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.

Articulates a series of scientific and technological developments described in a didactic way Shows a broad view of biometrics in several contexts: Current cases of study in five different areas Provides experimental results in different environments (availability of data and computer code to reproduce the experiments) Includes transdisciplinary approaches and discussions for each chapter Innovation as part of the final cases of study: developments are already affecting the new businesses of the digital age and the era of the brain

Includes case studies illustrating the business processes that underlines the use of big data and health analytics to improve healthcare delivery Discusses AI based smart paradigms for reliable predictions of infectious disease dynamics which can help or prevent disease transmission Highlights the different aspects of using extended reality for diverse healthcare applications and aggregates the current state of research Offers intelligent models of the smart recommender system for personal well-being services and computer-aided drug discovery and design methods Presents novel innovative techniques for extracting user social behavior known as sentiment analysis for healthcare related purposes

The aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises nine chapters. Chapter 1 provides a theoretical introduction of the computational optimization techniques regarding the gradient-based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. Chapter 2, discusses evolutionary computation techniques and genetic algorithm. Swarm intelligence theory and particle swarm optimization algorithm are reviewed in Chapter 3. Also, several variations of particle swarm optimization algorithm are analysed and explained such as Geometric PSO, PSO with mutation, Chaotic PSO with mutation, multi-objective PSO and Quantum mechanics - based PSO algorithm. Chapter 4 deals with two essential colony bio-inspired algorithms: Ant colony optimization (ACO) and Artificial bee colony (ABC). Chapter 5, presents and analyses Cuckoo search and Bat swarm algorithms and their latest variations. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO) and their improved algorithm modifications. The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and other algorithm variations such as binary and chaotic versions. Chapter 8 presents machine learning applications of swarm and evolutionary algorithms. Illustrative real-world examples are presented with real datasets regarding neural network optimization and feature selection, using: genetic algorithm, Geometric PSO, Chaotic Harmony Search, Chaotic Cuckoo Search, and Evolutionary Algorithm and also crime forecasting using swarm optimized SVM. In chapter 9, applications of swarm intelligence on deep long short-term memory (LSTM) networks and Deep Convolutional Neural Networks (CNNs) are discussed, including LSTM hyperparameter tuning and Covid19 diagnosis from chest X-Ray images. The aim of the book is to present and discuss several state-of-theart swarm intelligence and evolutionary algorithms together with their variances and also several illustrative applications on machine learning and deep learning.

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.

This book is about the behaviour of systems. Systems are important, for we interact with them all the time, and many of the actions we take are influenced by a system - for example, the system of performance measures in an organisation influences, often very strongly, how individuals within that organisation behave. Furthermore, sometimes we are involved in the design of systems, as is any manager contributing to the definition of what those performance measures might be. That manager will want to ensure that all the proposed performance measures will drive the 'right' behaviours rather than (inadvertently) encouraging dysfunctional 'game playing', and so anticipating how the performance measurement system will work in practice is a vital part of a wise design process. Some of the systems with which we interact are local, such as your organisation's performance measurement system. Some systems, however, are distant, but nonetheless very real, such as the healthcare system, the education system, the legal system and the climate system. Systems, therefore, exist on all scales, from the local to the global. And all systems are complex, some hugely so. That's why understanding how systems behave can be very helpful. Systems are complex for two main reasons. First, the manner in which they behave over time can be very hard to anticipate - and anticipating the future sensibly is of course a key objective of management. Second, the 'entities' within a system can be connected together in very complex ways, so that an intervention 'here' can result in an effect 'there', perhaps a long time afterward. Sometimes this can be surprising, and so we talk of 'unintended consequences' - but this is of course a euphemism for 'because I didn't understand how this system behaves, I had not anticipated that'. Systems thinking, the subject matter of this book, is the disciplined study of systems, and causal loop diagrams - the 'pictures' of this 'picture book' - are a very insightful way to represent the connectedness of the entities from which any system is composed, so taming that system's complexity.

