The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers

Ultra-wideband Radio Frequency Identification Systems describes the essentials of radio frequency identification (RFID)systems as well as their target markets. The book covers a study of commercially available RFID systems and characterizes their performance in terms of read range and reliability in the presence of conductive and dielectric materials. The capabilities and limitations of commercial RFID systems are reported followed by comprehensive discussions of the advantages and challenges of using ultra-wideband (UWB) technology for tag/reader communications. The book presents practical aspects of RFID system such as: EPC global and ISO standards, implementation, and target markets in a simple and easy to understand language.

Addresses real-world challenges in using AI Covers the entire AI process in a holistic manner Explains the technical issues in an easy to use manner Provides real-world examples of AI enablement Addresses the challenges of complex enterprises, coalitions and consortia Avoids the hype, with balanced perspective on benefits and drawbacks of AI

This book is for everyone interested in systems and the modern practice of engineering. The revolution in engineering and systems that has occurred over the past decade has led to an expansive advancement of systems engineering tools and languages. A new age of information-intensive complex systems has arrived with new challenges in a global business market. Science and information technology must now converge into a cohesive multidisciplinary approach to the engineering of systems if products and services are to be useful and competitive. For the non-specialist and even for practicing engineers, the subject of systems engineering remains cloaked in jargon and a sense of mystery. This need not be the case for any reader of this book and for students no matter what their background is. The concepts of architecture and systems engineering put forth are simple and intuitive. Readers and students of engineering will be guided to an understanding of the fundamental principles of architecture and systems and how to put them into engineering practice. This book offers a practical perspective that is reflected in case studies of real-world systems that are motivated by tutorial examples. The book embodies a decade of research and very successful academic instruction to postgraduate students that include practicing engineers. The material has been continuously improved and evolved from its basis in defence and aerospace towards the engineering of commercial systems with an emphasis on speed and efficiency. Most recently, the concepts, processes, and methods in this book have been applied to the commercialisation of wireless charging for electric vehicles. As a postgraduate or professional development course of study, this book will lead you into the modern practice of engineering in the twenty-first century. Much more than a textbook, though, Essential Architecture and Principles of Systems Engineering challenges readers and students alike to think about the world differently while providing them a useful reference book with practical insights for exploiting the power of architecture and systems.

Provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry Covers how deep learning will help in the diagnosis and the prediction of disease spread Presents a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing Highlights various challenges in applying the deep learning in health care Promotes research in deep learning application in understanding the biomedical process

This book is for everyone interested in systems and the modern practice of engineering. The revolution in engineering and systems that has occurred over the past decade has led to an expansive advancement of systems engineering tools and languages. A new age of information-intensive complex systems has arrived with new challenges in a global business market. Science and information technology must now converge into a cohesive multidisciplinary approach to the engineering of systems if products and services are to be useful and competitive. For the non-specialist and even for practicing engineers, the subject of systems engineering remains cloaked in jargon and a sense of mystery. This need not be the case for any reader of this book and for students no matter what their background is. The concepts of architecture and systems engineering put forth are simple and intuitive. Readers and students of engineering will be guided to an understanding of the fundamental principles of architecture and systems and how to put them into engineering practice. This book offers a practical perspective that is reflected in case studies of real-world systems that are motivated by tutorial examples. The book embodies a decade of research and very successful academic instruction to postgraduate students that include practicing engineers. The material has been continuously improved and evolved from its basis in defence and aerospace towards the engineering of commercial systems with an emphasis on speed and efficiency. Most recently, the concepts, processes, and methods in this book have been applied to the commercialisation of wireless charging for electric vehicles. As a postgraduate or professional development course of study, this book will lead you into the modern practice of engineering in the twenty-first century. Much more than a textbook, though, Essential Architecture and Principles of Systems Engineering challenges readers and students alike to think about the world differently while providing them a useful reference book with practical insights for exploiting the power of architecture and systems.

Metaheuristics for Resource Deployment under Uncertainty in Complex Systems analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided. Theories of modeling and solution techniques are used with uncertainty taken into account and real-world applications used. The authors present modeling and metaheuristics for solving resource deployment problems under uncertainty while the models deployed are related to stochastic programming, robust optimization, fuzzy programming, risk management, and single/multi-objective optimization. The resources are heterogeneous and can be sensors and actuators providing different tasks. Both separate and cooperative coverage of the resources are analyzed. Previous research has generally dealt with one type of resource and considers static and deterministic problems, so the book breaks new ground in its analysis of cooperative coverage with heterogeneous resources and the uncertain and dynamic properties of these resources using metaheuristics. This book will help researchers, professionals, academics, and graduate students in related areas to better understand the theory and application of resource deployment problems and theories of uncertainty, including problem formulations, assumptions, and solution methods.

Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science.

This book focuses on the latest applications of nonlinear approaches in different disciplines of engineering and to a range of scientific problems. For each selected topic, detailed concept development, derivations and relevant knowledge are provided for the convenience of the readers. The topics range from dynamic systems and control to optimal approaches in nonlinear dynamics. The volume further includes invited chapters from world class experts in the field. The selected topics are of great interest in the fields of engineering and physics and this book is ideal for engineers and researchers working in a broad range of practical topics and approaches.

Talks about securing healthcare data using Blockchain and Cryptography Discusses challenges and solutions for Blockchain technology in the healthcare sector Presents digital transformation of the healthcare sector using different technologies Covers the handling of healthcare data/medical records using Blockchain technology Explores managing the medical supply chain using Blockchain technology

Intelligent Support for Computer Science Education presents the authors' research journey into the effectiveness of human tutoring, with the goal of developing educational technology that can be used to improve introductory Computer Science education at the undergraduate level. Nowadays, Computer Science education is central to the concerns of society, as attested by the penetration of information technology in all aspects of our lives; consequently, in the last few years interest in Computer Science at all levels of schooling, especially at the college level, has been flourishing. However, introductory concepts in Computer Science such as data structures and recursion are difficult for novices to grasp. Key Features: Includes a comprehensive and succinct overview of the Computer Science education landscape at all levels of education. Provides in-depth analysis of one-on-one human tutoring dialogues in introductory Computer Science at college level. Describes a scalable, plug-in based Intelligent Tutoring System architecture, portable to different topics and pedagogical strategies. Presents systematic, controlled evaluation of different versions of the system in ecologically valid settings (18 actual classes and their laboratory sessions). Provides a time-series analysis of student behavior when interacting with the system. This book will be of special interest to the Computer Science education community, specifically instructors of introductory courses at the college level, and Advanced Placement (AP) courses at the high school level. Additionally, all the authors' work is relevant to the Educational Technology community, especially to those working in Intelligent Tutoring Systems, their interfaces, and Educational Data Mining, in particular as applied to human-human pedagogical interactions and to user interaction with educational software.

This book describes the latest advances in fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their applications in areas such as: intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems. The book is divided into five main parts. The first part proposes new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications; the second explores new concepts and algorithms in neural networks and fuzzy logic applied to recognition. The third part examines the theory and practice of meta-heuristics in various areas of application, while the fourth highlights diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical contexts. Finally, the fifth part focuses on applications of fuzzy logic, neural networks and meta-heuristics to robotics problems.

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein. Key Features: Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies. Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations. Written by statistical data analysis practitioner for practitioners. The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.

Many governments around the world are calling for the use of biometric systems to provide crucial societal functions, consequently making it an urgent area for action. The current performance of some biometric systems in terms of their error rates, robustness, and system security may prove to be inadequate for large-scale applications to process millions of users at a high rate of throughput. This book focuses on fusion in biometric systems. It discusses the present level, the limitations, and proposed methods to improve performance. It describes the fundamental concepts, current research, and security-related issues. The book will present a computational perspective, identify challenges, and cover new problem-solving strategies, offering solved problems and case studies to help with reader comprehension and deep understanding. This book is written for researchers, practitioners, both undergraduate and post-graduate students, and those working in various engineering fields such as Systems Engineering, Computer Science, Information Technology, Electronics, and Communications.

This book describes different control strategies adapted to heat pumps, at the purpose of increasing energy flexibility in buildings. It reports on the development of both simple rule-based controls (RBC) and advanced model predictive controls (MPC). These are tested and compared in both simulation and experimental setups. The book analyzes in detail all the different steps, including the development and tuning of the controllers, their testing in experimental settings and simulation studies. Bridging between advanced control systems theory concepts and practical needs, and discussing the advantages and main challenges of MPC and RBC controllers in terms of efficiency of heat pump operation, electricity prices, emission values, and users' comfort, this book offers an in-depth evaluation of innovative control strategies applied to energy demand management in buildings.

