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

Sujata Dash holds the position of Professor at the Information Technology School of Engineering and Technology, Nagaland University, Dimapur Campus, Nagaland, India, bringing more than three decades of dedicated service in teaching and mentoring students. She has been honoured with the prestigious Titular Fellowship from the Association of Commonwealth Universities, United Kingdom. As a testament to her global contributions, she served as a visiting professor in the Computer Science Department at the University of Manitoba, Canada. With a prolific academic record, she has authored over 200 technical papers published in esteemed international journals, and conference proceedings, and edited book chapters by reputed publishers Serving as a reviewer and Associate Editor for approximately 15 international journals. Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University, Odisha, India in the year 2013. He is working as a professor at Krupajal Engineering College under BPUT, Odisha, India. He has more than 20 years of teaching and research experience His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. In addition to research, he has guided two PhD students and 31 M. Tech students. He has published 150 International Journal papers (100 Scopus index). His professional activities include roles as Book Series Editor (CRC Press, Apple Academic Press, Wiley-Scrivener), Associate Editor, Editorial board member and/or reviewer of various International Journals. He is an Associate with no. of the conference societies. He has more than 250 international publications, 5 authored books, 25 edited and upcoming books; 40 book chapters into his account. He is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and OBA.OMS, SMIACSIT, SMUACEE, CSI. Willy Susilo received his Ph.D. degree in Computer Science from the University of Wollongong, Australia. He is a Distinguished Professor the Head of the School of Computing and Information Technology and the director of the Institute of Cybersecurity and Cryptology (iC2) at the University of Wollongong. Recently, he was awarded an Australian Laureate Fellowship, which is the most prestigious award in Australia, due to his contribution in cloud computing security. He was previously awarded a prestigious ARC Future Fellow by the Australian Research Council (ARC) and the Researcher of the Year award in 2016 by the University of Wollongong. He is a Fellow of IEEE, Australian Computer Society (ACS), IET and AAAI. His main research interests include cybersecurity, cryptography and information security. His work has been cited more than 25,000 times in Google Scholar. He is the Editor-in-Chief of the Elsevier Computer Standards and Interfaces and the MDPI Information journal. He has served as a program committee member in dozens of international conferences. He is currently serving as an Associate Editor in several international journals, including IEEE Transactions in Dependable and Secure Computing. Previously, he has served in many top-tier journals, such as IEEE Transactions in Information Forensics and Security. He has published more than 500 research papers in the area of cybersecurity and cryptology. Bernard Cheung went to Sevenoaks School and studied Medicine at the University of Cambridge. He was Professor of Clinical Pharmacology and Therapeutics at the University of Birmingham before returning to Hong Kong and being appointed the Sun Chieh Yeh Heart Foundation Professor in Cardiovascular Therapeutics. He was a Consultant Physician of Queen Mary Hospital and the Director of the Phase 1 Clinical Trials Units in Queen Mary Hospital and the University of Hong Kong-Shenzhen Hospital. Currently, he is the Biotechnology Director in the Innovation and Technology Commission. He is also the President of the Federation of Medical Societies of Hong Kong and the Edi...

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. Antid...