Complex problems usually cannot be solved by individual methods or techniques and require the synergism of more than one of them to be solved. This book presents a number of current efforts that use combinations of methods or techniques to solve complex problems in the areas of sentiment analysis, search in GIS, graph-based social networking, intelligent e-learning systems, data mining and recommendation systems. Most of them are connected with specific applications, whereas the rest are combinations based on principles. Most of the chapters are extended versions of the corresponding papers presented in CIMA-15 Workshop, which took place in conjunction with IEEE ICTAI-15, in November 2015. The rest are invited papers that responded to special call for papers for the book. The book is addressed to researchers and practitioners from academia or industry, who are interested in using combined methods in solving complex problems in the above areas.

This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.

This book is about computational models of reading, or models that explain (and often simulate) the mental processes that allow us to convert the marks on a printed page into the representations that allow us to understand the contents of what we are reading. Computational Models of Reading assumes no prior knowledge of the topic and is intended for psychologists, linguists, and educators who are interested in gaining a better understanding of what happens in the mind during reading. Erik D. Reichle includes introductory chapters on reading research and computational modelling, and the "core" chapters of the book review both important empirical findings and the models designed to explain those findings within four domains of reading research: word identification, sentence processing, discourse representation, and eye-movement control (which involves coordinating word, sentence, and discourse processing with the perceptual, cognitive, and motoric systems responsible for vision, attention, and eye movements). The final chapter of the book describes a new integrative model of reading, UEber-Reader, and several simulations using the models that demonstrate how it explains several key reading phenomena.

Gathering 20 chapters contributed by respected experts, this book reports on the latest advances in and applications of sliding mode control in science and engineering. The respective chapters address applications of sliding mode control in the broad areas of chaos theory, robotics, electrical engineering, physics, chemical engineering, memristors, mechanical engineering, environmental engineering, finance, and biology. Special emphasis has been given to papers that offer practical solutions, and which examine design and modeling involving new types of sliding mode control such as higher order sliding mode control, terminal sliding mode control, super-twisting sliding mode control, and integral sliding mode control. This book serves as a unique reference guide to sliding mode control and its recent applications for graduate students and researchers with a basic knowledge of electrical and control systems engineering.

This book features research related to computational intelligence and energy and thermal aware management of computing resources. The authors publish original and timely research in current areas of power, energy, temperature, and environmental engineering as and advances in computational intelligence that are benefiting the fields. Topics include signal processing architectures, algorithms, and applications; biomedical informatics and computation; artificial intelligence and machine learning; green technologies in information; and more. The book includes contributions from a wide range of researchers, academicians, and industry professionals. The book is made up both of extended papers presented at the International Conference on Intelligent Computing and Sustainable System (ICICSS 2018), September 20-21, 2018, and other accepted papers on R&D and original research work related to the practice and theory of technologies to enable and support Intelligent Computing applications.

This book highlights papers presented at the Second International Conference on Smart Vehicular Technology, Transportation, Communication and Applications (VTCA 2018), which was held at Mount Emei, Sichuan Province, China from 25 to 28 October 2018. The conference was co-sponsored by Springer, Southwest Jiaotong University, Fujian University of Technology, Chang'an University, Shandong University of Science and Technology, Fujian Provincial Key Lab of Big Data Mining and Applications, and the National Demonstration Center for Experimental Electronic Information and Electrical Technology Education (Fujian University of Technology). The conference was intended as an international forum for researchers and professionals engaged in all areas of smart vehicular technology, vehicular transportation, vehicular communication, and applications.

This book investigates observer-fault estimation techniques in detail, while also highlighting recent research and findings regarding fault estimation. Many practical control systems are subject to possible malfunctions, which may cause significant performance loss or even system instability. To improve the reliability, performance and safety of dynamical systems, fault diagnosis techniques are now receiving considerable attention, both in research and applications, and have been the subject of intensive investigations. Fault detection - the essential first step in fault diagnosis - is a binary decision-making process used to determine whether or not a fault has occurred. In turn, fault isolation is used to identify the location of the faulty component, while fault estimation is used to identify the size of the fault online. Compared with the problems involved in fault detection and isolation, fault estimation is considerably more challenging.

This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

This book provides a pioneering approach to modeling the human diabetic patient using a software agent. It is based on two MASc (Master of Applied Science) theses: one looking at the evolution of the patient agent in time, and another looking the interaction of the patient agent with the healthcare system. It shows that the software agent evolves in a manner analogous to the human patient and exhibits typical attributes of the illness such as reacting to food consumption, medications, and activity. This agent model can be used in a number of different ways, including as a prototype for a specific human patient with the purpose of helping to identify when that patient's condition deviates from normal variations. The software agent can also be used to study the interaction between the human patient and the health care system. This book is of interest to anyone involved in the management of diabetic patients or in societal research into the management of diabetes. The diabetic patient agent was developed using the Ackerman model for diabetes, but this model can be easily adapted for any other model subject with the necessary physiological data to support that model.