This book allows students to learn fundamental concepts in linear circuit analysis using a well-developed methodology that has been carefully refined through classroom use. Applying his many years of teaching experience, the author focuses the reader's attention on basic circuit concepts and modern analysis methods. The text includes detailed coverage of basics of different terminologies used in electric circuits, mesh and node equations, network analysis and network theorems, signals and its properties, graph theory and its application in circuit analysis, analogous systems, Fourier and Laplace transforms and their applications in circuit theory.Wide coverage of evolution integral, two-port networks, passive and active filters, state variable formulation of network problems and network synthesis have been made. Transient response and frequency domain analysis of network systems has also been discussed. The hall-mark feature of this text is that it helps the reader to gain a sound understanding on the basics of circuit theory.

Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area-including healthcare, finance, manufacturing, and marketing-could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.

Reviews different machine learning and deep learning techniques with a biomedical perspective Provides the relevant case studies that demonstrate applicability of different AI techniques Explain different kinds of inputs like various image modalities, biomedical signals types, etc. Covers the latest trends of AI-based biomedical domains including IoT, drug discovery, biomechanics, robotics, electronic health records, etc. Discusses the research challenges and opportunities in AI and biomedical domain

This book focuses on the design methodologies of various quantum circuits, DNA circuits, DNA-quantum circuits and quantum-DNA circuits. It considers the merits and challenges of multivalued logic circuits in quantum, DNA, quantum-DNA and DNA-quantum computing. Multiple-Valued Computing in Quantum Molecular Biology: Arithmetic and Combinational Circuits is Volume 1 of a two-volume set. From fundamentals to advanced levels, this book discusses different multiple-valued logic DNA-quantum and quantum-DNA circuits. The text consists of four parts. Part I introduces multiple-valued quantum computing and DNA computing. It contains the basic understanding of multiple-valued quantum computing, multiple-valued DNA computing, multiple-valued quantum-DNA computing and multiple-valued DNA-quantum computing. Part II examines heat calculation, speed calculation, heat transfer, data conversion and data management in multi-valued quantum, DNA, quantum-DNA and DNA-quantum computing. Part III discusses multiple-valued logic operations in quantum and DNA computing such as ternary AND, NAND, OR, NOR, XOR, XNOR and multiple-valued arithmetic operations such as adder, multiplier, divider and more. Quantum-DNA and DNA-quantum multiple-valued arithmetic operations are also explained in this section. Part IV explains multiple-valued quantum and DNA combinational circuits such as multiple-valued DNA-quantum and quantum-DNA multiplexer, demultiplexer, encoder and decoder. This book will be of great help to researchers and students in quantum computing, DNA computing, quantum-DNA computing and DNA-quantum computing researchers.

Discusses various design aspects of multiple-valued logic DNA-quantum and quantum-DNA sequential circuits, memory devices, programmable logic devices and nano-processors Presents how multiple-valued quantum, DNA, quantum-DNA and DNA-quantum nano processors are designed with algorithms Examines the architecture and design procedure of memory devices such as Random Access Memory (RAM), Read Only Memory (ROM) Reviews the designs and algorithms of Multiple-valued quantum, DNA, quantum-DNA and DNA-quantum nano processors.

The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: Provides a smart traffic congestion avoidance system with an integrated fuel consumption model. Predicts traffic in short-term and regular. This is illustrated with a case study. Efficient Traffic light controller and deviation system in accordance with the traffic scenario. IoT based Intelligent Transport Systems in a Global perspective. Intelligent Traffic Light Control System and Ambulance Control System. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. Bunch of solutions and ideas for smart traffic development in smart cities. This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications. This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.

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.

