The goal of this book is to provide, in a friendly and refreshing manner, both theoretical concepts and practical techniques for the important and exciting field of Artificial Intelligence that can be directly applied to real-world healthcare problems. Healthcare – the final frontier. Lately, it seems like Pandora opened the box and evil was released into the world. Fortunately, there was one thing left in the box: hope. In recent decades, hope has been increasingly represented by Intelligent Decision Support Systems. Their continuing mission: to explore strange new diseases, to seek out new treatments and drugs, and to intelligently manage healthcare resources and patients. Hence, this book is designed for all those who wish to learn how to explore, analyze and find new solutions for the most challenging domain of all time: healthcare.

The goal of this book is to provide, in a friendly and refreshing manner, both theoretical concepts and practical techniques for the important and exciting field of Artificial Intelligence that can be directly applied to real-world healthcare problems. Healthcare - the final frontier. Lately, it seems like Pandora opened the box and evil was released into the world. Fortunately, there was one thing left in the box: hope. In recent decades, hope has been increasingly represented by Intelligent Decision Support Systems. Their continuing mission: to explore strange new diseases, to seek out new treatments and drugs, and to intelligently manage healthcare resources and patients. Hence, this book is designed for all those who wish to learn how to explore, analyze and find new solutions for the most challenging domain of all time: healthcare. .

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.

Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling.

The effectiveness of federated learning in high‐performance information systems and informatics‐based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‐based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications. Features: • Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy. • Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy. • Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area. • Analyses the need for a personalized federated learning framework in cloud‐edge and wireless‐edge architecture for intelligent IoT applications. • Comprises real‐life case illustrations and examples to help consolidate understanding of topics presented in each chapter. This book is recommended for anyone interested in federated learning‐based intelligent algorithms for smart communications.

This book is intended to help management and other interested parties such as engineers, to understand the state of the art when it comes to the intersection between AI and Industry 4.0 and get them to realise the huge possibilities which can be unleashed by the intersection of these two fields. We have heard a lot about Industry 4.0, but most of the time, it focuses mainly on automation. In this book, the authors are going a step further by exploring advanced applications of Artificial Intelligence (AI) techniques, ranging from the use of deep learning algorithms in order to make predictions, up to an implementation of a full-blown Digital Triplet system. The scope of the book is to showcase what is currently brewing in the labs with the hope of migrating these technologies towards the factory floors. Chairpersons and CEOs must read these papers if they want to stay at the forefront of the game, ahead of their competition, while also saving huge sums of money in the process.

This book is dedicated to showcase research and innovation in smart healthcare systems and technologies led by women scientists, researchers, and practitioners. With the advent of artificial intelligence (AI) and related technologies, the healthcare sector has undergone tremendous changes in practice and management in recent years. On par to men, women have made significant contributions to tackle a variety of healthcare problems, creating smarter paradigms to provide effective and efficient solutions for patients and stakeholders. The book presents a small collection of contributions by outstanding women in STEM (Science, Technology, Engineering and Mathematics) education, focusing on the healthcare domain. The selected articles allow readers to comprehend current advances in AI and other methods for undertaking healthcare challenges. It is envisaged that the inspiring work by prominent women scientists, researchers, and practitioners reported in this book offers a beacon to propel women in pursuing STEM education and advancing the healthcare sector for the benefits of humankind.

Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving. In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape.

This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.