This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine-firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine-wavelet (SVM-Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.

This book presents some of the emerging techniques and technologies used to handle Web data management. Authors present novel software architectures and emerging technologies and then validate using experimental data and real world applications. The contents of this book are focused on four popular thematic categories of intelligent Web data management: cloud computing, social networking, monitoring and literature management. The Volume will be a valuable reference to researchers, students and practitioners in the field of Web data management, cloud computing, social networks using advanced intelligence tools.

This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.

This provides a comprehensive overview of the key principles of security concerns surrounding the upcoming Internet of Things (IoT), and introduces readers to the protocols adopted in the IoT. It also analyses the vulnerabilities, attacks and defense mechanisms, highlighting the security issues in the context of big data. Lastly, trust management approaches and ubiquitous learning applications are examined in detail. As such, the book sets the stage for developing and securing IoT applications both today and in the future.

This book demonstrates the success of Ambient Intelligence in providing possible solutions for the daily needs of humans. The book addresses implications of ambient intelligence in areas of domestic living, elderly care, robotics, communication, philosophy and others. The objective of this edited volume is to show that Ambient Intelligence is a boon to humanity with conceptual, philosophical, methodical and applicative understanding. The book also aims to schematically demonstrate developments in the direction of augmented sensors, embedded systems and behavioral intelligence towards Ambient Intelligent Networks or Smart Living Technology. It contains chapters in the field of Ambient Intelligent Networks, which received highly positive feedback during the review process. The book contains research work, with in-depth state of the art from augmented sensors, embedded technology and artificial intelligence along with cutting-edge research and development of technologies and applications of Ambient Intelligent Networks. This book is intended to introduce ideas, methods, technologies of the future development of humanity, Science and Technology.

Nowadays embedded and real-time systems contain complex software. The complexity of embedded systems is increasing, and the amount and variety of software in the embedded products are growing. This creates a big challenge for embedded and real-time software development processes and there is a need to develop separate metrics and benchmarks. "Embedded and Real Time System Development: A Software Engineering Perspective: Concepts, Methods and Principles" presents practical as well as conceptual knowledge of the latest tools, techniques and methodologies of embedded software engineering and real-time systems. Each chapter includes an in-depth investigation regarding the actual or potential role of software engineering tools in the context of the embedded system and real-time system. The book presents state-of-the art and future perspectives with industry experts, researchers, and academicians sharing ideas and experiences including surrounding frontier technologies, breakthroughs, innovative solutions and applications. The book is organized into four parts "Embedded Software Development Process", "Design Patterns and Development Methodology", "Modelling Framework" and "Performance Analysis, Power Management and Deployment" with altogether 12 chapters. The book is aiming at (i) undergraduate students and postgraduate students conducting research in the areas of embedded software engineering and real-time systems; (ii) researchers at universities and other institutions working in these fields; and (iii) practitioners in the R&D departments of embedded system. It can be used as an advanced reference for a course taught at the postgraduate level in embedded software engineering and real-time systems.

Computational Intelligence techniques have been widely explored in various domains including forensics. Analysis in forensic encompasses the study of pattern analysis that answer the question of interest in security, medical, legal, genetic studies and etc. However, forensic analysis is usually performed through experiments in lab which is expensive both in cost and time. Therefore, this book seeks to explore the progress and advancement of computational intelligence technique in different focus areas of forensic studies. This aims to build stronger connection between computer scientists and forensic field experts.

This book, "Computational Intelligence in" "Digital Forensics: Forensic Investigation and Applications," is the first volume in the Intelligent Systems Reference Library series. The book presents original research results and innovative applications of computational intelligence in digital forensics. This edited volume contains seventeen chapters and presents the latest state-of-the-art advancement of Computational Intelligence in Digital Forensics; in both theoretical and application papers related to novel discovery in intelligent forensics. The chapters are further organized into three sections: (1) Introduction, (2) Forensic Discovery and Investigation, which discusses the computational intelligence technologies employed in Digital Forensic, and (3) Intelligent Forensic Science Applications, which encompasses the applications of computational intelligence in Digital Forensic, such as human anthropology, human biometrics, human by products, drugs, and electronic devices.

Current companies and communities of practice are involved in intensive networking and collaborative systems by a great variety of electronic relations and collaborative interactions. This has resulted in entangled systems that need to be managed efficiently and in an autonomous way, thus facing many issues and challenges. The extensive research produced in this book will help virtual organizations to exploit latest and powerful technologies based on Grid and Wireless infrastructures as well as Cloud computing in order to alleviate complex issues and challenges arisen in networking and collaborative systems, in terms of collaborative applications, resource management, mobility, and security and system resilience. The ultimate aim of the book is to stimulate research that leads to the creation of responsive environments for networking and, at longer-term, the development of adaptive, secure, mobile, and intuitive intelligent systems for collaborative work and learning. Academic researchers, professionals and practitioners in the field will be inspired and put in practice the ideas and experiences proposed in the book in order to evaluate them for their specific research and work.

