Machine Learning, Big Data, and IoT for Medical Informatics
Published on 16. June 2021
Book
Paperback/Softback
458 pages
978-0-12-821777-1 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Healthcare engineers, informaticians, bio-statisticians, researchers, graduate students, data analyst, physician, and any other person having interest in the field of medical informatics. Scientist, researchers, practitioners, and academicians in higher education institutions including universities and vocational colleges.
Product notice
Paperback (trade)
Illustrations
Approx. 110 illustrations; Illustrations
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 24 mm
Weight
783 gr
ISBN-13
978-0-12-821777-1 (9780128217771)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Pardeep Kumar | Yugal Kumar | Mohamed A. Tawhid
Machine Learning, Big Data, and IoT for Medical Informatics
E-Book
06/2021
Academic Press
€113.00
Available for download
Persons
Dr. Pardeep Kumar is a Professor in the Department of Computer Science & Engineering at Jaypee University of Information Technology (JUIT), Wakanaghat. With more than 17 years of extensive experience in higher education, Dr. Kumar has served as Executive General Chair of 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) and 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC) , Guest Editor of Special Issue on "Robust and Secure Data Hiding Techniques for Telemedicine Applications", Multimedia Tools and Applications: An International Journal, Lead Guest Editor of Special Issue on "Recent Developments in Parallel, Distributed and Grid Computing for Big Data", published in International Journal of Grid and Utility Computing, Guest Editor of Special Issue on "Advanced Techniques in Multimedia Watermarking", published in International Journal of Information and Computer Security. Dr. Kumar is an Associate Editor of IEEE Access Journal. Dr. Kumar's research focus includes machine & deep learning optimized Internet of Things (IOT) solutions to real life complex problems; blockchain, Internet of Things, data science and artificial intelligence for smart cities including AI driven health and medical informatics, big data analytics.
Dr. Kumar is an Associate Professor in the Department of Computer Engineering, School of Technology Management and Engineering, NMIMS University, Chandigarh Campus, Mumbai, India. Prior to joining NMIMS University, Dr. Kumar was associated with Jaypee University of Information Technology (JUIT), Wakanaghat, Himachal Pradesh, India. He completed his PhD in Computer Science & Engineering from Birla institute of Technology, Mesra, Ranchi. He has more than 17 years of teaching and research experience, has published over 120 research papers in reputed journals, edited more than eight books, and has presented at various national and international conferences. His primary area of research includes medical informatics, meta-heuristic algorithms, data clustering, swarm intelligence, pattern recognition, medical data analytics.
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
Dr. Kumar is an Associate Professor in the Department of Computer Engineering, School of Technology Management and Engineering, NMIMS University, Chandigarh Campus, Mumbai, India. Prior to joining NMIMS University, Dr. Kumar was associated with Jaypee University of Information Technology (JUIT), Wakanaghat, Himachal Pradesh, India. He completed his PhD in Computer Science & Engineering from Birla institute of Technology, Mesra, Ranchi. He has more than 17 years of teaching and research experience, has published over 120 research papers in reputed journals, edited more than eight books, and has presented at various national and international conferences. His primary area of research includes medical informatics, meta-heuristic algorithms, data clustering, swarm intelligence, pattern recognition, medical data analytics.
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
Editor
Department of Computer Science and Engineering, Jaypee University of Information Technology (JUIT), Solan, Himachal Pradesh, India
School of Technology Management and Engineering, NMIMS University, Chandigarh Campus, Mumbai, India
Professor, Department of Mathematics and Statistics, Thompson Rivers University, Kamloops, British Columbia, Canada
Series Editor
Content
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
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