The volume "Modern Information Processing: From Theory to Applications," edited by Bernadette Bouchon-Meunier, Giulianella Coletti and Ronald Yager, is a collection of carefully selected papers drawn from the program of IPMU'04, which was held in Perugia, Italy.
The book represents the cultural policy of IPMU conference which is not focused on narrow range of methodologies, but on the contrary welcomes all the theories for the management of uncertainty and aggregation of information in intelligent systems, providing a medium for the exchange of ideas between theoreticians and practitioners in these and related areas.
The book is composed by 7 sections:
UNCERTAINTY
PREFERENCES
CLASSIFICATION AND DATA MINING
AGGREGATION AND MULTI-CRITERIA DECISION MAKING
KNOWLEDGE REPRESENTATION
The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations.
The book can help to build brigs among theories and methods methods for the management of uncertainty.
The book addresses issues which have a position of centrality in our information-centric world.
The book presents interesting results devoted to representing knowledge: the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization).
The book contributes to the goal: an efficient use of the information for a good decision strategy.
APPLIED DOMAINS
. The book contributes to enhancement of our ability to deal effectively with uncertainty in all of its manifestations.
. The book can help to build brigs among theories and methods methods for the management of uncertainty.
. The book addresses issues which have a position of centrality in our information-centric world.
. The book presents interesting results devoted to representing knowledge: the goal is to capture the subtlety of human knowledge (richness) and to allow computer manipulation (formalization).
. The book contributes to the goal: an efficient use of the information for a good decision strategy."

This book features selected papers presented at the 14th International Conference on Electromechanics and Robotics 'Zavalishin's Readings' - ER(ZR) 2019, held in Kursk, Russia, on April 17-20, 2019. The contributions, written by professionals, researchers and students, cover topics in the field of automatic control systems, electromechanics, electric power engineering and electrical engineering, mechatronics, robotics, automation and vibration technologies. The Zavalishin's Readings conference was established as a tribute to the memory of Dmitry Aleksandrovich Zavalishin (1900-1968) - a Russian scientist, corresponding member of the USSR Academy of Sciences, and founder of the school of valve energy converters based on electric machines and valve converters energy. The first conference was organized by the Institute of Innovative Technologies in Electromechanics and Robotics at the Saint Petersburg State University of Aerospace Instrumentation in 2006. The 2019 conference was held with the XIII International Scientific and Technical Conference "Vibration 2019", and was organized by Saint Petersburg State University of Aerospace Instrumentation (SUAI), Saint Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS) and the Southwest State University (SWSU) in with cooperation Russian Foundation for Basic Research (project No. 19-08-20021).

This book discusses vehicular communication systems, IoT, intelligent transportation systems and the Internet of Vehicles, and also introduces destination marketing in a structured manner. It is primarily intended for research students interested in emerging technologies for connected Internet of Vehicles and intelligent transportation system networks; academics in higher education institutions, including universities and vocational colleges; IT professionals; policy makers; and legislators. The book can also be used as a reference resource for both undergraduate and graduate studies. Written in plain and simple language, it describes new concepts so that they are accessible to readers without prior knowledge of the field.

This book discusses the principle of automotive intelligent technology from the point of view of modern sensing and intelligent control. Based on the latest research in the field, it explores safe driving with intelligent vision; intelligent monitoring of dangerous driving; intelligent detection of automobile power and transmission systems; intelligent vehicle navigation and transportation systems; and vehicle-assisted intelligent technology. It draws on the author's research in the field of automotive intelligent technology to explain the fundamentals of vehicle intelligent technology, from the information sensing principle to mathematical models and the algorithm basis, enabling readers to grasp the concepts of automotive intelligent technology. Opening up new scientific horizons and fostering innovative thinking, the book is a valuable resource for researchers as well as undergraduate and graduate students.

At the centre of the methodology used in this book is STEM learning variability space that includes STEM pedagogical variability, learners' social variability, technological variability, CS content variability and interaction variability. To design smart components, firstly, the STEM learning variability space is defined for each component separately, and then model-driven approaches are applied. The theoretical basis includes feature-based modelling and model transformations at the top specification level and heterogeneous meta-programming techniques at the implementation level. Practice includes multiple case studies oriented for solving the task prototypes, taken from the real world, by educational robots. These case studies illustrate the process of gaining interdisciplinary knowledge pieces identified as S-knowledge, T-knowledge, E-knowledge, M-knowledge or integrated STEM knowledge and evaluate smart components from the pedagogical and technological perspectives based on data gathered from one real teaching setting. Smart STEM-Driven Computer Science Education: Theory, Methodology and Robot-based Practices outlines the overall capabilities of the proposed approach and also points out the drawbacks from the viewpoint of different actors, i.e. researchers, designers, teachers and learners.

Making a Machine That Sees Like Us explains why and how our visual perceptions can provide us with an accurate representation of the external world. Along the way, it tells the story of a machine (a computational model) built by the authors that solves the computationally difficult problem of seeing the way humans do. This accomplishment required a radical paradigm shift - one that challenged preconceptions about visual perception and tested the limits of human behavior-modeling for practical application.
The text balances scientific sophistication and compelling storytelling, making it accessible to both technical and general readers. Online demonstrations and references to the authors' previously published papers detail how the machine was developed and what drove the ideas needed to make it work. The authors contextualize their new theory of shape perception by highlighting criticisms and opposing theories, offering readers a fascinating account not only of their revolutionary results, but of the scientific process that guided the way.

