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Big Data Analytics for Smart And Connected Health

Submission Deadline: June 30, 2016

IEEE Access invites manuscript submissions in the area of Big Data Analytics for Smart And Connected Health.

Smart and mobile devices are endowing the traditional healthcare with mobility, connection and intelligence, which have given birth to E-Health & Mobile Health. Meanwhile these smart devices continuously produce very large amounts of structured and unstructured data such that healthcare practitioners are increasingly facing difficulties in managing and capitalizing on them to their advantage. Compared to the results derived from mining the conventional datasets, unveiling the huge volume of interconnected heterogeneous big data has the potential to maximize our knowledge and insights in the healthcare domain. However, this also brings a series of new challenges to the research community. Big data analytics must deal with heterogeneity, volume, velocity, accuracy, privacy, and interactiveness that existing data analysis techniques and algorithms are incapable of. Altogether the aim is the development of advanced data-aware knowledge based healthcare systems.

This Special Section in IEEE Access focuses on bringing together researchers and practitioners in biomedical, machine learning and mobile computing fields to showcase the progress, algorithms, and applications of analyzing and extracting knowledge from large-scale datasets for smart and connected health.

Topics of interest include but are not limited to:

• Store and retrieve ever growing dataset from smart and mobile devises
• New protocols and interfaces for integration/distribution of newly arrived data
• Outlier detection algorithms for big data mining
• New approach to tackle large-scale bioinformatics classification problems
• