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Produktbild: Internet of Things and Machine Learning for Type I and Type II Diabetes

Internet of Things and Machine Learning for Type I and Type II Diabetes Use Cases

152,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.07.2024

Herausgeber

Sujata Dash + weitere

Verlag

Elsevier Science & Technology

Seitenzahl

448

Maße (L/B/H)

27,5/21,4/2,3 cm

Gewicht

1225 g

Sprache

Englisch

ISBN

978-0-323-95686-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.07.2024

Herausgeber

Verlag

Elsevier Science & Technology

Seitenzahl

448

Maße (L/B/H)

27,5/21,4/2,3 cm

Gewicht

1225 g

Sprache

Englisch

ISBN

978-0-323-95686-4

EU-Ansprechpartner

Elsevier B.V.
Radarweg 29
1043 NX Amsterdam
NL
productsafety@elsevier.com

Herstelleradresse

Elsevier Science & Technology
London Wall 125
EC2Y 5AS London
GB
tradeorders@elsevier.com

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  • Produktbild: Internet of Things and Machine Learning for Type I and Type II Diabetes

  • 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 Fog Computing-based Diabetes Prediction System for Remote Healthcare Applications
    22. Artificial intelligence approaches for risk stratification of diabetic kidney disease
    23. Computational Methods for predicting the occurrence of cardiac autonomic neuropathy
    24. Development of a Clinical Forecasting Model to Predict Comorbid Depression in Diabetes Patients and its Application in Policy Making for Depression Screening

    Section 5: Drug design and Treatment Response
    25. Enhancing Diabetic Maculopathy Classification through a Synergistic Deep Learning Approach by Combining Convolutional Neural Networks, Transfer Learning, and Attention Mechanisms
    26. Pharmacogenomics: the roles of genetic factors on treatment response and outcomes in diabetes
    27. Predicting treatment response in diabetes: the roles of machine learning-based models
    28. Antidiabetic Potential of Mangrove Plants: An Updated Review