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

Langue : Anglais

Coordonnateurs : Dey Nilanjan, Das Himansu, Naik Bighnaraj, Behera H S

Couverture de l’ouvrage Big Data Analytics for Intelligent Healthcare Management

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.

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
Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, JIS University, Kolkata, India. He is a visiting fellow of the University of Reading, UK, and also holds the position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held the honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He got his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (Springer Nature), Data-Intensive Research (Springer Nature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He is an associate editor of IET Image Processing (Wiley) and an editorial board member of Complex & Intelligent Systems (Springer Nature), Applied Soft Computing (Elsevier), and more. He has written 110 books and over 300 other publications in the areas of medical imaging, machine learning, computer aided diagnosis, data mining, etc. His works have been cited over 15,000 times. He is India’s Ambassador to the International Federation for Information Processing—Young ICT Group and a senior member of IEEE.
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 in
  • 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