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

Sujata Dash is a Professor of Computer Science at North Orissa University, India, with over 25 years of teaching and research experience. She received the prestigious Titular Fellowship from the Association of Commonwealth Universities, UK, and has served as a visiting professor at the University of Manitoba, Canada. Sujata has authored over 170 publications, including journal articles, conference proceedings, and book chapters in Springer, Elsevier, IEEE, and Wiley. She holds 10 patents and has published influential textbooks and monographs. An active member of international professional associations such as ACM, IEEE, and IACSIT, she also reviews for 15 journals including Bioinformatics and IEEE ACCESS. Sujata has delivered keynote speeches, chaired sessions at global conferences, and serves on the editorial boards of around 10 international journals. Her research interests encompass machine learning, data science, big data analytics, bioinformatics, and intelligent agents, making her a leading expert in computational sciences. 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 Editor-in-Chief of Postgraduate Medical Journal. Prof Cheung’s main research interest is in cardiovascular diseases and risk factors, including hypertension and the metabolic syndrome. Gary Tse is a medical scientist focusing on personalized care, health informatics and big data analytics. He is a professor in Hong Kong Metropolitan University and holds a joint position as reader at the University of Kent and public health consultant at the Medway Council in local government, UK. He serves as a professor at the Department of Cardiology and principal investigator at the Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, China. He is a visiting professor at the Faculty of Health and Medical Sciences, University of Surrey, and an honorary associate professor at the School of Pharmacy, University College London. He is an author of more than 500 scientific papers and an expert reviewer for >50 international journals in cardiology, cardiac electrophysiology, cardiovascular biology and epidemiology and five grant bodies from New Zealand, Poland, Sweden and the UK.

• 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