rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Internet of Things and machine learning for type I and type II diabetes
    sub title: use cases
  name:
    name part: Dash, Sujata
    name part: 1964-
    role:
      role term: editor.
  name:
    name part: Pani, Subhendu Kumar
    name part: 1980-
    role:
      role term: editor.
  name:
    name part: Susilo, Willy
    role:
      role term: editor.
  name:
    name part: Tse, Gary M.
    role:
      role term: editor.
  name:
    name part: Cheung, Bernard Man Yung
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    place:
      place term: London
    place:
      place term: San Diego, CA
    publisher: Academic Press
    date issued: [2024]
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research community, crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.
  table of contents: Section 1: Diagnosis 1. An Intelligent Diagnostic approach for diabetes Using rule-based Machine Learning techniques 2. Ensemble Sparse Intelligent Mining Techniques for Diabetes Diagnosis 3. Detection of Diabetic Retinopathy Using Neural Networks 4. An Intelligent Remote Diagnostic Approach for Diabetes Using Machine Learning Techniques 5. Diagnosis of Diabetic Retinopathy in Retinal Fundus Images Using Machine Learning and Deep Learning Models 6. Diagnosis of Diabetes Mellitus using Deep Learning Techniques and Big Data Section 2: Glucose monitoring 7. IoT and Machine Learning for Management of Diabetes Mellitus 8. Prediction of glucose concentration in type 1 diabetes patients based on Machine learning techniques 9. ML-Based PCA Methods to Diagnose Statistical Distribution of Blood Glucose Levels of Diabetic Patients Section 3: Prediction of complications and risk stratification 10. Overview of New trends on deep learning models for diabetes risk prediction 11. Clinical applications of deep learning in diabetes and its enhancements with future predictions 12. Feature Classification and Extraction of Medical Data Related to Diabetes Using Machine Learning Techniques: A Review 13. ML-based predictive model for type 2 diabetes mellitus using genetic and clinical data 14. Applications of IoT and data mining techniques for diabetes monitoring 15. Decision-making System for the Prediction of Type II Diabetes Using Data Balancing and Machine Learning Techniques 16. Comparative Analysis of Machine Learning Tools in Diabetes Prediction 17. Data Analytic models of patients dependent on insulin treatment 18. Prediction of Diabetes using Hybridization of Radial Basis Function Network and Differential Evaluation based Optimization Technique 19. An Overview of New Trends On Deep Learning Models For Diabetes Risk Prediction Section 4: Dialysis 20. Progression and Identification of heart disease risk factors in diabetic patients from electronic health records 21. An Intelligent …
  note: edited by Sujata Dash, Subhendu Kumar Pani, Willy Susilo, Bernard Man Yung Cheung, Gary Tse.
  note: Includes bibliographical references and index.
  subject:
    topic: Diabetes
    topic: Technological innovations
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Machine learning
  subject:
    topic: Internet of things
  subject:
    topic: Machine Learning
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Internet des objets
  classification: RC660
  classification: 616.4/62
  related item:
    identifier: (OCoLC)1406408750
  identifier: 9780323956932
  identifier: 0323956939
  identifier: 9780323956864
  identifier: 0323956866
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 240712
    record change date: 20260327180120.2
    record identifier: in00005773381
    language of cataloging:
      language term: eng