Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities delves into the transformative potential of artificial and cognitive computing in the realm of healthcare systems, maintaining a specific emphasis on sustainability. By exploring the integration of advanced technologies in smart cities, the authors examine and discuss how AI and cognitive computing can be harnessed to enhance healthcare delivery. The book provides focused navigation through innovative solutions and strategies that contribute to the creation of sustainable healthcare ecosystems within the dynamic environment of smart cities. From optimizing resource utilization to improving patient outcomes, this comprehensive exploration provides insight for readers with an interest in the future of healthcare within the era of intelligent urban development.

Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities delves into the transformative potential of artificial and cognitive computing in the realm of healthcare systems, maintaining a specific emphasis on sustainability. By exploring the integration of advanced technologies in smart cities, the authors examine and discuss how AI and cognitive computing can be harnessed to enhance healthcare delivery. The book provides focused navigation through innovative solutions and strategies that contribute to the creation of sustainable healthcare ecosystems within the dynamic environment of smart cities. From optimizing resource utilization to improving patient outcomes, this comprehensive exploration provides insight for readers with an interest in the future of healthcare within the era of intelligent urban development.

Software testing has greatly evolved since the first edition of this book in 2011. Testers are now required to work in "agile" teams and focus on automating test cases. It has thus been necessary to update this work, in order to provide fundamental knowledge that testers should have to be effective and efficient in today's world. This book describes the fundamental aspects of testing in the different lifecycles, and how to implement and benefit from reviews and static analysis. Multiple other techniques are approached, such as equivalence partitioning, boundary value analysis, use case testing, decision tables and state transitions. This second edition also covers test management, test progress monitoring and incident management, in order to ensure that the testing information is correctly provided to the stakeholders. This book provides detailed course-study material for the 2023 version of the ISTQB Foundation level syllabus, including sample questions to help prepare for exams.

The use of discrete-event simulation in various fields, such as in industry, logistics and public health, has really taken off over the last few decades. The implementation of discrete-event simulation does however require an understanding, and perhaps even a mastery, of precise theoretical and methodological principles. Discrete-Event Simulation presents the key concepts involved in any discrete-event simulation project, covering the most frequently used techniques for analysing data and results, the methodological and practical aspects of implementing discrete-event simulation, along with an introduction to the use of the “Arena” discrete-event simulation tool. This book combines the elements presented with applied examples, as well as numerous examples of simulation projects in various fields.

The data that must be processed in healthcare includes text, numbers, statistics, and images, and healthcare systems are continuously acquiring novel data from cutting-edge technologies like wearable devices. Semantic intelligence technologies, such as artificial intelligence, machine learning, and the internet of things, together with the hybrid methodologies which combine these approaches, are central to the development of the intelligent, knowledge-based systems now used in healthcare. This book, Roles and Challenges of Semantic Intelligence in Healthcare Cognitive Computing explores those emerging fields of science and technology in which cognitive computing techniques offer the effective solutions poised to impact healthcare in the foreseeable future, minimizing errors and improving the effectiveness of personalized care models. The book assesses the current landscape, and identifies the roles and challenges of integrating cognitive computing techniques into the widespread adoption of innovative smart healthcare solutions. Each chapter is the result of collaboration by experts from various domains, and provides a detailed overview of the potential offered by new technologies in the field. A wide spectrum of topics and emerging trends are covered, reflecting the multidisciplinary nature of healthcare and cognitive computing and including digital twins, eXplainable AI, AI-based decision-support systems in intensive care, and culinary healthcare, as well as the semantic internet of things (SIoT), natural language processing, and deep learning and graph models. The book presents new ideas which will facilitate collaboration among the different disciplines involved, and will be of interest to all those working in this rapidly evolving field.