Control Systems Theory with Engineering Applications uses modern concepts of control systems theory to provide an accessible text for the most important aspects in linear and nonlinear multivariable systems.  This book presents well-defined theoretical base with step-by-step instruction on how to apply it by thoroughly studying a great number of practical real world problems and using numerous examples. The book gives basic introductory theory relating to the linear state space concept.  It provides the reader with the necessary background needed to comprehend the principles and models presented throughout the remaining portions of the book.  The methods and applications presented include a variety of relevant topics selected for their insight and pedagogical effectiveness.   Topics: * A vast range of worked-out examples and qualitative illustrations. * Mathematical model developments including Newtonian Mechanics and Lagrange Equations of Motion * Analysis, identification and control of linear and nonlinear dynamic systems * Practical engineering examples including dynamic modeling in MATLAB and SIMULINK * Theory and applications of multivariable control * Constrained optimization of continuos and discrete time systems. Control Systems Theory with Engineering Applications is an exceptionally complete and accessible text/reference for graduates and professionals in automatic control and nonlinear control.  It is an ideal resource to bridge the gap between theory and practice for students and engineers working in the fields of electrical, mechanical and aerospace engineering.

Future predictions are always a topic of interest. Precise estimates are crucial in many activities as forecasting errors can lead to big financial loss. The sequential analysis of data and information gathered from past to present is call time series analysis. This book covers the recent advancements in time series forecasting. The book includes theoretical as well as recent applications of time series analysis. It focuses on the recent techniques used, discusses a combination of methodology and applications, presents traditional and advanced tools, new applications, and identifies the gaps in knowledge in engineering applications. This book is aimed at scientists, researchers, postgraduate students and engineers in the areas of supply chain management, production, inventory planning, and statistical quality control.

This book presents up-to-date research developments and novel methodologies regarding recursive filtering for 2-D shift-varying systems with various communication constraints. It investigates recursive filter/estimator design and performance analysis by a combination of intensive stochastic analysis, recursive Riccati-like equations, variance-constrained approach, and mathematical induction. Each chapter considers dynamics of the system, subtle design of filter gains, and effects of the communication constraints on filtering performance. Effectiveness of the derived theories and applicability of the developed filtering strategies are illustrated via simulation examples and practical insight. Features:- Covers recent advances of recursive filtering for 2-D shift-varying systems subjected to communication constraints from the engineering perspective. Includes the recursive filter design, resilience operation and performance analysis for the considered 2-D shift-varying systems. Captures the essence of the design for 2-D recursive filters. Develops a series of latest results about the robust Kalman filtering and protocol-based filtering. Analyzes recursive filter design and filtering performance for the considered systems. This book aims at graduate students and researchers in mechanical engineering, industrial engineering, communications networks, applied mathematics, robotics and control systems.

Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis copes with the filter design, fault estimation and reliable control problems for different classes of nonlinear stochastic time-varying systems with network-enhanced complexities. Divided into three parts, the book discusses the finite-horizon filtering, fault estimation and reliable control, and randomly occurring nonlinearities/uncertainties followed by designing of distributed state and fault estimators, and distributed filters. The third part includes problems of variance-constrained H state estimation, partial-nodes-based state estimation and recursive filtering for nonlinear time-varying complex networks with randomly varying topologies, and random coupling strengths. Offers a comprehensive treatment of the topics related to Networked Nonlinear Stochastic Time-Varying Systems with rigorous math foundation and derivation Unifies existing and emerging concepts concerning control/filtering/estimation and distributed filtering Provides a series of latest results by drawing on the conventional theories of systems science, control engineering and signal processing Deal with practical engineering problems such as event triggered H filtering, non-fragile distributed estimation, recursive filtering, set-membership filtering Demonstrates illustrative examples in each chapter to verify the correctness of the proposed results This book is aimed at engineers, mathematicians, scientists, and upper-level students in the fields of control engineering, signal processing, networked control systems, robotics, data analysis, and automation.

Provides insight into the skill set that requires leveraging strength to move further to act as a good data analyst Discusses how big data along with deep learning holds the potential to significantly increase data understanding and in turn, helps to make decisions Covers the numerous potential applications in healthcare, education, communications, media, and the entertainment industry Offers innovative platforms for integrating big data and deep learning Presents issues related to adequate data storage, sematic indexing, data tagging, and fast information retrieval from big data

Modern control theory and in particular state space or state variable methods can be adapted to the description of many different systems because it depends strongly on physical modeling and physical intuition. The laws of physics are in the form of differential equations and for this reason, this book concentrates on system descriptions in this form. This means coupled systems of linear or nonlinear differential equations. The physical approach is emphasized in this book because it is most natural for complex systems. It also makes what would ordinarily be a difficult mathematical subject into one which can straightforwardly be understood intuitively and which deals with concepts which engineering and science students are already familiar. In this way it is easy to immediately apply the theory to the understanding and control of ordinary systems. Application engineers, working in industry, will also find this book interesting and useful for this reason.

In line with the approach set forth above, the book first deals with the modeling of systems in state space form. Both transfer function and differential equation modeling methods are treated with many examples. Linearization is treated and explained first for very simple nonlinear systems and then more complex systems. Because computer control is so fundamental to modern applications, discrete time modeling of systems as difference equations is introduced immediately after the more intuitive differential equation models. The conversion of differential equation models to difference equations is also discussed at length, including transfer function formulations.

A vital problem in modern control is how to treat noise in control systems. Nevertheless this question is rarely treated in many control system textbooks because it is considered to be too mathematical and too difficult in a second course on controls. In this textbook a simple physical approach is made to the description of noise and stochastic disturbances which is easy to understand and apply to common systems. This requires only a few fundamental statistical concepts which are given in a simple introduction which lead naturally to the fundamental noise propagation equation for dynamic systems, the Lyapunov equation. This equation is given and exemplified both in its continuous and discrete time versions.

With the Lyapunov equation available to describe state noise propagation, it is a very small step to add the effect of measurements and measurement noise. This gives immediately the Riccati equation for optimal state estimators or Kalman filters. These important observers are derived and illustrated using simulations in terms which make them easy to understand and easy to apply to real systems. The use of LQR regulators with Kalman filters give LQG (Linear Quadratic Gaussian) regulators which are introduced at the end of the book. Another important subject which is introduced is the use of Kalman filters as parameter estimations for unknown parameters.

The textbook is divided into 7 chapters, 5 appendices, a table of contents, a table of examples, extensive index and extensive list of references. Each chapter is provided with a summary of the main points covered and a set of problems relevant to the material in that chapter. Moreover each of the more advanced chapters (3 - 7) are provided with notes describing the history of the mathematical and technical problems which lead to the control theory presented in that chapter. Continuous time methods are the main focus in the book because these provide the most direct connection to physics. This physical foundation allows a logical presentation and gives a good intuitive feel for control system construction. Nevertheless strong attention is also given to discrete time systems.

Very few proofs are included in the book but most of the important results are derived. This method of presentation makes the text very readable and gives a good foundation for reading more rigorous texts.

A complete set of solutions is available for all of the problems in the text. In addition a set of longer exercises is available for use as Matlab/Simulink laboratory exercises in connection with lectures. There is material of this kind for 12 such exercises and each exercise requires about 3 hours for its solution. Full written solutions of all these exercises are available.

The book explores modern sensor technologies while also discussing security issues, which is the dominant factor for many types of Internet of Things (IoT) applications. It also covers recent (IoT) applications such as the Markovian Arrival Process, fog computing, real-time solar energy monitoring, healthcare, and agriculture. Fundamental concepts of gathering, processing, and analyzing different Artificial Intelligence (AI) models in IoT applications are covered along with recent detection mechanisms for different types of attacks for effective network communication. On par with the standards laid out by international organizations in related fields, the book focuses on both core concepts of IoT along with major application areas. Designed for technical developers, academicians, data scientists, industrial researchers, professionals, and students, this book is useful in uncovering the latest innovations in the field of IoT.

the handbook is a valuable reference to researchers from industry and academia, as well as Masters and PhD students around the globe working in the metaheuristics and applications domain includes contributions from a variety of academics/researchers in the field of metaheuristics

Applied and Computational Control, Signals, and Circuits: Recent Developments is an interdisciplinary book blending mathematics, computational mathematics, scientific computing and software engineering with control and systems theory, signal processing, and circuit simulations. The material consists of seven state-of-the-art review chapters, each written by a leading expert in that field. Each of the technical chapters deals exclusively with some of the recent developments involving applications and computations of control, signals and circuits. Also included is a Chapter focusing on the newly developed Fortran-based software library, called SLICOT, for control systems design and analysis. This collection will be an excellent reference work for research scientists, practicing engineers, and graduate level students of control and systems, circuit design, power systems and signal processing.

This book presents the proceedings of the 17th Chinese Intelligent Systems Conference, held in Fuzhou, China, on Oct 16-17, 2021. It focuses on new theoretical results and techniques in the field of intelligent systems and control. This is achieved by providing in-depth study on a number of major topics such as Multi-Agent Systems, Complex Networks, Intelligent Robots, Complex System Theory and Swarm Behavior, Event-Triggered Control and Data-Driven Control, Robust and Adaptive Control, Big Data and Brain Science, Process Control, Intelligent Sensor and Detection Technology, Deep learning and Learning Control Guidance, Navigation and Control of Flight Vehicles and so on. The book is particularly suited for readers who are interested in learning intelligent system and control and artificial intelligence. The book can benefit researchers, engineers, and graduate students.