Examines the fundamental concepts and analysis of machine learning algorithms Reviews the methods to apply the Machine learning algorithm to diagnose various disease Provides innovative, new approaches for Machine learning in medical healthcare with its future direction Incorporates healthcare monitoring devices to overcome the healthcare problem Discusses the research challenges and future work directions for researchers in healthcare

In today’s era, there is a need for a system that can automate the process of treatment for the patient if medical facilities are out of reach. Smart healthcare can step in to make the patient more self-dependent. 6G with its features can be taken as the future of smart healthcare with IoT and AI. 6G-enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis offers the fundamentals, history, reality, and challenges faced in the smart healthcare industry today. It discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used. The book details the role that Machine Learning-based Deep Learning and 6G-enabled IoT concepts play in the automation of smart healthcare systems. The book goes on to presents applications of smart healthcare through various real-world examples and includes chapters on security and privacy in the 6G-enabled and IoT environment, as well as research on the future prospects of the smart healthcare industry. This book: Offers the fundamentals, history, reality, and the challenges faced in the smart healthcare industry Discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used Details the role that Machine Learning-based Deep Learning and 6G enabled IoT concepts play in the automation of smart healthcare systems Presents applications of smart healthcare through various real-world examples Includes topics on security and privacy in 6G enabled IoT, as well as research and future prospectus of the smart healthcare industry Interested readers of this book will include anyone working in or involved in smart healthcare research which includes, but is not limited to healthcare specialists, Computer Science Engineers, Electronics Engineers, Systems Engineers, and Pharmaceutical practitioners.

Since Computational Intelligence is a latest technological aspect, the book is likely to be adopted in almost all leading Universities. This book aims to provide state-of-art research in the context of Computational Intelligence related with Healthcare its applications, challenges and management and it would promote how optimization or intelligent techniques envisage the role of Artificial Intelligence-Machine/Deep Learning (AI-ML/DL) in Healthcare.

Although some IoT systems are built for simple event control where a sensor signal triggers a corresponding reaction, many events are far more complex, requiring applications to interpret the event using analytical techniques to initiate proper actions. Artificial intelligence of things (AIoT) applies intelligence to the edge and gives devices the ability to understand the data, observe the environment around them, and decide what to do best with minimum human intervention. With the power of AI, AIoT devices are not just messengers feeding information to control centers. They have evolved into intelligent machines capable of performing self-driven analytics and acting independently. A smart environment uses technologies such as wearable devices, IoT, and mobile internet to dynamically access information, connect people, materials and institutions, and then actively manages and responds to the ecosystem's needs in an intelligent manner. In this edited book, the authors present challenges, technologies, applications and future trends of AI-enabled IoT (AIoT) in realizing smart and intelligent environments, including frameworks and methodologies to apply AIoT in monitoring devices and environments, tools and practices most applicable to product or service development to solve innovation problems, advanced and innovative techniques and practical implementations to enhance future smart environment systems as. They plan to cover a broad range of applications including smart cities, smart transportation and smart agriculture. This book is a valuable resource for industry and academic researchers, scientists, engineers and advanced students in the fields of ICTs and networking, IoT, AI and machine and deep learning, data science, sensing, robotics, automation and smart technologies and smart environments.

1) Discusses technical details of the Machine Learning tools and techniques in the different types of cancers 2) Machine learning and data mining in healthcare is a very important topic and hence there would be a demand for such a book 3) As compared to other titles, the proposed book focuses on different types of cancer disease and their prediction strategy using machine leaning and data mining.

Includes innovative and new approaches for IoT in medical healthcare monitoring with 5G along with future directions Discusses the fundamental concepts and analysis of IoT and 5G in smart healthcare Focuses on methods used to apply IoT in monitoring devices for diagnosing diseases and transferring data using a 5G network. Presents new points of security and privacy concerns with expectations of IoT devices in 2030 where 91 billion devices will exist with over 10 connected devices per person Provides case studies depicting applications, best practices, as well as future predictions of IoT in everyday life Illustrates user focused wearable devices such as Fitbit health monitors and smartwatches where consumers are self-managing and self-monitoring their own health and providers are able to improve the experience of care

"Waiting our turn" is a part of our everyday life. The amount of time that a nation's populace wastes by waiting in queues is a major factor in both the quality of life there & the efficiency of the nation's economy. Queuing theory is an entire discipline in the field of Operations Research for the formal study of waiting lines.This subject developed rapidly after world war II due to its fruitful results in research on military operations. Q-theory has been used in the past to assess things such as staff schedules, working environment, productivity, customer waiting time etc. Efforts to minimize congestion so that both, the customer that waits and the one that serves get the most benefit, and vast applications of the subject in the practical situations are the main reasons behind its rapid development. Now, Q-theory has been used extensively in banking industry to increase business by careful placement of merchandising materials while at the same time alleviating both the actual & perceived amount of time a customer spends waiting in line. Q-management has been applied very successfully in many service-oriented industries to significantly reduce operating cost & improve service.