The Lean Approach to Digital Transformation: From Customer to Code and From Code to Customer is organized into three parts that expose and develop the three capabilities that are essential for a successful digital transformation: 1. Understanding how to co-create digital services with users, whether they are customers or future customers. This ability combines observation, dialogue, and iterative experimentation. The approach proposed in this book is based on the Lean Startup approach, according to an extended vision that combines Design Thinking and Growth Hacking. Companies must become truly "customer-centric", from observation and listening to co-development. The revolution of the digital age of the 21st century is that customer orientation is more imperative -- the era of abundance, usages rate of change, complexity of experiences, and shift of power towards communities -- are easier, using digital tools and digital communities. 2. Developing an information system (IS) that is the backbone of the digital transformation - called "exponential information system" to designate an open IS (in particular on its borders), capable of interfacing and combining with external services, positioned as a player in software ecosystems and built for processing scalable and dynamic data flows. The exponential information system is constantly changing and it continuously absorbs the best of information processing technology, such as Artificial Intelligence and Machine Learning. 3. Building software "micro-factories" that produce service platforms, which are called "Lean software factories." This "software factory" concept covers the integration of agile methods, tooling and continuous integration and deployment practices, a customer-oriented product approach, and a platform approach based on modularity, as well as API-based architecture and openness to external stakeholders. This software micro-factory is the foundation that continuously produces and provides constantly evolving services. These three capabilities are not unique or specific to this book, they are linked to other concepts such as agile methods, product development according to lean principles, software production approaches such as CICD (continuous integration and deployment) or DevOps. This book weaves a common frame of reference for all these approaches to derive more value from the digital transformation and to facilitate its implementation. The title of the book refers to the "lean approach to digital transformation" because the two underlying frameworks, Lean Startup and Lean Software Factory, are directly inspired by Lean, in the sense of the Toyota Way. The Lean approach is present from the beginning to the end of this book -- it provides the framework for customer orientation and the love of a job well done, which are the conditions for the success of a digital transformation.

Artificial Intelligence in Information and Communication Technologies, Healthcare and Education: A Roadmap Ahead is designed as a reference text and discusses inter-dependability, communication and effective control for the betterment of services through artificial intelligence (AI), as well as the challenges and path ahead for AI in computing and control across different domains of business and human life. The book accommodates technologies and application domains including backbone hardware, systems and methods for deployment, which help incorporating intelligence through different supervised and probabilistic learning approaches. Features The book attempts to establish a connection between hardware, software technologies and algorithmic intelligence for data analysis and decision support in domains such as healthcare, education and other aspects of business and mobility. It presents various recent applications of artificial intelligence in information and communication technologies such as search and optimization methods, machine learning, data representation and ontologies, and multi-agent systems. The book provides a collection of different case studies with experimentation results than mere theoretical and generalized approaches. Covers most of the applications using the trending technologies like machine learning (ML), data science (DS), Internet of Things (IoT), and underlying information and communication technologies. The book is aimed primarily at advanced undergraduates and postgraduate students studying computer science, computer applications, and information technology. Researchers and professionals will also find this book useful.

This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and solutions of using advanced techniques like wearable technologies and image processing in the sector. Features: Focuses on the use of artificial intelligence (AI) in healthcare with issues, applications, and prospects Presents the application of artificial intelligence in medical imaging, fractionalization of early lung tumour detection using a low intricacy approach, etc Discusses an artificial intelligence perspective on wearable technology Analyses cardiac dynamics and assessment of arrhythmia by classifying heartbeat using electrocardiogram (ECG) Elaborates machine learning models for early diagnosis of depressive mental affliction This book serves as a reference for students and researchers analyzing healthcare data. It can also be used by graduate and post graduate students as an elective course.

This book looks at how to design complex products that have many components with intricate relationships and requirements. It also discusses how to manage processes involved in their lifecycle, from concept generation to disposal, with the objectives of increasing customer satisfaction, quality, safety, and usability and meeting program timings and budgets. Part I covers systems engineering concepts, issues, and bases in product design. Part II examines quality, human factors, and safety engineering approaches. Part III describes important tools and methods used in these fields, and Part IV includes other relevant integration topics, interesting applications of useful techniques, and observations from a few "landmark" product development case studies.

- the author is in the BIMA Hall of Fame and is Chief Technology & Innovation Officer at Ernst & Young - the book explains the current state of AI and how it is governed, as well as detailing five potential futures involving AI and providing a clear Roadmap to manage the future of AI - easy and fun to read

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis. Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This book is for anyone who wants to gain an understanding of Blockchain technology and its potential. The book is research-oriented and covers different verticals of Blockchain technology. It discusses the characteristics and features of Blockchain, includes techniques, challenges, and future trends, along with case studies for deeper understanding. Blockchain Technology: Exploring Opportunities, Challenges, and Applications covers the core concepts related to Blockchain technology starting from scratch. The algorithms, concepts, and application areas are discussed according to current market trends and industry needs. It presents different application areas of industry and academia and discusses the characteristics and features of this technology. It also explores the challenges and future trends and provides an understanding of new opportunities. This book is for anyone at the beginner to intermediate level that wants to learn about the core concepts related to Blockchain technology.

