This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

COGNITIVE COMPUTING MODELS IN COMMUNICATION SYSTEMS A concise book on the latest research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, AI-based devices, computer technology, and their solutions. The book provides a comprehensive overview of state-of-the-art research work on cognitive models in communication systems and computing techniques. It also bridges the gap between various communication systems and solutions by providing the current models and computing techniques, their applications, the strengths and limitations of the existing methods, and the future directions in this area. The contributors showcase their latest research work focusing on the issues, challenges, and solutions in the field of data transmission techniques, computational algorithms, artificial intelligence (AI)-based devices, and computing techniques. Readers will find in this succinctly written and unique book: Topics covering the applications of advanced cognitive devices, models, architecture, and techniques. A range of case studies and applications that will provide readers with the tools to apply cutting-edge models and algorithms. In-depth information about new cognitive computing models and conceptual frameworks and their implementation. Audience The book is designed for researchers and electronics engineers, computer science engineers, industrial engineers, and mechanical engineers (both in academia and industry) working in the fields of machine learning, cognitive computing, mobile communication, and wireless network system.

utilizes the Consolidated Serial Homicide Offender Database, one of the largest and most robust open access databases of multiple murders available illustrated with in-depth case studies of SHOs, such as Felix Vail, Michael Sumpter, the Seminole Heights Killer, and the Austin Bomber provides commentary from those who have used these patterning methods in practice, in addition to laying out how to put the current suite of data tools to use within organizations

REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of "decision, information, decision, information," are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a "diary problem" that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jagare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you'll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world--and not just the lab--Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.

Artificial Intelligence for Autonomous Networks introduces the autonomous network by juxtaposing two unique technologies and communities: Networking and AI. The book reviews the technologies behind AI and software-defined network/network function virtualization, highlighting the exciting opportunities to integrate those two worlds. Outlining the new frontiers for autonomous networks, this book highlights their impact and benefits to consumers and enterprise customers. It also explores the potential of the autonomous network for transforming network operation, cyber security, enterprise services, 5G and IoT, infrastructure monitoring and traffic optimization, and finally, customer experience and care. With contributions from leading experts, this book will provide an invaluable resource for network engineers, software engineers, artificial intelligence, and machine learning researchers.

Privacy: Algorithms and Society focuses on encryption technologies and privacy debates in journalistic crypto-cultures, countersurveillance technologies, digital advertising, and cellular location data. Important questions are raised such as: How much information will we be allowed to keep private through the use of encryption on our computational devices? What rights do we have to secure and personalized channels of communication, and how should those be balanced by the state's interests in maintaining order and degrading the capacity of criminals and rival state actors to organize through data channels? What new regimes may be required for states to conduct digital searches, and how does encryption act as countersurveillance? How have key debates relied on racialized social constructions in their discourse? What transformations in journalistic media and practices have occurred with the development of encryption tools? How are the digital footprints of consumers tracked and targeted? Scholars and students from many backgrounds as well as policy makers, journalists, and the general reading public will find a multidisciplinary approach to questions of privacy and encryption encompassing research from Communication, Sociology, Critical Data Studies, and Advertising and Public Relations.

The leading thinkers from the cognitive science tradition participated in a workshop sponsored by Sandia National Laboratories in July of 2003 to discuss progress in building their models. The goal was to summarize the theoretical and empirical bases for cognitive systems and to present exemplary developments in the field. Following the workshop, a great deal of planning went into the creation of this book. Eleven of the twenty-six presenters were asked to contribute chapters, and four chapters are the product of the breakout sessions in which critical topics were discussed among the participants. An introductory chapter provides the context for this compilation.
"Cognitive Systems" thus presents a unique merger of cognitive modeling and intelligent systems, and attempts to overcome many of the problems inherent in current expert systems. It will be of interest to researchers and students in the fields of cognitive science, computational modeling, intelligent systems, artificial intelligence, and human-computer interaction.

Originally published in 1989 The Industrialization of Intelligence is a spirited blend of the principles of social science and computer technology. Critically praised in the United States and England by leading lights both literary and technological, it develops an original and provocative model of the interplay between computer systems and social systems. Noah Kennedy has composed a compelling story from key episodes in the development of the computer, and coupled it with a probing analysis of the true role of automation in modem society. The result firmly plants computer technology in the soil of western culture and denies the shrill claims that the information age represents a sudden break with the historical past. He starts with biographical vignettes from the lives of five pivotal thinkers, weaving their crucial insights into the larger fabric of contemporary and future society. He then gives a provocative forecast of the role of artificial intelligence in future society, and examines the probable impact of new computer technologies on employment and on the relationships between nations. The result is a reasoned understanding of our imminent future through a thoughtful analysis of our historical past.

