Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there's an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.

Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing. This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme. "This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems. The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market. It is an excellent book after all." - Guanrong Chen, City University of Hong Kong "This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author." - Alma Y. Alanis, University of Guadalajara, Mexico "This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones." - Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India

This monograph focuses on the mathematical modeling of distributed parameter systems in which mass/energy transport or wave propagation phenomena occur and which are described by partial differential equations of hyperbolic type. The case of linear (or linearized) 2 x 2 hyperbolic systems of balance laws is considered, i.e., systems described by two coupled linear partial differential equations with two variables representing physical quantities, depending on both time and one-dimensional spatial variable. Based on practical examples of a double-pipe heat exchanger and a transportation pipeline, two typical configurations of boundary input signals are analyzed: collocated, wherein both signals affect the system at the same spatial point, and anti-collocated, in which the input signals are applied to the two different end points of the system. The results of this book emerge from the practical experience of the author gained during his studies conducted in the experimental installation of a heat exchange center as well as from his research experience in the field of mathematical and computer modeling of dynamic systems. The book presents valuable results concerning their state-space, transfer function and time-domain representations, which can be useful both for the open-loop analysis as well as for the closed-loop design. The book is primarily intended to help professionals as well as undergraduate and postgraduate students involved in modeling and automatic control of dynamic systems.

In many practical applications we deal with a wide class of dynamical systems that are comprised of a family of continuous-time or discrete-time subsystems and a rule orchestrating the switching between the subsystems. This class of systems is frequently called switched system. Switched linear systems provide a framework that bridges the linear systems and the complex and/or uncertain systems. The mo- vation for investigating this class of systems is twofold: ?rst, it has an inherent multi-modal behavior in the sense that several dynamical subsystems are required to describe their behavior, which might depend on various environmental factors. Second, the methods of intelligent control systems are based on the idea of swit- ing between different controllers. Looked at in this light, switched systems provide an integral framework to deal with complex system behaviors such as chaos and multiple limit cycles and gain more insights into powerful tools such as intelligent control, adaptive control, and robust control. Switched systems have been inves- gated for a long time in the control and systems literature and have increasingly attracted more attention for the past three decades. The number of journal articles, books, and conference papers have grown exponentially and a number of fundam- tal concepts and powerful tools have been developed. It has been pointed out that switched systems have been studied from various viewpoints.

Focuses on the definition, engineering, and delivery of AI solutions as opposed to AI itself Reader will still gain a strong understanding of AI, but through the perspective of delivering real solutions Explores the core AI issues that impact the success of an overall solution including i. realities of dealing with data, ii. impact of AI accuracy on the ability of the solution to meet business objectives, iii. challenges in managing the quality of machine learning models Includes real world examples of enterprise scale solutions Provides a series of (optional) technical deep dives and thought experiments.

This book discusses the technological aspects for the implementation of Society 5.0. The foundation and recent advances of emerging technologies such as artificial intelligence, data science, Internet of Things, and Big Data for the realization of Society 5.0 are covered. Practical solutions to existing problems, examples, and case studies are also offered. Society 5.0 and the Future of Emerging Computational Technologies: Practical Solutions, Examples, and Case Studies discusses technologies such as machine learning, artificial intelligence, and Internet of Things for the implementation of Society 5.0. It offers a firm foundation and understanding of the recent advancements in various domains such as data analytics, neural networks, computer vision, and robotics, along with practical solutions to existing problems in fields such as healthcare, manufacturing industries, security, and infrastructure management. Applications and implementations are highlighted along with the correlation between technologies. Examples and case studies are presented throughout the book to augment text. This book can be used by research scholars in the engineering domain who wish to gain knowledge and contribute towards a modern and secure future society. The book will also be useful as a reference at universities for postgraduate students who are interested in technological advancements.

