This book presents recent research on the hybridization of intelligent methods, which refers to combining methods to solve complex problems. It discusses hybrid approaches covering different areas of intelligent methods and technologies, such as neural networks, swarm intelligence, machine learning, reinforcement learning, deep learning, agent-based approaches, knowledge-based system and image processing. The book includes extended and revised versions of invited papers presented at the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), held in The Hague, Holland, in August 2016. The book is intended for researchers and practitioners from academia and industry interested in using hybrid methods for solving complex problems.

There are many different approaches to understanding human consciousness. By conducting research to better understand various biological mechanisms, these can be redefined and utilized for technological purposes. Advanced Research on Biologically Inspired Cognitive Architectures is an essential reference source for the latest scholarly research on the biological elements of human cognition and examines the applications of consciousness within computing environments. Featuring exhaustive coverage on a broad range of innovative topics and perspectives, such as artificial intelligence, bio-robotics, and human-computer interaction, this publication is ideally designed for academics, researchers, professionals, graduate students, and practitioners seeking current research on the exploration of the intricacies of consciousness and different approaches of perception.

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.

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 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.

Advanced research in the field of mechatronics and robotics represents a unifying interdisciplinary and intelligent engineering science paradigm. It is a holistic, concurrent, and interdisciplinary engineering science that identifies novel possibilities of synergizing and fusing different disciplines. The Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics is a collection of innovative research on the methods and applications of knowledge in both theoretical and practical skills of intelligent robotics and mechatronics. While highlighting topics including green technology, machine learning, and virtual manufacturing, this book is ideally designed for researchers, students, engineers, and computer practitioners seeking current research on developing innovative ideas for intelligent robotics and autonomous and smart interdisciplinary mechatronic products.

This book focuses on modelling and simulation, control and optimization, signal processing, and forecasting in selected nonlinear dynamical systems, presenting both literature reviews and novel concepts. It develops analytical or numerical approaches, which are simple to use, robust, stable, flexible and universally applicable to the analysis of complex nonlinear dynamical systems. As such it addresses key challenges are addressed, e.g. efficient handling of time-varying dynamics, efficient design, faster numerical computations, robustness, stability and convergence of algorithms. The book provides a series of contributions discussing either the design or analysis of complex systems in sciences and engineering, and the concepts developed involve nonlinear dynamics, synchronization, optimization, machine learning, and forecasting. Both theoretical and practical aspects of diverse areas are investigated, specifically neurocomputing, transportation engineering, theoretical electrical engineering, signal processing, communications engineering, and computational intelligence. It is a valuable resource for students and researchers interested in nonlinear dynamics and synchronization with applications in selected areas.

This book addresses two of the most difficult and computationally intractable classes of problems: discrete resource constrained scheduling, and discrete-continuous scheduling. The first part of the book discusses problems belonging to the first class, while the second part deals with problems belonging to the second class. Both parts together offer valuable insights into the possibility of implementing modern techniques and tools with a view to obtaining high-quality solutions to practical and, at the same time, computationally difficult problems. It offers a valuable source of information for practitioners dealing with the real-world scheduling problems in industry, management and administration. The authors have been working on the respective problems for the last decade, gaining scientific recognition through publications and active participation in the international scientific conferences, and their results are obtained using population-based methods. Dr E. Ratajczk-Ropel explores multiple agent and A-Team concepts, while Dr A. Skakovski focuses on evolutionary algorithms with a particular focus on the population learning paradigm.

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.

Since its initial development, particle swarm optimization has gained wide recognition due to its ability to provide solutions efficiently, requiring only minimal implementation effort. Particle Swarm Optimization and Intelligence: Advances and Applications examines modern intelligent optimization algorithms proven as very efficient in applications from various scientific and technological fields. Providing distinguished and unique research, this innovative publication offers a compendium of leading field experiences as well as theoretical analyses and complementary techniques useful to academicians and practitioners.

This book presents original research articles addressing various aspects of economics, management and optimization. The topics discussed include economics, finance, marketing, resource allocation strategies, fuzzy logic, and network-based techniques for the analysis of economics, management and mathematical optimization. Combining the input of contributing professors and researchers from various Spanish, Italian and Latin American universities, the book will be of interest to students, researchers and practitioners, as well as members of the general public interested in the world of Economics and Management.

This book presents revised and extended versions of the best papers presented at the 9th International Joint Conference on Computational Intelligence (IJCCI 2017), held in Funchal, Madeira, from 1 to 3 November 2017. It focuses on four of the main fields of computational intelligence: evolutionary computation, fuzzy computation, neural computation, and cognitive and hybrid systems. As well as presenting the recent advances of these areas, it provides new and innovative solutions for established researchers and a source of information and/or inspiration those new to the field. Discussing innovative techniques in various application areas, it is a useful resource for individual researchers and a valuable addition to academic libraries (of universities and engineering schools).

This book presents the outcomes of the 16th International Conference on Software Engineering, Artificial Intelligence Research, Management and Applications (SERA 2018), which was held in Kunming, China on June 13-15, 2018. The aim of the conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. The book includes findings on all aspects (theory, applications and tools) of computer and information science, and discusses related practical challenges and the solutions adopted to solve them. The conference organizers selected the best papers from those accepted for presentation. The papers were chosen based on review scores submitted by members of the program committee and underwent a further rigorous round of review. From this second round, 13 of the conference's most promising papers were then published in this Springer (SCI) book and not the conference proceedings. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.

