The book explores the concepts and challenges in developing novel approaches using the Internet of Things, intelligent systems, machine intelligence systems, and data analytics in various industrial sectors such as manufacturing, smart agriculture, smart cities, food processing, environment, defense, stock market and healthcare. Further, it discusses the latest improvements in the industrial sectors using machine intelligence learning and intelligent systems techniques, especially robotics. Features: * Highlights case studies and solutions to industrial problems using machine learning and intelligent systems. * Covers applications in smart agriculture, smart healthcare, intelligent machines for disaster management, and smart manufacturing. * Provides the latest methodologies using machine intelligence systems in the early forecasting of weather. * Examines the research challenges and identifies the gaps in data collection and data analysis, especially imagery, signal, and speech. * Provides applications of digitization and smart processing using the Internet of Things and effective intelligent agent systems in manufacturing. * Discusses a systematic and exhaustive analysis of intelligent software effort estimation models. It will serve as an ideal reference text for graduate students, post-graduate students, IT Professionals, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The book is a collection of high-quality peer-reviewed research papers presented at International Conference on Information System Design and Intelligent Applications (INDIA 2017) held at Duy Tan University, Da Nang, Vietnam during 15-17 June 2017. The book covers a wide range of topics of computer science and information technology discipline ranging from image processing, database application, data mining, grid and cloud computing, bioinformatics and many others. The various intelligent tools like swarm intelligence, artificial intelligence, evolutionary algorithms, bio-inspired algorithms have been well applied in different domains for solving various challenging problems.

Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.

This book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms of nature-inspired computing for Biomedical systems. The proposed meta heuristic or nature-inspired techniques should be an enhanced, hybrid, adaptive or improved version of basic algorithms in terms of performance and convergence metrics. In this exciting and emerging interdisciplinary area a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Today, analysis and processing of data is one of big focuses among researchers community and information society. Due to evolution and knowledge discovery of natural computing, related meta heuristic or bio-inspired algorithms have gained increasing popularity in the recent decade because of their significant potential to tackle computationally intractable optimization dilemma in medical, engineering, military, space and industry fields. The main reason behind the success rate of nature inspired algorithms is their capability to solve problems. The nature inspired optimization techniques provide adaptive computational tools for the complex optimization problems and diversified engineering applications. Tentative Table of Contents/Topic Coverage: - Neural Computation - Evolutionary Computing Methods - Neuroscience driven AI Inspired Algorithms - Biological System based algorithms - Hybrid and Intelligent Computing Algorithms - Application of Natural Computing - Review and State of art analysis of Optimization algorithms - Molecular and Quantum computing applications - Swarm Intelligence - Population based algorithm and other optimizations

This book provides comprehensive details of all Swarm Intelligence based Techniques available till date in a comprehensive manner along with their mathematical proofs. It will act as a foundation for authors, researchers and industry professionals. This monograph will present the latest state of the art research being done on varied Intelligent Technologies like sensor networks, machine learning, optical fiber communications, digital signal processing, image processing and many more.

In this book, application-related studies for acoustic biomedical sensors are covered in depth. The book features an array of different biomedical signals, including acoustic biomedical signals as well as the thermal biomedical signals, magnetic biomedical signals, and optical biomedical signals to support healthcare. It employs signal processing approaches, such as filtering, Fourier transform, spectral estimation, and wavelet transform. The book presents applications of acoustic biomedical sensors and bio-signal processing for prediction, detection, and monitoring of some diseases from the phonocardiogram (PCG) signal analysis. Several challenges and future perspectives related to the acoustic sensors applications are highlighted. This book supports the engineers, researchers, designers, and physicians in several interdisciplinary domains that support healthcare.