Atnaujinkite slapukų nuostatas

El. knyga: Data Modelling and Analytics for the Internet of Medical Things

Edited by , Edited by , Edited by
  • Formatas: 328 pages
  • Išleidimo metai: 22-Dec-2023
  • Leidėjas: CRC Press
  • Kalba: eng
  • ISBN-13: 9781003825791
Kitos knygos pagal šią temą:
Data Modelling and Analytics for the Internet of Medical ThingsDidesnis paveikslėlis
  • Formatas: 328 pages
  • Išleidimo metai: 22-Dec-2023
  • Leidėjas: CRC Press
  • Kalba: eng
  • ISBN-13: 9781003825791
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

The emergence of the Internet of Medical Things (IoMT) is transforming the management of diseases, improving diseases diagnosis and treatment methods, and reducing healthcare costs and errors. This book covers all the essential aspects of IoMT in one place, providing readers with a comprehensive grasp of IoMT and related technologies.

Data Modelling and Analytics for the Internet of Medical Things integrates the architectural, conceptual, and technological aspects of IoMT, discussing in detail the IoMT, connected smart medical devices, and their applications to improve health outcomes. It explores various methodologies and solutions for medical data analytics in healthcare systems using machine learning and deep learning approaches, as well as exploring how technologies such as blockchain and cloud computing can further enhance data analytics in the e-health domain. Prevalent IoMT case studies and applications are also discussed.

The book suits scientists, design engineers, system integrators, and researchers in the field of IoMT. It will also be of interest to postgraduate students in computer science focusing on healthcare applications and as supplementary reading for IoMT courses.



The Internet of Medical Things (IoMT) is transforming the management of diseases, improving diseases diagnosis and treatment methods, and reducing healthcare cost and errors. This book integrates the architectural, conceptual, and technological aspects of IoMT, providing the reader with a comprehensive grasp of the IoMT landscape.

Part I. IoMT Datasets and Storage.
1. Remote Health Monitoring in the Era of the Internet of Medical Things.
2. Diabetic health care data analytics and application.
3. Blockchain for Handling Medical Data.
4. Cloud computing for complex IoMT data.
5. The potential of IoMT Devices in Early Detection of Suicidal Ideation. Part II. Machine Learning for Medical Things.
6. Application and Challenges of Machine Learning in Healthcare.
7. Artificial Intelligence and Internet of Medical Things in the Diagnosis and Prediction of Disease.
8. Predicting Cardiovascular Diseases Using Machine Learning: A Systematic Review of the Literature.
9. Identification of Unipolar Depression Using Boosting Algorithms.
10. Development of EEG based Identification of Learning Disability using Machine Learning Algorithms.
11. Deep Learning Approaches for IoMT.
12. Machine Learning and Deep Learning Techniques to Classify Depressed Patients from Healthy, Using Brain Signals from Electroencephalogram (EEG).
13. Dimensionality Reduction for IoMT Devices Using PCA.
14. Face Mask Detection System. Part III. IoMT: Data Analytics and Use Cases.
15. An IoT-based Real-time ECG Monitoring Platform for Multiple Patients.
16. Study on Anomaly Detection in Clinical Laboratory Data Using Internet of Medical Things.
17. Computational Intelligence Framework for Improving Quality of Life in Cancer Patients.
18. Major Depressive Disorder Detection using Data Science and Wearable Connected Devices.

Rajiv Pandey, Senior Member IEEE, is a faculty member at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India.

Pratibha Maurya is an assistant professor at Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, India.

Raymond Chiong is currently an associate professor with the University of Newcastle, Australia.
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97810038257912e.html