This book addresses the topic of fractional-order modeling of nuclear reactors. Approaching neutron transport in the reactor core as anomalous diffusion, specifically subdiffusion, it starts with the development of fractional-order neutron telegraph equations. Using a systematic approach, the book then examines the development and analysis of various fractional-order models representing nuclear reactor dynamics, ultimately leading to the fractional-order linear and nonlinear control-oriented models. The book utilizes the mathematical tool of fractional calculus, the calculus of derivatives and integrals with arbitrary non-integer orders (real or complex), which has recently been found to provide a more compact and realistic representation to the dynamics of diverse physical systems. Including extensive simulation results and discussing important issues related to the fractional-order modeling of nuclear reactors, the book offers a valuable resource for students and researchers working in the areas of fractional-order modeling and control and nuclear reactor modeling.

This book introduces readers to the latest exciting advances in human motion sensing and recognition, from the theoretical development of fuzzy approaches to their applications. The topics covered include human motion recognition in 2D and 3D, hand motion analysis with contact sensors, and vision-based view-invariant motion recognition, especially from the perspective of Fuzzy Qualitative techniques. With the rapid development of technologies in microelectronics, computers, networks, and robotics over the last decade, increasing attention has been focused on human motion sensing and recognition in many emerging and active disciplines where human motions need to be automatically tracked, analyzed or understood, such as smart surveillance, intelligent human-computer interaction, robot motion learning, and interactive gaming. Current challenges mainly stem from the dynamic environment, data multi-modality, uncertain sensory information, and real-time issues. These techniques are shown to effectively address the above challenges by bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in intelligent systems. The book not only serves as a valuable reference source for researchers and professionals in the fields of computer vision and robotics, but will also benefit practitioners and graduates/postgraduates seeking advanced information on fuzzy techniques and their applications in motion analysis.

This book provides insights into the First International Conference on Communication, Devices and Computing (ICCDC 2017), which was held in Haldia, India on November 2-3, 2017. It covers new ideas, applications and the experiences of research engineers, scientists, industrialists, scholars and students from around the globe. The proceedings highlight cutting-edge research on communication, electronic devices and computing, and address diverse areas such as 5G communication, spread spectrum systems, wireless sensor networks, signal processing for secure communication, error control coding, printed antennas, analysis of wireless networks, antenna array systems, analog and digital signal processing for communication systems, frequency selective surfaces, radar communication, and substrate integrated waveguide and microwave passive components, which are key to state-of-the-art innovations in communication technologies.

This volume highlights new trends and challenges in research on agents and the new digital and knowledge economy, and includes 23 papers classified into the following categories: business process management, agent-based modeling and simulation, and anthropic-oriented computing. All papers were originally presented at the 11th International KES Conference on Agents and Multi-Agent Systems - Technologies and Applications (KES-AMSTA 2017) held June 21-23, 2017 in Vilamoura, Algarve, Portugal. Today's economy is driven by technologies and knowledge. Digital technologies can free, shift and multiply choices, and often intrude on the territory of other industries by providing new ways of conducting business operations and creating value for customers and companies. The topics covered in this volume include software agents, multi-agent systems, agent modeling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computer embedded systems and nature inspired manufacturing, etc., all of which contribute to the modern Digital Economy. The results presented here will be of theoretical and practical value to researchers and industrial practitioners working in the fields of artificial intelligence, collective computational intelligence, innovative business models, the new digital and knowledge economy and, in particular, agent and multi-agent systems, technologies, tools and applications.

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 describes load modeling approaches for complex work pieces and batch forgings, and demonstrates analytical modeling and data-driven modeling approaches for known and unknown complex forging processes. It overcomes the current shortcomings of modeling, analysis and control approaches, presenting contributions in three major areas: In the first, several novel modeling approaches are proposed: a process/shape-decomposition modeling method to help estimate the deformation force; an online probabilistic learning machine for the modeling of batch forging processes; and several data-driven identification and modeling approaches for unknown forging processes under different work conditions. The second area develops model-based dynamic analysis methods to derive the conditions of stability and creep. Lastly, several novel intelligent control methods are proposed for complex forging processes. One of the most serious problems in forging forming involves the inaccurate forging conditions, velocity and position offered by the hydraulic actuator due to the complexity of both the deformation process of the metal work piece and the motion process of the hydraulic actuator. The book summarizes the current weaknesses of modeling, analysis and control approaches. are summarized as follows: a) With the current modeling approaches it is difficult to model complex forging processes with unknown parameters, as they only model the dynamics in local working areas but do not effectively model unknown nonlinear systems across multiple working areas; further, they do not take the batch forging process into account, let alone its distribution modeling. b) All previous dynamic analysis studies simplify the forging system to having a single-frequency pressure fluctuation and neglect the influences of non-linear load force. Further, they fail to take the flow equation in both valves and cylinders into account. c) Conventional control approaches only consider the linear deformation force and pay no attention to sudden changes and the motion synchronization for the multi-cylinder system, making them less effective for complex, nonlinear time-varying forging processes subject to sudden changes.

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.

The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems. Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.