Innovations in intelligent machines-3: contemporary achievements in intelligent systems

Modern information technology relies on intelligent systems that can learn and reason over a range of knowledge hidden in available datasets. Achieving these objectives involve applying machine learning biology and nature inspired methods to inductively construct computational and mathematical models (that can use explicit or implicit human supervision) and gain insight in terms of patterns and relationships hidden into the datasets in hand. This ‘insight’ helps the intelligent systems to reason and learn and to use the extracted knowledge for prediction of trends and tendencies, for processes and products monitoring and control, for fault detection and diagnosing in a wide range of application areas. The aim of this edited book is to promote current theoretical and application oriented Intelligent systems research (more specifically in the field of neural networks computing) and to present examples of experimental and real-world investigations that demonstrate contemporary achievements and advances in the area. Leading researchers contribute articles presenting works from this multi-faceted and burgeoning area of research at both theoretical and application levels, covering a variety of intelligent systems related topics. The book is aimed to serve as a valuable source of up-to-date theoretical and application oriented research in Intelligent systems for researchers, scholars, PhD and advanced Master students with special interest in this area.