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

Section 1: Diagnosis1. An Intelligent Diagnostic approach for diabetes Using rule-based Machine Learning techniques2. Ensemble Sparse Intelligent Mining Techniques for Diabetes Diagnosis3. Detection of Diabetic Retinopathy Using Neural Networks4. An Intelligent Remote Diagnostic Approach for Diabetes Using Machine Learning Techniques5. Diagnosis of Diabetic Retinopathy in Retinal Fundus Images Using Machine Learning and Deep Learning Models6. Diagnosis of Diabetes Mellitus using Deep Learning Techniques and Big DataSection 2: Glucose monitoring7. IoT and Machine Learning for Management of Diabetes Mellitus8. Prediction of glucose concentration in type 1 diabetes patients based on Machine learning techniques9. ML-Based PCA Methods to Diagnose Statistical Distribution of Blood Glucose Levels of Diabetic Patients Section 3: Prediction of complications and risk stratification10. Overview of New trends on deep learning models for diabetes risk prediction11. Clinical applications of deep learning in diabetes and its enhancements with future predictions12. Feature Classification and Extraction of Medical Data Related to Diabetes Using Machine Learning Techniques: A Review13. ML-based predictive model for type 2 diabetes mellitus using genetic and clinical data14. Applications of IoT and data mining techniques for diabetes monitoring15. Decision-making System for the Prediction of Type II Diabetes Using Data Balancing and Machine Learning Techniques16. Comparative Analysis of Machine Learning Tools in Diabetes Prediction17. Data Analytic models of patients dependent on insulin treatment18. Prediction of Diabetes using Hybridization of Radial Basis Function Network and Differential Evaluation based Optimization Technique19. An Overview of New Trends On Deep Learning Models For Diabetes Risk Prediction Section 4: Dialysis20. Progression and Identification of heart disease risk factors in diabetic patients from electronic health records21. An Intelligent Fog Computing-based Diabetes Prediction System for Remote Healthcare Applications22. Artificial intelligence approaches for risk stratification of diabetic kidney disease23. Computational Methods for predicting the occurrence of cardiac autonomic neuropathy24. Development of a Clinical Forecasting Model to Predict Comorbid Depression in Diabetes Patients and its Application in Policy Making for Depression ScreeningSection 5: Drug design and Treatment Response25. Enhancing Diabetic Maculopathy Classification through a Synergistic Deep Learning Approach by Combining Convolutional Neural Networks, Transfer Learning, and Attention Mechanisms26. Pharmacogenomics: the roles of genetic factors on treatment response and outcomes in diabetes27. Predicting treatment response in diabetes: the roles of machine learning-based models28. Antidiabetic Potential of Mangrove Plants: An Updated Review