Dieses Werk, das sich umfassend mit der Einfuhrung von maschinellem Lernen, KI und dem IoT im Gesundheitswesen beschaftigt, richtet sich an Forschende, Fachkrafte im Gesundheitswesen, Wissenschaftler und Technologen. Die Nutzung von maschinellem Lernen und kunstlicher Intelligenz im Internet der Dinge (IoT) fur Anwendungen im Gesundheitswesen sowie die damit einhergehenden Herausforderungen werden ausfuhrlich eroertert. Das IoT erzeugt gewaltige Datenmengen von unterschiedlicher Qualitat. Die intelligente Verarbeitung und Analyse dieser Datenmengen sind der Schlussel zur Entwicklung intelligenter IoT-Anwendungen, wodurch Raum fur die Nutzung des maschinellen Lernens (ML) geschaffen wird. Mit ihren Recheninstrumenten, die bei der Erledigung bestimmter Aufgaben die menschliche Intelligenz ersetzen koennen, macht es die kunstliche Intelligenz (KI) moeglich, dass Computer aus Erfahrung lernen, sich an neue Eingaben anpassen und bisher von Menschen durchgefuhrte Aufgaben ubernehmen. Da IoT-Plattformen eine Schnittstelle bieten, um Daten von unterschiedlichen Geraten zusammenzutragen, lassen sie sich leicht mit AI/ML-Systemen verbinden. Vor diesen Hintergrund besteht der Wert der KI in ihrer Fahigkeit, schnell Erkenntnisse aus Daten zu gewinnen, automatisch Muster zu erkennen und Anomalien in den von intelligenten Sensoren und Geraten erzeugten Daten zu erkennen ? aus Angaben zu Temperatur, Druck, Luftfeuchtigkeit, Luftqualitat, Schwingungen und Gerauschen ? die fur eine schnelle Diagnose extrem hilfreich sein koennen.

Advances in Imaging and Electron Physics, Volume 218 merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains. Specific chapters in this release cover Phase retrieval methods applied to coherent imaging, X-ray phase-contrast imaging: a broad overview of some fundamentals, Graphene and borophene as nanoscopic materials for electronics - with review of the physics, and more.

Quantitative Atomic-Resolution Electron Microscopy, Volume 217, the latest release in the Advances in Imaging and Electron Physics series merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy, and the computing methods. Chapters in this release include Statistical parameter estimation theory, Efficient fitting algorithm, Statistics-based atom counting , Atom column detection, Optimal experiment design for nanoparticle atom-counting from ADF STEM images, and more.

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.

Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer's disease and Parkinson's disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level.

Intelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning. This book addresses key problems such as attrition and lack of engagement in MOOCs and online learning in general. This book explores the state of the art of artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the various challenges of current e-learning in general and MOOC education. In particular, Intelligent Systems and Learning Data Analytics in Online Education shares stimulating theoretical and practical research from leading international experts. This publication provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners to evaluate and apply.

Cognitive Computing for Human-Robot Interaction: Principles and Practices explores the efforts that should ultimately enable society to take advantage of the often-heralded potential of robots to provide economical and sustainable computing applications. This book discusses each of these applications, presents working implementations, and combines coherent and original deliberative architecture for human-robot interactions (HRI). Supported by experimental results, it shows how explicit knowledge management promises to be instrumental in building richer and more natural HRI, by pushing for pervasive, human-level semantics within the robot's deliberative system for sustainable computing applications. This book will be of special interest to academics, postgraduate students, and researchers working in the area of artificial intelligence and machine learning. Key features: Introduces several new contributions to the representation and management of humans in autonomous robotic systems; Explores the potential of cognitive computing, robots, and HRI to generate a deeper understanding and to provide a better contribution from robots to society; Engages with the potential repercussions of cognitive computing and HRI in the real world.

Up-to-date coverage of every topic on the CEH v11 exam This effective self-study guide covers 100% of the EC Council's Certified Ethical Hacker Version 11 exam objectives. The book discusses the latest ethical hacking tools, techniques, and exploits. Readers will find learning objectives at the beginning of each chapter, step-by-step exercises, exam tips, practice exam questions, and in-depth explanations. An integrated test preparation system based on proven pedagogy, CEH Certified Ethical Hacker All-in-One Exam Guide, Fifth Edition covers all five phases of ethical hacking: reconnaissance, gaining access, enumeration, maintaining access, and covering tracks. Readers will learn about malware, hacking Web applications and mobile platforms, cloud computing vulnerabilities, and more. Designed to help candidates pass the exam with ease, this authoritative resource also serves as an essential on-the-job reference. Complete coverage of all CEH v11 exam objectives Includes online access to the Total Tester customizable practice exam software containing 300 practice questions Written by an experienced educator with more than 20 years of experience in the field

Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl's work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

Quantum computing is radically different from the conventional approach of transforming bits strings from one set of 0's and 1's to another. With quantum computing, everything changes. The physics that we use to understand bits of information and the devices that manipulate them are totally different. The way in which we build such devices is different, requiring new materials, new design rules and new processor architectures. Finally, the way we program these systems is entirely different. Quantum engineering is a revolutionary approach to quantum technology. It encompasses both fundamental physics and the broad engineering skill-set necessary to meet the practical challenges of the future. The proposed book will cover the high-quality reviewed book chapters on original research & innovations and compelling insights in Quantum Computing and Engineering. Data scientists, Engineers, Industry, researchers and students working in the field of quantum computing and its allied research will benefit greatly from this publication.