Unmatched review prep for the MRI Certification LANGE Review: MRI Clinical Concepts and Imaging Applications Manual with Registry Review is a critical resource for radiologic technologists to successfully perform MRI examinations and prepare for the national registry examination to become MRI technologists. Organized into three parts, the book addresses everything from patient preparation to clinical applications of performing the MRI procedure to review questions based on the national registry examination. You'll learn about performing MRI examinations and obtaining the correct imaging plane for the brain, head, spine, chest, abdomen, pelvis, and musculoskeletal system, plus patient/part positioning, scan range, slice alignment and protocol variations to accommodate unique conditions and pathologies. More than 200 multiple-choice questions help prepare you for the national registry examination. Essential resource for National Registry exam prep Comprehensive, methodical coverage learning Tailored for those enrolled in an MRI program, specifically for individuals completing a clinical internship

Franco's "Design with Operational Amplifiers and Analog Integrated Circuits, 4e" combines theory with real-life applications to deliver a straightforward look at analog design principles and techniques. An emphasis on the physical picture helps the student develop the intuition and practical insight that are the keys to making sound design decisions.is The book is intended for a design-oriented course in applications with operational amplifiers and analog ICs. It also serves as a comprehensive reference for practicing engineers. This new edition includes enhanced pedagogy (additional problems, more in-depth coverage of negative feedback, more effective layout), updated technology (current-feedback and folded-cascode amplifiers, and low-voltage amplifiers), and increased topical coverage (current-feedback amplifiers, switching regulators and phase-locked loops).

The insertion of communication networks in feedback control loops complicates analysis and synthesis of cyber-physical systems (CPSs), and network-induced uncertainties may degrade system control performance. Thus, this book researches networked delay compensation and event-triggered control approaches for a series of CPSs subject to network-induced uncertainties. The authors begin with an introduction to the concepts and challenges of CPSs, followed by an overview of networked control approaches and event-triggered control strategies in CPSs. Then, networked delay compensation and event-triggered control approaches are proposed for CPSs with network communication delay, data dropout, signal quantization, and event-triggered communication. More specifically, networked delay compensation approaches are proposed for linear/nonlinear networked controlled plants with time-varying and random network communication delays and data dropouts. To reduce computational burden and network communication loads in CPSs, event-triggered control, self-triggered control, co-design of event-triggered control and quantized control techniques, and event-triggered disturbance rejection control approaches are also presented. This book is an essential text for researchers and engineers interested in cybersecurity, networked control, and CPSs. It would also prove useful for graduate students in the fields of science, engineering, and computer science.

As its name implies, the aim of Systems Design and Engineering: Facilitating Multidisciplinary Development Projects is to help systems engineers develop the skills and thought processes needed to successfully develop and implement engineered systems. Such expertise typically does not come through study but from action, hard work, and cooperation. To that end, the authors have chosen a "hands-on" approach for presenting material rather than concentrating on theory, as so often is the case in a classroom setting. This attractive and accessible text is a mix of theory and practical approach, illustrated with examples that have enough richness and variability to hold your attention. Models are presented for controlling the design, change, and engineering processes. Various aspects of systems engineering and methods providing the big picture at system level are discussed. In some ways, you can think of the book as a compact "starter's kit" for systems engineers. Although the authors are recognized experts in academic settings, they attribute much of their success in systems engineering to their own hands-on experiences and want to show you how to achieve that same level of expertise. Simply reading this book or any other book will not suffice for the learning process to become a systems engineer - no book will do that. However, by following the principles laid out in this book, you can develop the necessary skills and expertise to help you start an interesting, challenging, and rewarding career as a systems engineer.