The main objective of pervasive computing systems is to create environments where computers become invisible by being seamlessly integrated and connected into our everyday environment, where such embedded computers can then provide inf- mation and exercise intelligent control when needed, but without being obtrusive. Pervasive computing and intelligent multimedia technologies are becoming incre- ingly important to the modern way of living. However, many of their potential applications have not yet been fully realized. Intelligent multimedia allows dynamic selection, composition and presentation of the most appropriate multimedia content based on user preferences. A variety of applications of pervasive computing and - telligent multimedia are being developed for all walks of personal and business life. Pervasive computing (often synonymously called ubiquitous computing, palpable computing or ambient intelligence) is an emerging ?eld of research that brings in revolutionary paradigms for computing models in the 21st century. Pervasive c- puting is the trend towards increasingly ubiquitous connected computing devices in the environment, a trend being brought about by a convergence of advanced el- tronic - and particularly, wireless - technologies and the Internet. Recent advances in pervasive computers, networks, telecommunications and information technology, along with the proliferation of multimedia mobile devices - such as laptops, iPods, personal digital assistants (PDAs) and cellular telephones - have further stimulated the development of intelligent pervasive multimedia applications. These key te- nologiesarecreatingamultimediarevolutionthatwillhavesigni?cantimpactacross a wide spectrum of consumer, business, healthcare and governmental domains.

This unique book dicusses the latest research, innovative ideas, challenges and computational intelligence (CI) solutions in sustainable computing. It presents novel, in-depth fundamental research on achieving a sustainable lifestyle for society, either from a methodological or from an application perspective. Sustainable computing has expanded to become a significant research area covering the fields of computer science and engineering, electrical engineering and other engineering disciplines, and there has been an increase in the amount of literature on aspects sustainable computing such as energy efficiency and natural resources conservation that emphasizes the role of ICT (information and communications technology) in achieving system design and operation objectives. The energy impact/design of more efficient IT infrastructures is a key challenge in realizing new computing paradigms. The book explores the uses of computational intelligence (CI) techniques for intelligent decision support that can be exploited to create effectual computing systems, and addresses sustainability problems in computing and information processing environments and technologies at the different levels of CI paradigms. An excellent guide to surveying the state of the art in computational intelligence applied to challenging real-world problems in sustainable computing, it is intended for scientists, practitioners, researchers and academicians dealing with the new challenges and advances in area.

This Volume discusses the underlying principles and analysis of the different concepts associated with an emerging socio-inspired optimization tool referred to as Cohort Intelligence (CI). CI algorithms have been coded in Matlab and are freely available from the link provided inside the book. The book demonstrates the ability of CI methodology for solving combinatorial problems such as Traveling Salesman Problem and Knapsack Problem in addition to real world applications from the healthcare, inventory, supply chain optimization and Cross-Border transportation. The inherent ability of handling constraints based on probability distribution is also revealed and proved using these problems.

This edited volume deals with the theoretical and methodological aspects, as well as various evolutionary algorithm applications to many real world problems originating from science, technology, business and commerce. It comprises 15 chapters including an introductory chapter which covers the fundamental definitions and outlines some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.

This edited volume is targeted at presenting the latest state-of-the-art methodologies in "Hybrid Evolutionary Algorithms." The chapters deal with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. Overall, the book has 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. The contributions were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.

Foundations of Computational Intelligence Volume 4: Bio-Inspired Data Mining Theoretical Foundations and Applications Recent advances in the computing and electronics technology, particularly in sensor devices, databases and distributed systems, are leading to an exponential growth in the amount of data stored in databases. It has been estimated that this amount doubles every 20 years. For some applications, this increase is even steeper. Databases storing DNA sequence, for example, are doubling their size every 10 months. This growth is occurring in several applications areas besides bioinformatics, like financial transactions, government data, environmental mo- toring, satellite and medical images, security data and web. As large organizations recognize the high value of data stored in their databases and the importance of their data collection to support decision-making, there is a clear demand for - phisticated Data Mining tools. Data mining tools play a key role in the extraction of useful knowledge from databases. They can be used either to confirm a parti- lar hypothesis or to automatically find patterns. In the second case, which is - lated to this book, the goal may be either to describe the main patterns present in dataset, what is known as descriptive Data Mining or to find patterns able to p- dict behaviour of specific attributes or features, known as predictive Data Mining. While the first goal is associated with tasks like clustering, summarization and association, the second is found in classification and regression problems.

