Intelligent systems provide a platform to connect the research in artificial intelligence to real-world problem solving applications. Various intelligent systems have been developed to face real-world applications. This book discusses the modern advances in intelligent systems and the tools in applied artificial intelligence. It consists of twenty-three chapters authored by participants of the 25th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2012) which was held in Dalian, China. This book is divided into six parts, including Applied Intelligence, Cognitive Computing and Affective Computing, Data Mining and Intelligent Systems, Decision Support Systems, Machine Learning, and Natural Language Processing. Each part includes three to five chapters. In these chapters, many approaches, applications, restrictions, and discussions are presented. The material of each chapter is self-contained and was reviewed by at least two anonymous referees to assure the high quality. Readers can select any individual chapter based on their research interests without the need of reading other chapters. We hope that this book provides useful reference values to researchers and students in the field of applied intelligence. We also hope that readers will find opportunities and recognize challenges through the papers presented in this book.

Ongoing advancements in modern technology have led to significant developments in intelligent systems. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Intelligent Systems: Concepts, Methodologies, Tools, and Applications contains a compendium of the latest academic material on the latest breakthroughs and recent progress in intelligent systems. Including innovative studies on information retrieval, artificial intelligence, and software engineering, this multi-volume book is an ideal source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of intelligent systems.

This book contains a selection of articles from The 2013 World Conference on Information Systems and Technologies (WorldCIST'13), a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences and concerns in the several perspectives of Information Systems and Technologies. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Intelligent and Decision Support Systems; Software Systems, Architectures, Applications and Tools; Computer Networks, Mobility and Pervasive Systems; Radar Technologies; and Human-Computer Interaction.

Intelligent Systems involve a large class of systems which posses human-like capabilities such as learning, observation, perception, interpretation, reasoning under uncertainty, planning in known and unknown environments, decision making, and control action. The field of intelligent systems is actually a new interdisciplinary field which is the outcome of the interaction, cooperation and synergetic merging of classical fields such as system theory, control theory, artificial intelligence, information theory, operational research, soft computing, communications, linguistic theory, and others. Integrated intelligent decision and control systems involve three primary hierarchical levels, namely organization, coordination and execution levels. As we proceed from the be performed organization to the execution level, the precision about the jobs to increases and accordingly the intelligence required for these jobs decreases. This is in compliance with the principle of increasing precision with decreasing intelligence (IPOI) known from the management field and theoretically established by Saridis using information theory concepts. This book is concerned with intelligent systems and techniques and gives emphasis on the computational and processing issues. Control issues are not included here. The contributions of the book are presented in four parts as follows.

Traditional artificial intelligence (AI) techniques are based around mathematical techniques of symbolic logic, with programming in languages such as Prolog and LISP invented in the 1960s. These are referred to as "crisp" techniques by the soft computing community. The new wave of AI methods seeks inspiration from the world of biology, and is being used to create numerous real-world intelligent systems with the aid of soft computing tools. These new methods are being increasingly taught at the upper end of the curriculum, sometimes as an adjunct to traditional AI courses, and sometimes as a replacement for them. Where a more radical approach is taken and the course is being taught at an introductory level, we have recently published Negnevitsky's book. Karray and Silva will be suitable for the majority of courses which will be found at an advanced level. Karray and de Silva cover the problem of control and intelligent systems design using soft-computing techniques in an integrated manner. They present both theory and applications, including industrial applications, and the book contains numerous worked examples, problems and case studies. Covering the state-of-the-art in soft-computing techniques, the book gives the reader sufficient knowledge to tackle a wide range of complex systems for which traditional techniques are inadequate.

This book explores the latest research trends in intelligent systems and smart applications. It presents high-quality empirical and review studies focusing on various topics, including information systems and software engineering, knowledge management, technology in education, emerging technologies, and social networks. It provides insights into the theoretical and practical aspects of intelligent systems and smart applications.

This book covers all the emerging trends in artificial intelligence (AI) and the Internet of Things (IoT). The Internet of Things is a term that has been introduced in recent years to define devices that are able to connect and transfer data to other devices via the Internet. While IoT and sensors have the ability to harness large volumes of data, AI can learn patterns in the data and quickly extract insights in order to automate tasks for a variety of business benefits. Machine learning, an AI technology, brings the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate, and it can have significant advantages over traditional business intelligence tools for analyzing IoT data, including being able to make operational predictions up to 20 times earlier and with greater accuracy than threshold-based monitoring systems. Further, other AI technologies, such as speech recognition and computer vision can help extract insights from data that used to require human review. The powerful combination of AI and IoT technology is helping to avoid unplanned downtime, increase operating efficiency, enable new products and services, and enhance risk management.