Resulting from the need for greater realism in models of human and organizational behavior in military simulations, there has been increased interest in research on integrative models of human performance, both within the cognitive science community generally, and within the defense and aerospace industries in particular. This book documents accomplishments and lessons learned in a multi-year project to examine the ability of a range of integrated cognitive modeling architectures to explain and predict human behavior in a common task environment that requires multi-tasking and concept learning. This unique project, called the Agent-Based Modeling and Behavior Representation (AMBR) Model Comparison, involved a series of human performance model evaluations in which the processes and performance levels of computational cognitive models were compared to each other and to human operators performing the identical tasks. In addition to quantitative data comparing the performance of the models and real human performance, the book also presents a qualitatively oriented discussion of the practical and scientific considerations that arise in the course of attempting this kind of model development and validation effort. The primary audiences for this book are people in academia, industry, and the military who are interested in explaining and predicting complex human behavior using computational cognitive modeling approaches. The book should be of particular interest to individuals in any sector working in Psychology, Cognitive Science, Artificial Intelligence, Industrial Engineering, System Engineering, Human Factors, Ergonomics and Operations Research. Any technically or scientifically oriented professional or student should find the material fully accessible without extensive mathematical background.

This reference text discusses intelligent robotic and drone technology with embedded Internet of Things (IoT) for smart applications. The text discusses future directions of optimization methods with various engineering and science fundamentals used in robotics and drone-based applications. Its emphasis is on covering deep learning and similar models of neural network-based learning techniques employed in solving optimization problems of different engineering and science applications. It covers important topics including sensors and actuators in the internet of things (IoT), internet-of-robotics-things (IoRT), IoT in agriculture and food processing, routing challenges in flying Ad-hoc networks, and smart cities. The book will serve as a useful text for graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and mechanical engineering.

If you are curious about the basics of artificial intelligence, blockchain technology, and quantum computing as key enablers for digital transformation and innovation, Digital Fluency is your handy guide. The real-world applications of these cutting-edge technologies are expanding rapidly, and your daily life will continue to be affected by each of them. There is no better time than now to get started and become digitally fluent. You need not have previous knowledge of these versatile technologies, as author Volker Lang will expertly guide you through this digital age. He illustrates key concepts and applications in numerous practical examples and more than 48 catchy figures throughout Digital Fluency. The end of each chapter presents you with a helpful implementation checklist of central lessons before proceeding to the next. This book gets to the heart of digital buzzwords and concepts, and tells you what they truly mean. Breaking down topics such as automated driving and intelligent robotics powered by artificial intelligence, blockchain-based cryptocurrencies and smart contracts, drug development and optimization of financial investment portfolios by quantum computing, and more is imperative to being ready for what the future of industry holds. Whether your own digital transformation journey takes place within your private or public organization, your studies, or your individual household, Digital Fluency maps out a concrete digital action plan for all of your technology and innovation strategy needs. What You Will Learn Gain guidance in the digital age without requiring any previous knowledge about digital technologies and digital transformation Get acquainted with the most popular current and prospective applications of artificial intelligence, blockchain technology, and quantum computing across a wide range of industries including healthcare, financial services, and the automobile industry Become familiar with the digital innovation models of Amazon, Google, Microsoft, IBM, and other world-leading organizations Implement your own digital transformation successfully along the eight core dimensions of a concrete digital action plan Who This Book Is ForThought-leaders, business executives and industry strategists, management and strategy consultants, politicians and policy makers, entrepreneurs, financial analysts, investors and venture capitalists, students and research scientists, as well as general readers, who want to become digitally fluent.

The book gathers the chapters of Cognitive InfoCommunication research relevant to a variety of application areas, including data visualization, emotion expression, brain-computer interfaces or speech technologies. It provides an overview of the kind of cognitive capabilities that are being analyzed and developed. Based on this common ground, it may become possible to see new opportunities for synergy among disciplines that were heretofore viewed as being separate. Cognitive InfoCommunication begins by modeling human cognitive states and aptitudes in order to better understand what the user of a system is capable of comprehending and doing. The patterns of exploration and the specific tools that are described can certainly be of interest and of great relevance for all researchers who focus on modeling human states and aptitudes. This innovative research area provides answers to the latest challenges in influence of cognitive states and aptitudes in order to facilitate learning or generally improve performance in certain cognitive tasks such as decision making. Some capabilities are purely human, while others are purely artificial, but in general this distinction is rarely clear-cut. Therefore, when discussing new human cognitive capabilities, the technological background which makes them possible cannot be neglected, and indeed often plays a central role. This book highlights the synergy between various fields that are perfectly fit under the umbrella of CogInfoCom and contribute to understanding and developing new, human-artificial intelligence hybrid capabilities. These, merged capabilities are currently appearing, and the importance of the role they play in everyday life are unique to the cognitive entity generation that is currently growing up.

