Quantum Inspired Computational Intelligence: Research and Applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence-or soft computing-is conjoined with quantum computing to achieve this objective. The models covered can be applied to any endeavor which handles complex and meaningful information.

Applied Smart Health Care Informatics Explores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare Applied Smart Health Care Informatics explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies. Detailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services. Provides an overview of different deep learning applications for intelligent healthcare informatics management Describes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management Proposes IoT solutions that disseminate essential medical information for intelligent healthcare management Discusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques Examines the use of exploratory data analysis in intelligent healthcare informatics systems Applied Smart Health Care Informatics: A Computational Intelligence Perspective is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing.

This book discusses recent developments in the vast domain of optimization. Featuring papers presented at the 1st International Conference on Frontiers in Optimization: Theory and Applications (FOTA 2016), held at the Heritage Institute of Technology, Kolkata, on 24-26 December 2016, it opens new avenues of research in all topics related to optimization, such as linear and nonlinear optimization; combinatorial-, stochastic-, dynamic-, fuzzy-, and uncertain optimization; optimal control theory; as well as multi-objective, evolutionary and convex optimization and their applications in intelligent information and technology, systems science, knowledge management, information and communication, supply chain and inventory control, scheduling, networks, transportation and logistics and finance. The book is a valuable resource for researchers, scientists and engineers from both academia and industry.

There has been rapid growth in biomedical engineering in recent decades, given advancements in medical imaging and physiological modelling and sensing systems, coupled with immense growth in computational and network technology, analytic approaches, visualization and virtual-reality, man-machine interaction and automation. Biomedical engineering involves applying engineering principles to the medical and biological sciences and it comprises several topics including biomedicine, medical imaging, physiological modelling and sensing, instrumentation, real-time systems, automation and control, signal processing, image reconstruction, processing and analysis, pattern recognition, and biomechanics. It holds great promise for the diagnosis and treatment of complex medical conditions, in particular, as we can now target direct clinical applications, research and development in biomedical engineering is helping us to develop innovative implants and prosthetics, create new medical imaging technologies and improve tools and techniques for the detection, prevention and treatment of diseases. The contributing authors in this edited book present representative surveys of advances in their respective fields, focusing in particular on techniques for the analysis of complex biomedical data. The book will be a useful reference for graduate students, researchers and industrial practitioners in computer science, biomedical engineering, and computational and molecular biology.

Proper analysis of image and multimedia data requires efficient extraction and segmentation techniques. Among the many computational intelligence approaches, the soft computing paradigm is best equipped with several tools and techniques that incorporate intelligent concepts and principles. This book is dedicated to object extraction, image segmentation, and edge detection using soft computing techniques with extensive real-life application to image and multimedia data. The authors start with a comprehensive tutorial on the basics of brain structure and learning, and then the key soft computing techniques, including evolutionary computation, neural networks, fuzzy sets and fuzzy logic, and rough sets. They then present seven chapters that detail the application of representative techniques to complex image processing tasks such as image recognition, lighting control, target tracking, object extraction, and edge detection. These chapters follow a structured approach with detailed explanations of the problems, solutions, results, and conclusions. This is both a standalone textbook for graduates in computer science, electrical engineering, system science, and information technology, and a reference for researchers and engineers engaged with pattern recognition, image processing, and soft computing.

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques - genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

Proper analysis of image and multimedia data requires efficient extraction and segmentation techniques. Among the many computational intelligence approaches, the soft computing paradigm is best equipped with several tools and techniques that incorporate intelligent concepts and principles. This book is dedicated to object extraction, image segmentation, and edge detection using soft computing techniques with extensive real-life application to image and multimedia data. The authors start with a comprehensive tutorial on the basics of brain structure and learning, and then the key soft computing techniques, including evolutionary computation, neural networks, fuzzy sets and fuzzy logic, and rough sets. They then present seven chapters that detail the application of representative techniques to complex image processing tasks such as image recognition, lighting control, target tracking, object extraction, and edge detection. These chapters follow a structured approach with detailed explanations of the problems, solutions, results, and conclusions. This is both a standalone textbook for graduates in computer science, electrical engineering, system science, and information technology, and a reference for researchers and engineers engaged with pattern recognition, image processing, and soft computing.

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques - genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit, therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit, therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters