Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.
In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.
This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.
Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.
In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.
This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.
1. Predictive analytics and machine learning for medical
informatics: A survey of tasks and techniques
2. Geolocation-aware IoT and cloud-fog-based solutions for
healthcare
3. Machine learning vulnerability in medical imaging
4. Skull stripping and tumor detection using 3D U-Net
5. Cross color dominant deep autoencoder for quality enhancement of
laparoscopic video: A hybrid deep learning and range-domain
filtering-based approach
6. Estimating the respiratory rate from ECG and PPG using machine
learning techniques
7. Machine learning-enabled Internet of Things for medical
informatics
8. Edge detection-based segmentation for detecting skin lesions
9. A review of deep learning approaches in glove-based gesture
classification
10. An ensemble approach for evaluating the cognitive performance
of human population at high altitude
11. Machine learning in expert systems for disease diagnostics in
human healthcare
12. An entropy-based hybrid feature selection approach for medical
datasets
13. Machine learning for optimizing healthcare resources
14. Interpretable semi-supervised classifier for predicting cancer
stages
15. Applications of blockchain technology in smart healthcare: An
overview
16. Prediction of leukemia by classification and clustering
techniques
17. Performance evaluation of fractal features toward seizure
detection from electroencephalogram signals
18. Integer period discrete Fourier transform-based algorithm for
the identification of tandem repeats in the DNA sequences
19. A blockchain solution for the privacy of patients' medical
data
20. A novel approach for securing e-health application in a cloud
environment
21. An ensemble classifier approach for thyroid disease diagnosis
using the AdaBoostM algorithm
22. A review of deep learning models for medical diagnosis
23. Machine learning in precision medicine
Dr. Pardeep Kumar is currently working as an Associate Professor in
the Department of Computer Science and Engineering at Jaypee
University of Information Technology, Waknaghat, Solan, India, and
has more than 14 years of experience in academia. Previously, he
was associated with Mody University of Technology and Science,
Laxmangarh, Sikar, Rajasthan. He completed his PhD in Computer
Science and Engineering at Uttarakhand Technical University,
Dehradun. He obtained his MTech in Computer Science and Engineering
from Guru Jambheshwar University of Science and Technology, Hisar,
Haryana, and his BTech in Information Technology from Kurukshetra
University, Kurukshetra, Haryana. Dr. Kumar has been appointed as
Associate Editor of the IEEE Access journal, as well as Guest
Editor of several SCI and Scopus Index journals of international
repute. Dr. Yugal Kumar is currently working as an Assistant
Professor (senior grade) in the Department of Computer Science and
Engineering at Jaypee University of Information Technology,
Waknaghat, Solan, India. He completed his PhD in Computer Science
and Engineering at the Birla institute of Technology, Mesra,
Ranchi, and has now more than 14 years of teaching and research
experience. He has presented and published more than 80 research
papers, as well as participated in various national and
international conferences. He is serving as a member of the
editorial review board of various journals including PLOS ONE,
Journal of Advanced Computational Intelligence and Intelligent
Informatics, and Journal of Information Processing System. Mohamed
A. Tawhid earned his PhD in Applied Mathematics from the University
of Maryland Baltimore County, Maryland, United States. From 2000 to
2002, he was a postdoctoral fellow at the Faculty of Management,
McGill University, Montreal, Quebec, Canada. Currently, he is a
Professor at Thompson Rivers University, Kamloops, British
Columbia, Canada. He has published more than 75 peer-reviewed
research papers, 13 book chapters and edited four special issues in
international journals. He has also co-authored a book published by
Springer. His research has been funded by Natural Sciences and
Engineering Research Council (NSERC) grants. Moreover, he has
served on several journals' editorial boards and worked on several
industrial projects in Canada. Fatos Xhafa, PhD in Computer
Science, is Full Professor at the Technical University of Catalonia
(UPC), Barcelona, Spain. He has held various tenured and visiting
professorship positions. He was a Visiting Professor at the
University of Surrey, UK (2019/2020), Visiting Professor at the
Birkbeck College, University of London, UK (2009/2010) and a
Research Associate at Drexel University, Philadelphia, USA
(2004/2005). He was a Distinguished Guest Professor at Hubei
University of Technology, China, for the duration of three years
(2016-2019). Prof. Xhafa has widely published in peer reviewed
international journals, conferences/workshops, book chapters,
edited books and proceedings in the field (H-index 55). He has been
awarded teaching and research merits by the Spanish Ministry of
Science and Education, by IEEE conferences and best paper awards.
Prof. Xhafa has an extensive editorial service. He is founder and
Editor-In-Chief of Internet of Things - Journal - Elsevier (Scopus
and Clarivate WoS Science Citation Index) and of International
Journal of Grid and Utility Computing, (Emerging Sources Citation
Index), and AE/EB Member of several indexed Int'l Journals. Prof.
Xhafa is a member of IEEE Communications Society, IEEE Systems, Man
& Cybernetics Society and Founder Member of Emerging Technical
Subcommittee of Internet of Things.
His research interests include IoT and Cloud-to-thing continuum
computing, massive data processing and collective intelligence,
optimization, security and trustworthy computing and machine
learning, among others. He can be reached at fatos@cs.upc.edu.
Please visit also http://www.cs.upc.edu/~fatos/ and at
http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos
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