Medical Big Data and Internet of Medical Things
43,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 6-10 Tagen
payback
22 °P sammeln
  • Broschiertes Buch

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems.

Key Features:

Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment

Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data

Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things

Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data

Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems.

Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT)

Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.

Autorenporträt
Nilanjan Dey is an Assistant Professor in the Department of Information Technology, Techno India College of Technology, Kolkata, W.B., India. He holds an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA and Research Scientist of Laboratory of Applied Mathematical Modeling in Human Physiology, Territorial Organization of- Scientific and Engineering Unions, BULGARIA. Associate Researcher of Laboratoire RIADI, University of Manouba, TUNISIA. His research topic is Medical Imaging, Soft computing, Data mining, Machine learning, Rough set, Computer Aided Diagnosis, Atherosclerosis. He has 20 books and 300 international conferences and journal papers. Surekha Borra is currently a Professor in the Department of ECE, K. S. Institute of Technology, Bangalore, India. She earned her Doctorate in Image Processing from Jawaharlal Nehru Technological University, Hyderabad, India, in 2015. Her research interests are in the areas of Image and Video Analytics, Machine Learning, Biometrics and Remote Sensing. She has published one edited book, several book chapters and research papers to her credit in refereed & indexed journals, and conferences at international and national levels. Her international recognition includes her professional memberships & services in refereed organizations, programme committees, editorial & review boards, wherein she has been a guest editor for 2 journals and reviewer for journals published by IEEE, IET, Elsevier, Taylor & Francis, Springer, IGI-Global etc,. She has received Woman Achiever's Award from The Institution of Engineers (India), for her prominent research and innovative contribution (s)., Woman Educator & Scholar Award for her contributions to teaching and scholarly activities, Young Woman Achiever Award for her contribution in Copyright Protection of Images. Dr Aboul Ella Hassanein is the Founder and Head of the Egyptian Scientific Research Group (SRGE) and a Professor of Information Technology at the Faculty of Computer and Information, Cairo University. Professor Hassanien is ex-dean of the faculty of computers and information, Beni Suef University. Prof. Hassanien is a collaborative researcher member of the Computational Intelligence Laboratory at the Department of Electrical and Computer Engineering, University of Manitoba. He also holds the Chair of Computer Science and Information Technology at the Egyptian Syndicate of Scientific Professions (ESSP). Dr Hassanien is the founder and head of Africa Scholars Association in Information and Communication Technology. Professor Hassanien has more than 650 scientific research papers published in prestigious international journals and conferences and over 40 books covering such diverse topics as data mining, medical images, Big Data analysis, virtual reality, intelligent systems, social networks and smart environment. His other research areas include computational intelligence, medical image analysis, security, animal identification and multimedia data mining.