Big Data Analytics for Intelligent Healthcare Management

Big Data Analytics for Intelligent Healthcare Management

ISBN-10:
012818146X
ISBN-13:
9780128181461
Pub. Date:
04/13/2019
Publisher:
Elsevier Science
ISBN-10:
012818146X
ISBN-13:
9780128181461
Pub. Date:
04/13/2019
Publisher:
Elsevier Science
Big Data Analytics for Intelligent Healthcare Management

Big Data Analytics for Intelligent Healthcare Management

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Overview

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.


Product Details

ISBN-13: 9780128181461
Publisher: Elsevier Science
Publication date: 04/13/2019
Series: Advances in ubiquitous sensing applications for healthcare , #3
Pages: 312
Product dimensions: 7.50(w) x 9.25(h) x (d)

About the Author

Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He also holds a position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded 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 (SpringerNature), Data-Intensive Research(SpringerNature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He was an associate editor of IET Image Processing and editorial board member of Complex & Intelligent Systems, Springer Nature. He is an editorial board member of Applied Soft Computing, Elsevier. He is having 35 authored books and over 300 publications in the area of medical imaging, machine learning, computer aided diagnosis, data mining, etc. He is the Fellow of IETE and 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 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.

Bighnaraj Naik is an Assistant Professor in the Department of Computer Application, Veer Surendra Sai University of Technology (formerly UCE Burla), Odisha, India. He has published more than 100 research articles in various peer reviewed international journals, conferences, and book chapters. He has edited 10 books for publishers including Elsevier, Springer, and IGI Global. At present, he has more than 10 years of teaching experience in the field of computer science and information technology. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and his areas of interest include data science, data mining, machine learning, deep learning, computational intelligence (CI), and CI’s applications in science and engineering. He has served as Guest Editor of various special issues of journals such as Information Fusion (Elsevier), Neural Computing and Applications (Springer), Evolutionary Intelligence (Springer), International Journal of Computational Intelligence Studies (Inderscience), and International Journal of Swarm Intelligence (Inderscience). He is an active reviewer of various journals from publishers including IEEE Transactions, Elsevier, Springer, and Inderscience. Currently, he is undertaking a major research project as Principal Investigator, which is funded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India.

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.

Table of Contents

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

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A comprehensive guide to the application of Big Data analytics for biomedical engineering and clinical and healthcare research and management

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