+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)

PRINTER FRIENDLY

Big Data Analytics for Intelligent Healthcare Management. Advances in ubiquitous sensing applications for healthcare

  • ID: 4720958
  • Book
  • April 2019
  • 312 Pages
  • Elsevier Science and Technology

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

  • Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more
  • Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc.
  • Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Note: Product cover images may vary from those shown
1. Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges
2. Big Data Analytics Challenges and Solutions
3. Big Data Analytics in Healthcare: A Critical Analysis
4. Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer
5. Chronic TTH Analysis by EMG and GSR Biofeedback on Various Modes and Various Medical Symptoms Using IoT
6. Multilevel Classification Framework of fMRI Data: A Big Data Approach
7. Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IOT
8. Blockchain in Healthcare: Challenges and Solutions
9. Intelligence-Based Health Recommendation System Using Big Data Analytics
10. Computational Biology Approach in Management of Big Data of Healthcare Sector
11. Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis
Note: Product cover images may vary from those shown
Dey, Nilanjan
Nilanjan Dey, PhD, is visiting fellow of the University of Reading, UK, and visiting Professor at Wenzhou Medical University, China and Duy Tan University, Vietnam. He was an honorary visiting scientist at Global Biomedical Technologies Inc., USA (2012-2015). Dr. Dey has authored/edited more than 45 books with Elsevier, Wiley, CRC Press, and Springer, and published more than 300 papers. He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associated Editor of?IEEE Access and International Journal of Information Technology. He is the Series Co-Editor of Tracts in Nature-Inspired Computing (Springer), Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier), Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal Processing and Data Analysis, CRC.?His main research interests include medical imaging, machine learning, computer-aided diagnosis and data mining.
Das, Himansu
Himansu Das is working as an as Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He has received his B. Tech and M. Tech degree from Biju Pattnaik University of Technology (BPUT), Odisha, India. He has published several research papers in various international journals and conferences. He has also edited several books of international repute. He is associated with different international bodies as Editorial/Reviewer board member of various journals and conferences. He is a proficient in the field of Computer Science Engineering and served as an organizing chair, publicity chair and act as member of program committees of many national and international conferences. He is also associated with various educational and research societies like IACSIT, ISTE, UACEE, CSI, IET, IAENG, ISCA etc., His research interest includes Grid Computing, Cloud Computing, and Machine Learning. He has also 10 years of teaching and research experience in different engineering colleges.
Naik, Bighnaraj
Bighnaraj Naik is an Assistant Professor in the Department of Computer Application, Veer SurendraSai University of Technology (Formerly UCE Burla), Odisha, India. He has published more than 90 research papers in various reputed peer reviewed International Journals, Conferences and Book Chapters. He has edited eleven books from various publishers such as Elsevier, Springer and IGI Global. At present, he has more than ten years of teaching experience in the field of Computer Science and IT. He is a member of IEEE. His area of interest includes Data Mining, Computational Intelligence, Soft Computing and its applications.
Behera, H S
Dr. Himansu Sekhar Behera is currently working as an Associate Professor and Head of the Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), India. He received his Doctor of Philosophy in Engineering (Ph.D.) from Biju Pattnaik University of Technology (BPUT), India. His research and development experience includes over 19 years in academia spanning different technical Institutes in India. His research interests include Data Mining, Soft Computing, Evolutionary Computation, Machine Intelligence and Distributed Systems. He has authored or co-authored over 100 research papers various international conferences and journals, as well as contributing several book chapters. He has edited 11 books and serves as an associate editor / member of the editorial board of various international journals and also guest edited 8 special issues on various topics of Inderscience and IGI Global Journals.
Note: Product cover images may vary from those shown
Adroll
adroll