Securing Next-Generation Connected Healthcare Systems focuses on the crucial aspects of IoT security in a connected environment, which will not only benefit from cutting-edge methodological approaches but also assist in the rapid scalability and improvement of these systems. This book shows how to utilize technologies like blockchain and its integration with IoT for communication, data security, and trust management. This book will introduce the security aspect of next generation technologies for healthcare, covering a wide range of security and computing methodologies. Researchers, data scientists, students, and professionals interested in the application of artificial intelligence in healthcare management, data security of connected healthcare systems and related fields, specifically on data intensive secured systems and computing environments will finds this to be a welcomed resource. - Covers the latest next generation connected healthcare technologies using parallel computing - Presents all the security aspects in next-generation technologies for healthcare - Utilizes technologies such as blockchain and its integration with IoT for communication, data security, and trust management - Discusses privacy and security issues and challenges in data intensive cloud computing environment - Dives into the concept of parallel and distributed computing technologies and their applications in the real world

Internet of things (IoT) applications employed for healthcare generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution is ideal in order to generate faster reports in comparison to the traditional way. Given the current state of the world in which every day IoT devices are developed to provide healthcare solutions, it is essential to consider the mechanisms used to collect and analyze the data to provide thorough reports. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications applies artificial intelligence (AI) in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and discusses security solutions for edge analytics in healthcare IoT. Covering topics such as data analytics and next generation healthcare systems, it is ideal for researchers, academicians, technologists, IT specialists, data scientists, healthcare industries, IoT developers, data security analysts, educators, and students.

The cognitive approach to the IoT provides connectivity to everyone and everything since IoT connected devices are known to increase rapidly. When the IoT is integrated with cognitive technology, performance is improved, and smart intelligence is obtained. Discussed in this book are different types of datasets with structured content based on cognitive systems. The IoT gathers the information from the real time datasets through the internet, where the IoT network connects with multiple devices. This book mainly concentrates on providing the best solutions to existing real-time issues in the cognitive domain. Healthcare-based, cloud-based and smart transportation-based applications in the cognitive domain are addressed. The data integrity and security aspects of the cognitive computing main are also thoroughly discussed along with validated results.

The second in the Women Securing the Future with TIPPSS series, this book provides insight and expert advice from seventeen women leaders in technology, healthcare and policy to address the challenges of Trust, Identity, Privacy, Protection, Safety and Security (TIPPSS) for connected healthcare, and the growing Internet of Medical Things (IoMT) ecosystem. The ten chapters in this book delve into trust, security and privacy risks in connected healthcare for patients, medical devices, personal and clinical data, healthcare providers and institutions, and provide practical approaches to manage and protect the data, devices, and humans. Cybersecurity, technology and legal experts discuss risks, from data and device hacks to ransomware, and propose approaches to address the challenges including new frameworks for architecting and evaluating medical device and connected hospital cybersecurity. We all need to be aware of the TIPPSS challenges in connected healthcare, and we call upon engineers, device manufacturers, system developers and healthcare providers to ensure trust and manage the risk. Featuring contributions from prominent female experts and role models in technology, cybersecurity, engineering, computer science, data science, business, healthcare, accessibility, research, law, privacy and policy, this book sets the stage to improve security and safety in our increasingly connected world.

Provides applications of soft computing techniques related to healthcare systems, such as machine learning, fuzzy logic, and statistical mathematics, play in the advancements of smart healthcare systems Examine descriptive, predictive, and social network techniques and discusses analytical tools and the important role they play in enhancing the services to connected healthcare systems Addresses real-time challenges and case studies in the Healthcare industry Presents various soft computing methodologies like fuzzy logic, ANN, and Genetic Algorithms, to help decision making Focuses on data-centric operations in the Healthcare industry

This comprehensive book focuses on better big-data security for healthcare organizations. Following an extensive introduction to the Internet of Things (IoT) in healthcare including challenging topics and scenarios, it offers an in-depth analysis of medical body area networks with the 5th generation of IoT communication technology along with its nanotechnology. It also describes a novel strategic framework and computationally intelligent model to measure possible security vulnerabilities in the context of e-health. Moreover, the book addresses healthcare systems that handle large volumes of data driven by patients’ records and health/personal information, including big-data-based knowledge management systems to support clinical decisions. Several of the issues faced in storing/processing big data are presented along with the available tools, technologies and algorithms to deal with those problems as well as a case study in healthcare analytics. Addressing trust, privacy, and security issues as well as the IoT and big-data challenges, the book highlights the advances in the field to guide engineers developing different IoT devices and evaluating the performance of different IoT techniques. Additionally, it explores the impact of such technologies on public, private, community, and hybrid scenarios in healthcare. This book offers professionals, scientists and engineers the latest technologies, techniques, and strategies for IoT and big data.

Information security practices are the backbone of smart factories, which dynamically coordinate and optimize production processes based on data produced and collected by the underlying cyber-physical systems, in terms of resource usage. Recent advances in the best practices, opportunities, challenges, and benefits of information security must be studied and considered for businesses across sectors to successfully utilize the practices in their internet of things, 5G, and next-generation wireless networks. Information Security Practices for the Internet of Things, 5G, and Next-Generation Wireless Networks highlights research on secure communication of 5G, internet of things, and next-generation wireless networks along with related areas to ensure secure and internet-compatible internet of things systems. The book also discusses the effects of the internet of things technologies on various situations in smart city design. Covering a range of topics such as secure communications and security evaluations, this reference work is ideal for industry professionals, business owners, engineers, researchers, scholars, practitioners, academicians, instructors, and students.

Internet of Healthcare Things (IoHT) is an Internet of Things (IoT)-based solution that includes a network architecture which allows the connection between a patient and healthcare facilities. This book covers various research issues of smart and secure IoHT, aimed at providing solutions for remote healthcare monitoring using pertinent techniques. Applications of machine learning techniques and data analytics in IoHT, along with the latest communication and networking technologies and cloud computing, are also discussed. Features: Provides a detailed introduction to IoHT and its applications Reviews underlying sensor and hardware technologies Includes recent advances in the IoHT, such as remote healthcare monitoring and wearable devices Explores applications of data analytics/data mining in IoHT, including data management and data governance Focuses on regulatory and compliance issues in IoHT This book is intended for graduate students and researchers in Bioinformatics, Biomedical Engineering, Big Data and Analytics, Data Mining, and Information Management, IoT and Computer and Electrical Engineering.

AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMS This book provides cutting-edge chapters on machine-empowered solutions for next-generation systems for today’s society. Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user’s privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. Audience Researchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students.

This book discusses the applications, challenges, and future trends of machine learning in medical domain, including both basic and advanced topics. The book presents how machine learning is helpful in smooth conduction of administrative processes in hospitals, in treating infectious diseases, and in personalized medical treatments. The authors show how machine learning can also help make fast and more accurate disease diagnoses, easily identify patients, help in new types of therapies or treatments, model small-molecule drugs in pharmaceutical sector, and help with innovations via integrated technologies such as artificial intelligence as well as deep learning. The authors show how machine learning also improves the physician’s and doctor’s medical capabilities to better diagnosis their patients. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning that will enhance the efficiency and effectiveness of the healthcare system. Provides researchers in machine and deep learning with a conceptual understanding of various methodologies of implementing the technologies in medical areas; Discusses the role machine learning and IoT play into locating different virus and diseases across the globe, such as COVID-19, Ebola, and cervical cancer; Includes fundamentals and advances in machine learning in the medical field, supported by significant case studies and practical applications.