Wellbeing Machine

Wellbeing Machine shows how wellbeing arises in the intimate processes of daily life. Wellbeing and illbeing are generally seen as interior states of the individual, which can readily be linked to individuals being blamed for the status of their wellbeing. This book shifts attention away from the individual and onto the collective body. This approach generates a conceptual entity called the wellbeing machine, which comprises four assemblages that represent different responses to the challenges of everyday life experienced by people with depression. In this manner, wellbeing emerges from assemblages that transform in a sustainable way over time. Assemblages associated with illbeing are generative and vital to the production of wellbeing. Wellbeing Machine shifts discussion about the wellbeing bioeconomy into new terrain. It investigates the intersections between emergent wellbeing and labour, power, and capitalism, and produces knowledge about wellbeing that does not contribute negative associations about individuals¿ wellbeing levels.

This book is the result of a collaboration between a human editor and an artificial intelligence algorithm to create a machine-generated literature overview of research articles analyzing the mediating role of social media on the psychological wellbeing of youth. It’s a new publication format in which state-of-the-art computer algorithms are applied to select the most relevant articles published in Springer Nature journals and create machine-generated literature reviews by arranging the selected articles in a topical order and creating short summaries of these articles. In this volume, a human counsellor psychologist used the algorithm to explore articles that present results of research about the impacts of social media on the psychological wellbeing of youth. The Internet has always been popular among youth, but during the pandemic it has attracted even more attention since many aspects of life further migrated to the digital world, thus adding substantially to Internet’s ever-increasing popularity. Today, youth spend a majority portion of their time on the Internet and an increasing amount on social media. In such digitally dependent times, this book attempts to provide insights on the positive and negative impact of the Internet and social media on youth mental health, and also provides specific observations on personality traits.

The perception of smart cities encompasses a strategy that uses different types of technologies, artificial intelligence (AI), and machine learning and in which, through the internet of things (IoT) and sensor-based data collection, the strategy extrapolates information using insights gained from that data to manage or monitor or track assets, resources, and services efficiently in an urban area. Both these models deeply affect the localities where they are applied and can create together immense possibilities for urban recovery, better quality of life, physical and mental health protection, and economic and social redevelopment. Smart Cities and Machine Learning in Urban Health promotes interdisciplinary work that develops and illustrates the concept of resilience in relation to smart city and machine learning. The book examines the ability of an area and its communities to recover quickly from difficulties; the rigidness and resistance of an area and its communities to possible crisis; the ability of an area, its communities, infrastructure, and business to spring back into shape; and the responsiveness and mitigation towards the crisis with a special look at the impact of the COVID-19 pandemic. The research’s theoretical foundation rests on a wide range of non-architectural sources, primarily AI, sociology, urban studies, and technological development, but it explores everything on cases taken from real cities, thus transforming them into pieces of architectural interest. Covering topics such as carbon emissions, digital healthcare systems, and urban transformation, this book is an essential resource for graduate and post-graduate students, policymakers, researchers, university faculty, engineers, public management, hospital administration, professors, and academicians.

Introduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model Development There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research. Written by an international team of authors and edited by renowned experts in the areas of process safety and occupational and environmental health, sample topics covered within the work include: An introduction to the fundamentals of machine learning, including regression, classification and cross-validation, and an overview of software and tools Detailed reviews of various applications in the areas of chemical safety and health, including flammability prediction, consequence prediction, asset integrity management, predictive nanotoxicity and environmental exposure assessment, and more Perspective on the possible future development of this field Machine Learning in Chemical Safety and Health serves as an essential guide on both the fundamentals and applications of machine learning for industry professionals and researchers in the fields of process safety, chemical safety, occupational and environmental health, and industrial hygiene.

Offering a holistic view of the pioneering trends and innovations in smart healthcare management, this book focuses on the methodologies, frameworks, design issues, tools, architectures, and technologies necessary to develop and understand intelligent healthcare systems and emerging applications in the present era. Smart Technologies in Healthcare Management: Pioneering Trends and Applications provides an overview of various technical and innovative aspects, challenges, and issues in smart healthcare, along with recent and novel findings. It highlights the latest advancements and applications in the field of intelligent systems and explores the importance of cloud computing and the design of sensors in an IoT system. The book offers algorithms and a framework with models in machine learning and AI for smart healthcare management. A detailed flow chart and innovative and modified methodologies related to intelligent computing in healthcare are discussed, as well as real-world-based examples so that readers can compare technical concepts with daily life concepts. This book will be a useful reference for academicians and the healthcare industry, along with professionals interested in exploring innovations in varied applicational areas of AI, IoT, and machine learning. Researchers, startup companies, and entrepreneurs will also find this book of interest.

Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

Understanding Wellbeing is an accessible introduction to the concept of wellbeing and its relevance to areas of health and social policy. Understanding Wellbeing provides students, professionals and practitioners of health and social care with the essential resources for understanding and promoting wellbeing. The book includes case studies, activities and reflection points to engage the reader with both the theory and its practical application. The book provides an overview of the concept of wellbeing and its relationship with and role in health, including: Psychological aspects of wellbeing — mind/body influences, psychology, spirituality Physical aspects of wellbeing — food, exercise, genetics, health promotion Social approaches to wellbeing — social policy, culture, environment, housing, education, information The Editors: Anneyce Knight is Senior Lecturer at Bournemouth University The late Allan McNaught was Principal Lecturer at the University of Greenwich. The authors are a multi-professional group of health academics with considerable national and international experience across the statutory and non-statutory sectors.

Drawing upon insights from feminist new materialism the book traces the complex material-discursive processes through which women’s recovery from depression is enacted within a gendered biopolitics. Within the biomedical assemblage that connects mental health policy, service provision, research and everyday life, the gendered context of recovery remains little understood despite the recurrence and pervasiveness of depression. Rather than reducing experience to discrete biological, psychological or sociological categories, feminist thinking moves with the biopsychosocialities implicated in both distress and lively modes of becoming well. Using a post-qualitative approach, the book creatively re-presents how women ‘do’ recovery within and beyond the normalising imperatives of biomedical and psychotherapeutic practices. By pursuing the affective movement of self through depression this inquiry goes beyond individualised models to explore the enactment of multiple self-world relations. Reconfiguring depression and recovery as bodymind matters opens up a relational ontology concerned with the entanglement of gender inequities and mental (ill) health.