Solid requirements engineering has increasingly been recognized as the key to improved, on-time, and on-budget delivery of software and systems projects. New software tools are emerging that are empowering practicing engineers to improve their requirements engineering habits. However, these tools are not usually easy to use without significant training. Requirements Engineering for Software and Systems, Fourth Edition is intended to provide a comprehensive treatment of the theoretical and practical aspects of discovering, analyzing, modeling, validating, testing, and writing requirements for systems of all kinds, with an intentional focus on software-intensive systems. It brings into play a variety of formal methods, social models, and modern requirements writing techniques to be useful to practicing engineers. The book is intended for professional software engineers, systems engineers, and senior and graduate students of software or systems engineering. Since the first edition, there have been made many changes and improvements to this textbook. Feedback from instructors, students, and corporate users was used to correct, expand, and improve the materials. The fourth edition features two newly added chapters: "On Non-Functional Requirements" and "Requirements Engineering: Road Map to the Future." The latter provides a discussion on the relationship between requirements engineering and such emerging and disruptive technologies as Internet of Things, Cloud Computing, Blockchain, Artificial Intelligence, and Affective Computing. All chapters of the book were significantly expanded with new materials that keep the book relevant to current industrial practices. Readers will find expanded discussions on new elicitation techniques, agile approaches (e.g., Kanpan, SAFe, and DEVOps), requirements tools, requirements representation, risk management approaches, and functional size measurement methods. The fourth edition also has significant additions of vignettes, exercises, and references. Another new feature is scannable QR codes linked to sites containing updates, tools, videos, and discussion forums to keep readers current with the dynamic field of requirements engineering.

This book describes the modelling and optimisation of vibration reduction systems in an integrated fashion using nonlinear equations of motion. It proposes an effective optimisation method for determining the basic characteristics of the non-linear visco-elastic elements used in passive vibration reduction systems. In the case of semi-active and active vibration isolators, a design process of the advanced control systems is proposed that makes possible to optimise the controller settings relatively to the selected vibro-isolation criteria. The approach developed here is subsequently tested by means of experimental investigations conducted on various sample vibration reduction systems: passive, semi-active and active. The book presents a biomechanical modelling approach that allows users to select the properties of vibro-isolation systems for different types of oscillation and different optimisation criteria - and can significantly reduce the harmful vibrations that can affect the human body in the process. Further, the book equips readers to evaluate the viscoelastic characteristics of passive systems and design control systems for semi-active and active systems. Modelling and Control Design of Vibration Reduction Systems offers a valuable guide for researchers and practitioners alike. It also provides students and academics with systematic information on the procedures to be followed in the design process for semi-active or active vibration reduction systems.

A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.

This book puts forward the concept of a virtual equivalent system (VES) based on theoretical analysis and simulation results. The new concept will facilitate the development of a unified framework for analyzing the stability and convergence of self-tuning control (STC) systems, and potentially, of all adaptive control systems. The book then shows that a time-varying STC system can be converted into a time-invariant system using a certain nonlinear compensation signal, which reduces the complexity and difficulty of stability and convergence analysis. In closing, the VES concept and methodology are used to assess the stability of multiple model adaptive control (MMAC) systems and T-S model-based fuzzy control systems.

This volume gathers the latest fundamental research contributions, innovations, and applications in the field of design and analysis of complex robotic mechanical systems, machines, and mechanisms, as presented by leading international researchers at the 2nd USCToMM Symposium on Mechanical Systems and Robotics (USCToMM MSR), held in Rapid City, South Dakota, USA on May 19-21, 2022. It covers highly diverse topics, including soft, wearable and origami robotic systems; applications to walking, flying, climbing, underground, swimming and space systems; human rehabilitation and performance augmentation; design and analysis of mechanisms and machines; human-robot collaborative systems; service robotics; mechanical systems and robotics education; and the commercialization of mechanical systems and robotics. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting and impactful research results that will inspire novel research directions and foster multidisciplinary research collaborations among researchers from around the globe.

The notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems. ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things. The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.

1. Focuses on practical design and manufacturing process 2. Contains Industrial working experiences 3. Includes innovations in development of electric machines 4. Includes read-to-implement solutions in electric machine design 5. Discusses state-of-the-art technology in modern electric machine design

This book presents the entire body of thought of Norbert Wiener (1894-1964), knowledge of which is essential if one wishes to understand and correctly interpret the age in which we live. The focus is in particular on the philosophical and sociological aspects of Wiener's thought, but these aspects are carefully framed within the context of his scientific journey. Important biographical events, including some that were previously unknown, are also highlighted, but while the book has a biographical structure, it is not only a biography. The book is divided into four chronological sections, the first two of which explore Wiener's development as a philosopher and logician and his brilliant interwar career as a mathematician, supported by his philosophical background. The third section considers his research during World War II, which drew upon his previous scientific work and reflections and led to the birth of cybernetics. Finally, the radical post-war shift in Wiener's intellectual path is considered, examining how he came to abandon computer science projects and commenced ceaseless public reflections on the new sciences and technologies of information, their social effects, and the need for responsibility in science.

