The Internet has become an integral part of human life, yet the web still utilizes mundane interfaces to the physical world, which makes Internet operations somewhat mechanical, tedious, and less human-oriented. Filling a large void in the literature, Intelligent Technologies for Web Applications is one of the first books to focus on providing vital fundamental and advanced guidance in the area of Web intelligence for beginners and researchers.

The book covers techniques from diverse areas of research, including:
Natural language processing Information extraction, retrieval, and filtering Knowledge representation and management Machine learning Databases Data, web, and text mining Human–computer interaction Semantic web technologies

To develop effective and intelligent web applications and services, it is critical to discover useful knowledge through analyzing large amounts of content, hidden content structures, or usage patterns of web data resources. Intended to improve and reinforce problem-solving methods in this area, this book delves into the hybridization of artificial intelligence (AI) and web technologies to help simplify complex Web operations. It introduces readers to the state-of-the art development of web intelligence techniques and teaches how to apply these techniques to develop the next generation of intelligent Web applications.

The book lays out presented projects, case studies, and innovative ideas, which readers can explore independently as standalone research projects. This material facilitates experimentation with the book’s content by including fundamental tools, research directions, practice questions, and additional reading.

Contents

Part I: Introduction to the Web, machine learning, new AI techniques, and web intelligence

Introduction to World Wide Web

Brief history of the Web and the Internet

Blogs

Tweets

Wikis

Collaborative mapping

Aggregation technologies

Open platpplication programming interface, and programming tools

Web intelligence

Intelligence in web applications

Organization of this book

Machine learning concepts

Introduction

Linear regression

Supervised learning: Classification

Support vector machines

Nearest neighbor classifiers

Unsupervised learning: clustering

Hidden Markov models

Bayesian methods

Reinforcement learning

Applications of machine learning

Conclusion

Overview of constituents for the new artificial intelligence

Foundations of the new artificial intelligence and knowledge-based system

Fuzzy systems

Artificial neural networks

Genetic algorithms and evolutionary computing

Rough sets

Soft computing

Benefits of the new AI to World Wide Web

Web intelligence

Internet, web, grid, and cloud

Introduction to web intelligence

Perspectives of WI

Levels of WI

Goal of WI...