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc. Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems. This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

During the past decades scheduling has been among the most studied op- mization problemsanditisstillanactiveareaofresearch!Schedulingappears in many areas of science, engineering and industry and takes di?erent forms depending on the restrictions and optimization criteria of the operating en- ronments [8]. For instance, in optimization and computer science, scheduling has been de?ned as "the allocation of tasks to resources over time in order to achieve optimality in one or more objective criteria in an e?cient way" and in production as "production schedule, i. e. , the planning of the production or the sequence of operations according to which jobs pass through machines and is optimal with respect to certain optimization criteria. " Although there is a standardized form of stating any scheduling problem, namely "e?cient allocation ofn jobs onm machines -which can process no more than one activity at a time- with the objective to optimize some - jective function of the job completion times", scheduling is in fact a family of problems. Indeed, several parameters intervene in the problem de?nition: (a) job characteristics (preemptive or not, precedence constraints, release dates, etc. ); (b) resource environment (single vs. parallel machines, un- lated machines, identical or uniform machines, etc. ); (c) optimization criteria (minimize total tardiness, the number of late jobs, makespan, ?owtime, etc. ; maximize resource utilization, etc. ); and, (d) scheduling environment (static vs. dynamic,intheformerthenumberofjobstobeconsideredandtheirready times are available while in the later the number of jobs and their charact- istics change over time).

The success of the World Wide Web depends on the ability of users to store, p- cess and retrieve digital information regardless of distance boundaries, languages and domains of knowledge. The universality and ?exibility of the World Wide Web have also enabled the rapid growth of a variety of new services and applications based on human-machine interaction. The semantics of exchanged information and services should be useful not only for human to human communications, but also in that machines would be able to understand and automatically process web content. Semanticsgive well-de?nedmeaningto web content and enable computersand p- ple to work in cooperation. Today, the crucial challenge becomes the development of languages to express information in a machine processable format. Now more than ever, new advanced techniques and intelligent approaches are required to tra- form the Web into a universal reasoning and computing machine. Web intelligence attempts to deal with this challenge by exploiting information technologies and - ti?cial intelligence approaches to design the next generation of web-empowered systems and services.

First studied in social insects like ants, indirect self-organizing interactions - known as "stigmergy" - occur when one individual modifies the environment and another subsequently responds to the new environment. The implications of self-organizing behavior extend to robotics and beyond. This book explores the application of stigmergy for a variety of optimization problems. The volume comprises 12 chapters including an introductory chapter conveying the fundamental definitions, inspirations and research challenges.

Richard Chbeir, Youakim Badr, Ajith Abraham, and Aboul-Ella Hassanien Abstract As the Web continues to grow and evolve, more and more data are becoming available. Particularly, multimedia and XML-based data are produced regularly and in increasing way in our daily digital activities, and their retrieval and access must be explored and studied in this emergent web-based era. This book provides reviews of the cutting-edge technologies and insights of various topics related to XML-based and multimedia information access and retrieval under the umbrella of Web Intelligence and reporting how organizations can gain compe- tive advantages by applying new different emergent techniques in the real-world scenarios. The primary target audience for the book includes researchers, scholars, postgraduate students and developers who are interested in advanced information retrieval on the web research and related issues. 1 Introduction Since the last two decades, Internet has changed our daily life by rede?ning the meanings and processes of business, commerce, marketing, ?nance, publishing, R. Chbeir Universite ' de Bourgogne, LE2I-UMR CNRS 5158, Fac. de Sciences Mirande, 21078 Dijon Cedex, France e-mail: richard. chbeir@u-bourgogne. fr Y. Badr INSA de Lyon, Universite ' de Lyon, Depart ' ement Informatique, 7 avenue Jean Capelle, 69621 Villeurbanne CX, France e-mail: youakim. badr@insa-lyon. fr A. Abraham Norwegian University of Science & Technology, Center for Quanti?able Quality of Service in Communication Systems, O. S. Bragstads plass 2E, 7491 Trondheim, Norway e-mail: ajith. abraham@ieee. org A. -E. Hassanien Kuwait University, College of Business & Administration, Dept.

This volume provides the academic and industrial community with a medium for presenting original research and applications related to information assurance and security using computational intelligence techniques. It details current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world problems using computational intelligence.

Medical Informatics (MI) is an emerging interdisciplinary science that deals with clinical health-related information, its structure, acquisition and use. MI is grounded in the principles of computer science, computational intelligence, as well as the clinical and basic sciences. This book deals with the application of computational intelligence in medical informatics. Addressing the various issues of medical informatics using different computational intelligence approaches is the novelty of this edited volume. This volume comprises of 15 chapters including an introductory chapter giving the fundamental definitions and some important research challenges.

Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.

This book is the second of three volumes that illustrate the concept of social networks from a computational point of view. The book contains contributions from a international selection of world-class experts, concentrating on topics relating to security and privacy (the other two volumes review "Tools, Perspectives, and Applications," and "Mining and Visualization" in CSNs). Topics and features: presents the latest advances in security and privacy issues in CSNs, and illustrates how both organizations and individuals can be protected from real-world threats; discusses the design and use of a wide range of computational tools and software for social network analysis; describes simulations of social networks, and the representation and analysis of social networks, with a focus on issues of security, privacy, and anonymization; provides experience reports, survey articles, and intelligence techniques and theories relating to specific problems in network technology.

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.