This proceeding includes original and peer-reviewed research papers from the 3rd International Conference on Control, Instrumentation and Mechatronics Engineering (CIM2022). The conference is a virtual conference held on 2-3 March 2022. The topics covered latest work and finding in the area of Control Engineering, Mechatronics, Robotics and Automation, Artificial Intelligence, Manufacturing, Sensor, Measurement and Instrumentation. Moreover, the latest applications of instrumentations, control and mechatronics are provided. Therefore, this proceeding is a valuable material for researchers, academicians, university students and engineers.

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB (R) Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

The book, Transformation in Healthcare with Emerging Technologies, presents healthcare industrial revolution based on service aggregation and virtualisation that can transform the healthcare sector with the aid of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Bigdata and Blockchain. These technologies offer fast communication between doctors and patients, protected transactions, safe data storage and analysis, immutable data records, transparent data flow service, transaction validation process, and secure data exchanges between organizations. Features: * Discusses the Integration of AI, IoT, big data and blockchain in healthcare industry * Highlights the security and privacy aspect of AI, IoT, big data and blockchain in healthcare industry * Talks about challenges and issues of AI, IoT, big data and blockchain in healthcare industry * Includes several case studies It is primarily aimed at graduates and researchers in computer science and IT who are doing collaborative research with the medical industry. Industry professionals will also find it useful.

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

The book Digital Health Transformation with Blockchain and Artificial Intelligence covers the global digital revolution in the field of healthcare sector. The population has been overcoming the COVID-19 period; therefore, we need to establish intelligent digital healthcare systems using various emerging technologies like Blockchain and Artificial Intelligence. Internet of Medical Things is the technological revolution that has included the element of "smartness" in the healthcare industry and also identifying, monitoring, and informing service providers about the patient's clinical information with faster delivery of care services. This book highlights the important issues i.e. (a) How Internet of things can be integrated with the healthcare ecosystem for better diagnostics, monitoring, and treatment of the patients, (b) Artificial Intelligence for predictive and preventive healthcare systems, (c) Blockchain for managing healthcare data to provide transparency, security, and distributed storage, and (d) Effective remote diagnostics and telemedicine approach for developing smart care. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies. Features: Blockchain and internet of things in healthcare systems Secure Digital Health Data Management in Internet of Things Public Perception towards AI-Driven Healthcare Security, privacy issues and challenges in adoption of smart digital healthcare Big data analytics and Internet of things in the pandemic era Clinical challenges for digital health revolution Artificial intelligence for advanced healthcare Future Trajectory of Healthcare with Artificial Intelligence 9 Parkinson disease pre-diagnosis using smart technologies Emerging technologies to combat the COVID-19 Machine Learning and Internet of Things in Digital Health Transformation Effective Remote Healthcare and Telemedicine Approaches Legal implication of blockchain technology in public health This Book on "Digital Health Transformation with Blockchain and Artificial Intelligence" aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of secured healthcare data analytics. It can also be used as a textbook for a Masters course in security and biomedical engineering. This book will also present new methods for the medical data analytics, blockchain technology, and diagnosis of different diseases to improve the quality of life in general, and better integration into digital healthcare.

This book focuses on the design of efficient & dynamic methods to allocate divisible resources under various auction mechanisms, discussing their applications in power & microgrid systems and the V2G & EV charging coordination problems in smart grids. It describes the design of dynamic methods for single-sided and double-sided auction games and presents a number of simulation cases verifying the performances of the proposed algorithms in terms of efficiency, convergence and computational complexity. Further, it explores the performances of certain auction mechanisms in a hierarchical structure and with large-scale agents, as well as the auction mechanisms for the efficient allocation of multi-type resources. Lastly, it generalizes the main and demonstrates their application in smart grids. This book is a valuable resource for researchers, engineers, and graduate students in the fields of optimization, game theory, auction mechanisms and smart grids interested in designing dynamic auction mechanisms to implement optimal allocation of divisible resources, especially electricity and other types of energy in smart grids.

The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges.

Explains concepts of Internet of Everything problems, research challenge goals, and vision in smart cities Based on the most recent innovations, and covering the major challenges in smart cities, between IoT and Big Data Examines security issues and challenges related to data-intensive advances in IoT Addresses the total information science challenges in Internet of Everything enabled technologies Covers the exploring and creating IoT environment related self-adaptive systems