Each Student Book and ActiveBook have has clearly laid out pages with a range of supportive features to aid learning and teaching: Getting to know your unit sections ensure learners understand the grading criteria and unit requirements. Getting ready for Assessment sections focus on preparation for external assessment with guidance for learners on what to expect. Hints and tips will help them prepare for assessment and sample answers are provided for a range of question types including, short and long answer questions, all with a supporting commentary. Learners can also prepare for internal assessment using this feature. A case study of a learner completing the internal assessment for that unit covering 'How I got started', 'How I brought it all together' and 'What I got from the experience'. Pause Point feature provide opportunities for learners to self-evaluate their learning at regular intervals. Each Pause Point point feature gives learners a Hint or Extend option to either revisit and reinforce the topic or to encourage independent research or study skills. Case Study and Theory into Practice features enable development of problem-solving skills and place the theory into real life situations learners could encounter. Assessment Activity/Practice provide scaffolded assessment practice activities that help prepare learners for assessment. Within each assessment practice activity, a Plan, Do and Review section supports learners' formative assessment by making sure they fully understand what they are being asked to do, what their goals are and how to evaluate the task and consider how they could improve. Dedicated Think Future pages provide case studies from the industry, with a focus on aspects of skills development that can be put into practice in a real work environment and further study.

Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts.

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.

The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.

Image Processing for Automated Diagnosis of Cardiac Diseases highlights current and emerging technologies for the automated diagnosis of cardiac diseases. It presents concepts and practical algorithms, including techniques for the automated diagnosis of organs in motion using image processing. This book is suitable for biomedical engineering researchers, engineers and scientists in research and development, and clinicians who want to learn more about and develop advanced concepts in image processing to overcome the challenges of automated diagnosis of heart disease.

Special Forces are a key component of every modern army, capable of carrying out clandestine operations, reconnaissance, and incisive attacks behind enemy lines. Units such as the British SAS, US Navy SEALs, the US Army’s Delta Force, Polish GROM and the France’s National Gendarmerie Intervention Group are famous for their bravery and formidable record. Aircraft are a key element of their functionality, without which Special Forces would not be able to move quickly to the combat zone. Arranged into chapters divided by transports, gunships, helicopters, and unmanned aerial vehicles, the book includes the AC-130H gunship, which can be armed with weapons such as the M61 Vulcan rotary cannon and can destroy ground targets from a range of 2,000 metres; the CV-22 Osprey tiltrotor aircraft, which can land large numbers of men and material in tight spaces because of its STOL capabilities; the Eurocopter AS365 Dauphin II, used by the British Joint Special Forces Aviation Wing (JSFAW) for the insertion of small units behind enemy lines; and the Mil Mi- 171Sh Storm rotorcraft, used by the Russian Spetsnaz commandos for operations in difficult terrain. Illustrated with 140 photographs and artworks, Aircraft of the Special Forces is a dynamic guide to the specialist aircraft and UAVs deployed by Special Forces throughout the world today.

The study of cyberspace is relatively new within the field of social sciences, yet interest in the subject is significant. Conflicts, Crimes and Regulations in Cyberspace contributes to the scientific debate being brought to the fore by addressing international and methodological issues, through the use of case studies. This book presents cyberspace as a socio-technical system on an international level. It focuses on state and non-state actors, as well as the study of strategic concepts and norms. Unlike global studies, the socio-technical approach and "meso" scale facilitate the analysis of cyberspace in international relations. This is an area of both collaboration and conflict for which specific modes of regulation have appeared.

Gamification is being used everywhere; despite its apparent plethora of benefits, the unbalanced use of its main mechanics can end up in catastrophic results for a company or institution. Currently, there is a lack of knowledge of what it is, leading to its unregulated and ad hoc use without any prior planning. This unbalanced use prejudices the achievement of the initial goals and impairs the user's evolution, bringing potential negative reflections. Currently, there are few specifications and modeling languages that allow the creation of a system of rules to serve as the basis for a gamification engine. Consequently, programmers implement gamification in a variety of ways, undermining any attempt at reuse and negatively affecting interoperability. Next-Generation Applications and Implementations of Gamification Systems synthesizes all the trends, best practices, methodologies, languages, and tools that are used to implement gamification. It also discusses how to put gamification in action by linking academic and informatics researchers with professionals who use gamification in their daily work to disseminate and exchange the knowledge, information, and technology provided by the international communities in the area of gamification throughout the 21st century. Covering topics such as applied and cloud gamification, chatbots, deep learning, and certifications and frameworks, this book is ideal for programmers, computer scientists, software engineers, practitioners of technological companies, managers, academicians, researchers, and students.

Artificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathematics to stimulate novel research directions based on the state-of-the-art in AI and smart robotics. Organized by application into ten chapters, this book offers a practical tool for researchers and engineers looking for new avenues and use-cases that combine AI with smart robotics. As we witness exponential growth in automation and the rapid advancement of underpinning technologies, such as ubiquitous computing, sensing, intelligent data processing, mobile computing and context aware applications, this book is an ideal resource for future innovation.

Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.