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.

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.

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.

OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.

This book focuses on various advanced technologies which integrate with machine learning to assist one of the most leading industries, healthcare. It presents recent research works based on machine learning approaches supported by medical and information communication technologies with the use of data and image analysis. The book presents insight about techniques which broadly deals in delivery of quality, accurate and affordable healthcare solutions by predictive, proactive and preventative methods. The book also explores the possible use of machine learning in enterprises, such as enhanced medical imaging/diagnostics, understanding medical data, drug discovery and development, robotic surgery and automation, radiation treatments, creating electronic smart records and outbreak prediction.

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.