-Driven Web Services
• 7.2. Image and Video Analysis for Web Applications
• 7.3. Real-Time Object Detection and Recognition
• 7.4. Integrating AI Vision into Web Services for Enhanced User Experience
• 7.5. Case Study: AI-Based Image Recognition in Web-Based Health Services

Chapter 8: AI for Automation and Workflow Optimization in Web Services

• 8.1. Automating Tasks with AI-Enabled Web Services
• 8.2. Intelligent Task Scheduling and Load Balancing
• 8.3. AI-Powered Monitoring and Predictive Maintenance
• 8.4. Improving User Experience through AI-Driven Automation
• 8.5. Case Study: AI-Powered Automation in Enterprise Web Services

Chapter 9: Ethical Considerations in AI-Enhanced Web Services

• 9.1. Ensuring Fairness and Transparency in AI Models
• 9.2. Data Privacy and Security in AI-Driven Web Services
• 9.3. Bias in AI Algorithms and Mitigation Strategies
• 9.4. The Role of Governance and Regulation in AI-Powered Web Services
• 9.5. Ethical Challenges in AI-Powered Web Services: A Case Study

Chapter 10: AI in Web Service Integration and Interoperability

• 10.1. AI-Driven Integration Techniques for Heterogeneous Web Services
• 10.2. Interoperability Challenges and Solutions with AI
• 10.3. Using AI for Cross-Platform Communication in Web Services
• 10.4. Semantic Web and AI: Enabling Better Integration
• 10.5. Case Study: AI in Integrating Web Services Across Different Industries

Chapter 11: Monitoring and Scaling AI-Enabled Web Services

• 11.1. Performance Monitoring in AI-Enhanced Web Services
• 11.2. Scaling AI Models and Services for Large-Scale Applications
• 11.3. Managing Latency and Real-Time Processing in AI Web Services
• 11.4. Optimizing the Cost of AI Computations in Cloud Services
• 11.5. Case Study: Scaling AI-Driven Web Services in E-Commerce

Chapter 12: Future Directions in AI and Web Services

• 12.1. The Evolution of Web Services with AI: Trends and Innovations
• 12.2. The Role of 5G and Edge Computing in AI-Powered Web Services
• 12.3. Autonomous Web Services Powered by AI and Blockchain
• 12.4. Integrating AI with IoT for Smarter Web Services
• 12.5. Future Challenges and Opportunities in AI and Web Service Integration

Chapter 13: Conclusion

• 13.1. Key Takeaways from the Book
• 13.2. The Impact of AI on the Future of Web Services
• 13.3. Final Thoughts on the Future of AI in Web Development

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