This book is a collection of papers presented during the 8th World Conference on Soft Computing in February 2022. The papers cover multiple areas important for soft computing. Some papers are dedicated to fundamental aspects of soft computing, i.e., fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation, and neural networks. Others emphasize the application of soft computing methods to data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling.

This edited book captures salient global security challenges and presents 'design' solutions in dealing with wicked problems. Through case studies and applied research this book reveals the many perspectives, tools and approaches to support security design. Security design thereby can support risk and threat analysis, risk communication, problem framing and development of interventions strategies. From the refugee crisis to economic slowdowns in emerging markets, from ever-rising numbers of terrorist and cyberattacks to global water shortages, to the proliferation of the Internet of Things and its impact on the security of our homes, cities and critical infrastructure, the current security landscape is diverse and complex. These global risks have been in the headlines in the last year (Global Risks Report) and pose significant security challenges both nationally and globally. In fact, national security is no longer just national. Non-state actors, cyber NGO, rising powers, and hybrid wars and crimes in strategic areas pose complex challenges to global security. In the words of Horst Rittel (1968):"Design is an activity, which aims at the production of a plan, which plan -if implemented- is intended to bring about a situation with specific desired characteristics without creating unforeseen and undesired side and after effects."

This new volume discusses the applications and challenges of deep learning and the internet of things for applications in healthcare. It describes deep learning techniques in conjunction with IoT used by practitioners and researchers worldwide. The authors explore the convergence of IoT and deep learning to enable things to communicate, share information, and coordinate decisions. The book includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. Chapters look at assistive devices in healthcare, alerting and detection devices, energy efficiency in using IoT, data mining for gathering health information for individuals with autism, IoT for mobile applications, and more. The text also offers mathematical and conceptual background that presents the latest technology as well as a selection of case studies.

Gets the reader up to date with an introduction to the concept of data science, its reasons and origin Focused on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis Provides an overview of recent developments and applications on data linked to engineering and management perspectives Covers how data scientists, analysts, and program managers that are interested in productivity and improving their business can do so by incorporating a data science workflow effectively Presents recent trends, highlights future developments, along with discussing problems and offering suggested solutions

Learn to make games that are more fun and engaging! Building on fundamental principles of Artificial Intelligence, Funge explains how to create Non-Player Characters (NPCs) with progressively more sophisticated capabilities. Starting with the basic capability of acting in the game world, the book explains how to develop NPCs who can perceive, remember what they perceive, and then continue in the game play to think about the effects of possible actions, and finally learn from their experience. Funge considers the system architecture and explains how to implement potential behaviors (both reactive and deliberate) for intelligent and responsive NPCs allowing for games that are more fun and engaging. Emphasizing enduring design principles, Funge covers the basics of Game AI and provides a clear, easy to read introduction that beginning programmers and game designers will enjoy.

This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.

Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown's 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking--the key disciplines for making systems smarter An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle A systems thinking approach to people systems--systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers.

Strategy Representation: An Analysis of Planning Knowledge describes an innovative methodology for investigating the conceptual structures that underlie human reasoning. This work explores the nature of planning strategies--the abstract patterns of planning behavior that people recognize across a broad range of real world situations. With a sense of scale that is rarely seen in the cognitive sciences, this book catalogs 372 strategies across 10 different planning domains: business practices, education, object counting, Machiavellian politics, warfare, scientific discovery, personal relationships, musical performance, and the anthropomorphic strategies of animal behavior and cellular immunology. Noting that strategies often serve as the basis for analogies that people draw across planning situations, this work attempts to explain these analogies by defining the fundamental concepts that are common across all instances of each strategy. By aggregating evidence from each of the strategy definitions provided, the representational requirements of strategic planning are identified. The important finding is that the concepts that underlie strategic reasoning are of incredibly broad scope. Nearly 1,000 fundamental concepts are identified, covering every existing area of knowledge representation research and many areas that have not yet been adequately formalized, particularly those related to common sense understanding of mental states and processes. An organization of these concepts into 48 fundamental areas of knowledge and representation is provided, offering an invaluable roadmap for progress within the field.

Pamela McCorduck revisits the artificial intelligence arena and updates this look at the history and future of artificial intelligence with over 100 pages of new material. This book should inspire anyone interested in human intelligence and the future of machine intelligence. The reader should not be surprised to find out that there is much more to AI than simple robots and manufacturing automation.