The book offers an integrated vision on Cloud and HPC, Big Data, Analytics and virtualization in computing-oriented manufacturing, combining information and communication technologies, service-oriented control of holonic architectures as well as enterprise integration solutions based on SOA principles. It is structured in eight parts, each one grouping research and trends in digital manufacturing and service oriented manufacturing control: Cloud and Cyber-Physical Systems for Smart Manufacturing, Reconfigurable and Self-organized Multi-Agent Systems for Industry and Service, Sustainability Issues in Intelligent Manufacturing Systems, Holonic and Multi-agent System Design for Industry and Service, Should Intelligent Manufacturing Systems be Dependable and Safe?, Service-oriented Management and Control of Manufacturing Systems, Engineering and Human Integration in Flexible and Reconfigurable Industrial Systems,Virtualization and Simulation in Computing-oriented Industry and Service.p>

This book presents a comprehensive definition of smart grids and their benefits, and compares smart and traditional grids. It also introduces a design methodology for stand-alone hybrid renewable energy system with and without applying the smart grid concepts for comparison purposes. It discusses using renewable energy power plants to feed loads in remote areas as well as in central power plants connected to electric utilities. Smart grid concepts used in the design of the hybrid renewable power systems can reduce the size of components, which can be translated to a reduction in the cost of generated energy. The proposed hybrid renewable energy system includes wind, photovoltaic, battery, and diesel, and is used initially to feed certain loads, covering the load required completely. The book introduces a novel methodology taking the smart grid concept into account by dividing the loads into high and low priority parts. The high priority part should be supplied at any generated conditions. However, the low priority loads can be shifted to the time when the generated energy from renewable energy sources is greater than the high priority loads requirements. The results show that the use of this smart grid concept reduces the component size and the cost of generated energy compared to that without dividing the loads. The book also describes the use of smart optimization techniques like particle swarm optimization (PSO) and genetic algorithm (GA) to optimally design the hybrid renewable energy system. This book provides an excellent background to renewable energy sources, optimal sizing and locating of hybrid renewable energy sources, the best optimization methodologies for sizing and designing the components of hybrid renewable energy systems, and offers insights into using smart grid concepts in the system's design and sizing. It also helps readers understand the dispatch methodology and how to connect the system's different components, their modeling, and the cost analysis of the system.

This book explores the subject of artificial psychology and how the field must adapt human neuro-psychological testing techniques to provide adequate cognitive testing of advanced artificial intelligence systems. It shows how classical testing methods will reveal nothing about the cognitive nature of the systems and whether they are learning, reasoning, and evolving correctly; for these systems, the authors outline how testing techniques similar to/adapted from human psychological testing must be adopted, particularly in understanding how the system reacts to failure or relearning something it has learned incorrectly or inferred incorrectly. The authors provide insights into future architectures/capabilities that artificial cognitive systems will possess and how we can evaluate how well they are functioning. It discusses at length the notion of human/AI communication and collaboration and explores such topics as knowledge development, knowledge modeling and ambiguity management, artificial cognition and self-evolution of learning, artificial brain components and cognitive architecture, and artificial psychological modeling. Explores the concepts of Artificial Psychology and Artificial Neuroscience as applied to advanced artificially cognitive systems; Provides insight into the world of cognitive architectures and biologically-based computing designs which will mimic human brain functionality in artificial intelligent systems of the future; Provides description and design of artificial psychological modeling to provide insight into how advanced artificial intelligent systems are learning and evolving; Explores artificial reasoning and inference architectures and the types of modeling and testing that will be required to "trust" an autonomous artificial intelligent systems.

This book focuses on the generalization of map features, providing descriptions and classifying groups of map objects into six categories: point clusters, groups of contours, road networks, river networks, continuous areal features and discrete areal features. Discussing the methods and algorithms in map generalization in equal measure, it also describes the approaches for describing map features. The book is a valuable reference for graduates and researchers who are interested in cartography and geographic information science/systems, especially those in automated map generalization and spatial databases construction.

This book summarizes the new research results presented at the 12th Joint Conference on Knowledge-Based Software Engineering (JCKBSE 2018), which took place on August 27-30, 2018 on the island of Corfu, Greece. The JCKBSE is a well-established international biennial conference that focuses on the applications of Artificial Intelligence in Software Engineering. The JCKBSE 2018 was organized by the Department of Informatics of the University of Piraeus, the Department of Computer and Information Engineering of Nippon Institute of Technology, and the Department of Informatics of Ionian University. The book will benefit not only experts and researchers in the field of (Knowledge-Based) Software Engineering, but also general readers in the fields of Artificial Intelligence, Computational Intelligence and Computer Science who wish to learn more about the field of (Knowledge-Based) Software Engineering and its applications. An extensive list of bibliographic references at the end of each paper encourages readers to probe further into the application areas that interest them most.