Intelligent Interactive Multimedia Systems for e-Healthcare Applications

This new volume explores how the merging of interactive multimedia with artificial intelligence has created new and advanced tools in healthcare. It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more.

The book covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for management systems using machine learning techniques, and for fusion applications in medical image processing. The chapter authors discuss using data mining and machine learning techniques for COVID-19 diagnosis and prediction, in detecting knee osteoarthritis using texture descriptor algorithms, in applying algorithms in fetal ECG enhancement using blockchain for wearable internet of things in healthcare, and more. A chapter also reviews how doctors can make good use of genomics and genetic data through advanced technology. The book concludes with discussions of open issues, challenges, and future research directions for using intelligent interactive multimedia in healthcare.

Key features:

• Provides an in-depth understanding of emerging technologies and integration of artificial intelligence, deep learning, big data, IoT in healthcare
• Details specific applications for the use of AI, big data, and IoT in healthcare
• Discusses how AI technology can help in formulating protective measures for COVID-19 and other diseases
• Includes case studies

Intelligent Interactive Multimedia Systems for e-Healthcare Applications will be valuable to undergraduate and graduate students planning their careers in either industry or research and to software engineers for using multimedia with artificial intelligence, deep learning, big data, and IoT for healthcare applications.

Shaveta Malik, PhD, is Associate Professor in the Computer Engineering Department at Terna Engineering College, University of Mumbai, Nerul, India. She has more than 11 years of teaching and research experience. She is a member of numerous editorial boards and scientific and advisory committees of international conferences and journals. She has been a co-chair at international conferences also. Her research area focuses on image processing, machine learning, deep learning, and artificial intelligence.

Amit Kumar Tyagi, PhD, is Assistant Professor (Senior Grade) and Senior Researcher at Vellore Institute of Technology, Chennai Campus, India. His current research focuses on machine learning with big data, blockchain technology, data science, cyber-physical systems, smart computing, and security and privacy. He has contributed to several projects such as AARIN and P3-Block to address some of the open issues related to privacy breaches in vehicular applications (such as parking) and medical cyber-physical systems. He is a member of IEEE.