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"The work updates the popular original with new information about screening, staging, treatments, prevention and more"--
This insightful book provides an analysis of the central ethical issues that have arisen in combatting global terrorism and, in particular, jihadist terrorist groups, notably Al Qaeda, Islamic State and their affiliates. Chapters explore the theoretical problems that arise in relation to terrorism, such as the definition of terrorism and the concept of collective responsibility, and consider specific ethical issues in counter-terrorism.
This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evoluti...
Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.
New technologies like AI, medical apps and implants seem very exciting but they too often have bugs and are susceptible to cyberattacks. Even well-established technologies like infusion pumps, pacemakers and radiotherapy aren't immune. Until digital healthcare improves, digital risk means that patients may be harmed unnecessarily, and healthcare staff will continue to be blamed for problems when it's not their fault. This book tells stories of widespread problems with digital healthcare. The stories inspire and challenge anyone who wants to make hospitals and healthcare better. The stories and their resolutions will empower patients, clinical staff and digital developers to help transform digital healthcare to make it safer and more effective. This book is not just about the bugs and cybersecurity threats that affect digital healthcare. More importantly, it's about the solutions that can make digital healthcare much safer.
Respect for patient autonomy and data privacy are generally accepted as foundational western bioethical values. Nonetheless, as our society embraces expanding forms of personal and health monitoring, particularly in the context of an aging population and the increasing prevalence of chronic diseases, questions abound about how artificial intelligence (AI) may change the way we define or understand what it means to live a free and healthy life. Who should have access to our health and recreational data and for what purpose? How can we find a balance between users' physical safety and their autonomy? Should we allow individuals to forgo continuous health monitoring, even if such monitoring may...
The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains.
Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.
This is the first book to explore the epistemology and ethics of advanced imaging tests, in order to improve the critical understanding of the nature of knowledge they provide and the practical consequences of their utilization in healthcare. Advanced medical imaging tests, such as PET and MRI, have gained center stage in medical research and in patients’ care. They also increasingly raise questions that pertain to philosophy: What is required to be an expert in reading images? How are standards for interpretation to be fixed? Is there a problem of overutilization of such tests? How should uncertainty be communicated to patients? How to cope with incidental findings? This book is of interest and importance to scholars of philosophy of medicine at all levels, from undergraduates to researchers, to medical researchers and practitioners (radiologists and nuclear physicians) interested in a critical appraisal of the methodology of their discipline and in the ethical principles and consequences of their work.