The revolution in healthcare as well as demand for efficient real-time healthcare services are driving the progression of edge computing, AI-mediated techniques, deep learning, and IoT applications for healthcare industries and cloud computing. Edge computing helps to meet the demand for newer and more sophisticated healthcare systems that are more personalized and that match the speed of modern life. With applications of edge computing, automated intelligence and intuitions are incorporated into existing healthcare analysis tools for identifying, forecasting, and preventing high-risk diseases. Reconnoitering the Landscape of Edge Intelligence in Healthcare provides comprehensive research on edge intelligence technology with the emphasis on application in the healthcare industry. It covers all the various areas of edge intelligence for data analysis in healthcare, looking at the emerging technologies such as AI-based techniques, machine learning, IoT, cloud computing, and deep learning with illustrations of the design, implementation, and management of smart and intelligent healthcare systems. Chapters showcase the advantages and highlights of the adoption of the intelligent edge models toward smart healthcare infrastructure. The book also addresses the increased need for a high level of medical data security while transferring real-time data to cloud-based architecture, a matter of prime concern for both patient and doctor. Topics include edge intelligence for wearable sensor technologies and their applications for health monitoring, the various edge computing techniques for disease prediction, e-health services and e-security solutions through IoT devices that aim to improve the quality of care for transgender patients, smart technology in ambient assisted living, the role of edge intelligence in limiting virus spread during pandemics, neuroscience in decoding and analysis of visual perception from the neural patterns and visual image reconstruction, and more. The technology addressed include energy aware cross-layer routing protocol (ECRP), OMKELM-IDS technique, graphical user interface (GUI), IOST (an ultra-fast, decentralized blockchain platform), etc. This volume will be helpful to engineering students, research scholars, and manufacturing industry professionals in the fields of engineering applications initiatives on AI, machine learning, and deep learning techniques for edge computing.

This book presents the select proceedings of the 2nd International Conference on Intelligent Systems and Applications 2023. The theme of this conference is ‘Intelligent Systems for Smart Cities'. It covers the topics of intelligent systems in multiple aspects such as healthcare, supply chain and logistics, smart homes and smart structures, banking and finance, a sustainable environment, social media and cyber security, crime prevention, and disaster management. The book will be useful for researchers and professionals interested in the broad field of artificial intelligence and machine learning.

In contemporary healthcare, Industry 5.0 technologies present a paradoxical challenge and opportunity. The rapid integration of Cyber Physical Systems, Cloud Computing, Internet of Things, Artificial Intelligence, Smart Factories, and Cognitive Computing has ushered in unprecedented transformations, yet it has concurrently given rise to critical vulnerabilities within healthcare systems. As sensitive patient data becomes increasingly digitized, the specter of cybersecurity threats looms larger than ever. The book, titled "Technologies for Sustainable Healthcare Development," undertakes the crucial task of addressing this pressing concern. Focused on Cybersecurity and Data Science Innovations in Industry 5.0 Technologies for Sustainable Healthcare, it serves as an indispensable guide for professionals, researchers, and policymakers aiming to fortify healthcare systems against unauthorized access and cyber threats while unlocking the potential of transformative technologies. The overarching objective of Technologies for Sustainable Healthcare Development is to dissect the challenges posed by the convergence of cybersecurity, data science, and Industry 5.0 in healthcare. This timely publication delves into the evolution of cybersecurity and data science, providing insights into their symbiotic relationship and the implications for healthcare. Through its exploration of cutting-edge research, innovative solutions, and practical applications, the book becomes a beacon for those seeking to navigate the evolving landscape of secure healthcare development. It does not merely dissect problems but endeavors to provide sustainable development strategies, contributing to the advancement of robust and efficient healthcare systems.

COGNITIVE ANALYTICS AND REINFORCEMENT LEARNING The combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research. Cognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications. Readers will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems. The book’s real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. Whether you are a student looking to gain a deeper understanding of these cutting-edge technologies, an AI practitioner seeking innovative solutions for your projects, or an industry leader interested in the strategic applications of AI, this book offers a treasure trove of insights and knowledge to help you navigate the complex and exciting world of cognitive analytics and reinforcement learning. Audience The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.