"This book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss artificial intelligence applications in depression, hypertension and diabetes management. The text also presents an artificial intelligence chatbot for depression, diabetes, and hypertension self-help. This book: Provides a structured overview of recent developments of artificial intelligence applications in the healthcare sector. Presents an in-depth understanding of how artificial intelligence techniques can be applied to diabetes management. Showcases supervised learning techniques based on datasets for depression management. Discusses artificial intelligence chatbot for diabetes, depression, and hypertension self-care. Highlights the importance of artificial intelligence in managing and predicting diabetes, hypertension, and depression. The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering"--
This book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss artificial intelligence applications in depression, hypertension and diabetes management.
This book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss artificial intelligence applications in depression, hypertension and diabetes management. The text also presents an artificial intelligence chatbot for depression, diabetes, and hypertension self-help.
This book:
- Provides a structured overview of recent developments of artificial intelligence applications in the healthcare sector.
- Presents an in-depth understanding of how artificial intelligence techniques can be applied to diabetes management.
- Showcases supervised learning techniques based on datasets for depression management.
- Discusses artificial intelligence chatbot for diabetes, depression, and hypertension self-care.
- Highlights the importance of artificial intelligence in managing and predicting diabetes, hypertension, and depression.
The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.
1. Artificial Intelligence and Digital Health Twin Applications in
Healthcare A Systematic Review.
2. Early Detection of Diabetic Foot Ulcers
Using Optical Flow Based Ensemble Learning CNN Framework.
3. Identification
of potential biomarkers for diabetes mellitus using Gene expression datasets,
Machine learning, and R packages to predict the risk for diabetes.
4. Machine
learning for chronic diseases.
5. AI Chatbot For Diabetes Self-Help.
6.
Future of Diabetic Management Using Artificial Intelligence.
7. Detection of
Generalized Anxiety Disorder.
8. Machine learning techniques for prediction
of hypertension.
9. Food recommendation for hypertensive persons.
10.
Optimized Support Vector Machines for Detection of Mental Disorders.
11.
Artificial Intelligence Applications in depression management.
12. Depression
Prediction Using Machine Learning Techniques.
13. Future of depression
management using Artificial Intelligence.
14. AI chatbot for depression
self-help.
15. Artificial Intelligence in Healthcare: Futuristic
Opportunities.
Gourav Bathla is working as Associate Professor at GLA University, India. He has 17 years of teaching and research experience. He has completed PhD from Punjabi University, Punjab, India. He has completed M.E from Delhi College of Engineering,India. He is GATE qualified with All India Rank 59. He is an active researcher and published 25 research papers in reputed Journals and 10 research papers in International Conferences. He has published 4 patents. His areas of interest are Big Data, Machine Learning, Deep Learning, NLP, and Programming Languages. He is reviewer of various Journals such as Engineering Applications of Artificial Intelligence, Scientific Reports, Journal of Supercomputing, Online Information Review, IAES International Journal of Artificial Intelligence etc. and TPC member of various International Conferences.
Sanoj Kumar works as an assistant professor (SG) at the University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India. Earlier, he worked as a postdoctoral fellow with the Department of Mathematics and Computer Science, University of Udine, Italy, from October 2013 to September 2014. He completed his PhD in mathematics from IIT Roorkee, India, in 2013. Dr. Kumar's research interests include image processing, computer vision, and machine learning. He has authored more than 22 papers published in refereed international journals and conferences, including those from Elsevier, Springer, Taylor & Francis, etc. He has also authored two book chapters. He is a reviewer for various journals such as ISA Transactions (Elsevier), IET Image Processing, Optical Engineering (SPIE), Applied Mathematical Modeling (Elsevier), etc. He also got the best paper and young scientist awards in NETCRYPT 2020. His teaching area includes Engineering Mathematics I, Engineering Mathematics II, discrete mathematics, graph theory, optimization techniques, numerical analysis, linear algebra, probability and statistics, real analysis, complex analysis, differential equation etc.
Harish Garg is working as an Associate Professor at Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab, India. He is ranked in the World's Top 2% Scientist List and Rank #1 in India & World Rank #229 published by Stanford University in the consecutive three years 2020, 2021, 2022. He is the recipient of the Obada-Prize 2022 Young Distinguished Researchers. He is also the recipient of the Top-Cited paper by an India-based author (2015 2019) from Elsevier Publisher. He also serves as an advisory board member of the Universal Scientific Education and Research Network (USERN). Dr. Garg's research interests include Computational Intelligence, Multi-criteria decision making, Evolutionary algorithms, Reliability analysis, Expert systems and decision support systems, Computing with words and Soft Computing. He has authored more than 410 papers (over 360 are SCI) published in refereed International Journals including IEEE Transactions, Elsevier, Springer etc. He has also authored seven book chapters. Also, he edited 8 books from Elsevier, Springer and other publishers. His Google citations are over 18240 with H-index- 76. Dr. Garg also serves on editorial boards of several leading international journals, this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of IEEE Transaction of Fuzzy Systems, Soft Computing, Alexandria Engineering Journal, Journal of Intelligent & Fuzzy Systems, Complex and Intelligent Systems, Journal of Industrial & Management Optimization, CAAI Transactions on Intelligence Technology, etc. For more details about him, kindly follow his webpage https://sites.google.com/site/harishg58iitr/home.
Deepika Saini is an assistant professor at Graphic Era (deemed to be) University, Dehradun, Uttarakhand, India. Previously, in 2016, she received her Ph.D. in Mathematics from IIT Roorkee in India. She completed her M.Sc. in Mathematics from H.N.B. Garhwal University, Srinagar, Uttarakhand, India. She won the gold medal for securing first place among all PG students in her M.Sc. in 2005. Dr. Saini's research interests include computer vision, image processing, computer graphics, and their applications in various branches of engineering. She has published more than 16 papers in various international journals and reputed conferences, including those of Elsevier, Springer, Taylor & Francis, etc. She has also authored a book chapter. She also got the best paper award in NETCRYPT 2020. Her teaching area includes Mathematics I, Mathematics II, Mathematics III, discrete mathematics, computer based numerical and statistics techniques, linear programming, numerical analysis, linear algebra, algebra, differential equation etc.
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