Deep Learning in Smart eHealth Systems


Book Description

One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson's disease patients. This framework offers a new way to assess and monitor patients' motor deficits in a personalized and automated way, improving the efficiency and accuracy of diagnosis and treatment. Compared to other books on eHealth and Parkinson's disease, this book offers a unique perspective and solution to the challenges facing patients and healthcare providers. It combines state-of-the-art technology, such as wearable devices and deep learning algorithms, with clinical expertise to develop a personalized and efficient evaluation framework for Parkinson's disease patients. This book provides a roadmap for the integration of cutting-edge technology into clinical practice, paving the way for more effective and patient-centered healthcare. To understand this book, readers should have a basic knowledge of eHealth, IoT, deep learning, and Parkinson's disease. However, the book provides clear explanations and examples to make the content accessible to a wider audience, including researchers, practitioners, and students interested in the intersection of technology and healthcare.




Introduction to Smart eHealth and eCare Technologies


Book Description

Enables decision makers to evaluate the impact of technology introduction on process efficiency, cost savings, and health and care quality improvements. Presents real-world implementations, case studies, and field trials. Contains contributions from experts in industry, the public sector, and academia. Provides an extensive overview of the current situation and future trends in well-being technology.




Health Informatics Meets EHealth


Book Description

Biomedical engineering and health informatics are closely related to each other, and it is often difficult to tell where one ends and the other begins, but ICT systems in healthcare and biomedical systems and devices are already becoming increasingly interconnected, and share the common entity of data. This is something which is set to become even more prevalent in future, and will complete the chain and flow of information from the sensor, via processing, to the actuator, which may be anyone or anything from a human healthcare professional to a robot. Methods for automating the processing of information, such as signal processing, machine learning, predictive analytics and decision support, are increasingly important for providing actionable information and supporting personalized and preventive healthcare protocols in both biomedical and digital healthcare systems and applications. This book of proceedings presents 50 papers from the 12th eHealth conference, eHealth2018, held in Vienna, Austria, in May 2018. The theme of this year’s conference is Biomedical Meets eHealth – From Sensors to Decisions, and the papers included here cover a wide range of topics from the field of eHealth. The book will be of interest to all those working to design and implement healthcare today.







Internet of Things enabled Machine Learning for Biomedical Application


Book Description

The text begins by highlighting the benefits of the Internet of Things-enabled machine learning in the healthcare sector, examines the diagnosis of diseases using machine learning algorithms, and analyzes security and privacy issues in the healthcare systems using the Internet of Things. The text elaborates on image processing implementation for medical images to detect and classify diseases based on magnetic resonance imaging and ultrasound images. This book: · Covers the procedure to recognize emotions using image processing and the Internet of Things-enabled machine learning. · Highlights security and privacy issues in the healthcare system using the Internet of Things. · Discusses classification and implementation techniques of image segmentation. · Explains different algorithms of machine learning for image processing in a comprehensive manner. · Provides computational intelligence on the Internet of Things for future biomedical applications including lung cancer. It is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.




5G Internet of Things and Changing Standards for Computing and Electronic Systems


Book Description

Internet of things networks have changed the standard of how computing and electronic systems are interconnected. Computing and electronic devices and systems, with the help of 5G technology, can now be seamlessly linked in a way that is rapidly turning the globe into a digital world. Smart cities and the internet of things are here to stay but not without some challenges; a thorough review of the opportunities, difficulties, and benefits of 5G internet of things is necessary for it to be successfully utilized and implemented. 5G Internet of Things and Changing Standards for Computing and Electronic Systems examines modern computers and electronics and how they provide seamless connectivity due to the development of internet of things technology. Moreover, this reference covers various technologies and their roles and impacts in the future of smart cities. Covering a range of topics such as machine learning and renewable energy systems, this reference work is ideal for scientists, engineers, policymakers, researchers, practitioners, academicians, scholars, instructors, and students.




Artificial Intelligence and Machine Learning for COVID-19


Book Description

This book is dedicated to addressing the major challenges in fighting COVID-19 using artificial intelligence (AI) and machine learning (ML) – from cost and complexity to availability and accuracy. The aim of this book is to focus on both the design and implementation of AI-based approaches in proposed COVID-19 solutions that are enabled and supported by sensor networks, cloud computing, and 5G and beyond. This book presents research that contributes to the application of ML techniques to the problem of computer communication-assisted diagnosis of COVID-19 and similar diseases. The authors present the latest theoretical developments, real-world applications, and future perspectives on this topic. This book brings together a broad multidisciplinary community, aiming to integrate ideas, theories, models, and techniques from across different disciplines on intelligent solutions/systems, and to inform how cognitive systems in Next Generation Networks (NGN) should be designed, developed, and evaluated while exchanging and processing critical health information. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via wireless/wired enabling technologies.




System Design for Epidemics Using Machine Learning and Deep Learning


Book Description

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.




Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing


Book Description

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.




Machine Learning for Cyber Physical System


Book Description

This book provides a comprehensive platform for learning the state-of-the-art machine learning algorithms for solving several cybersecurity issues. It is helpful in guiding for the implementation of smart machine learning solutions to detect various cybersecurity problems and make the users to understand in combating malware, detect spam, and fight financial fraud to mitigate cybercrimes. With an effective analysis of cyber-physical data, it consists of the solution for many real-life problems such as anomaly detection, IoT-based framework for security and control, manufacturing control system, fault detection, smart cities, risk assessment of cyber-physical systems, medical diagnosis, smart grid systems, biometric-based physical and cybersecurity systems using advance machine learning approach. Filling an important gap between machine learning and cybersecurity communities, it discusses topics covering a wide range of modern and practical advance machine learning techniques, frameworks, and development tools to enable readers to engage with the cutting-edge research across various aspects of cybersecurity.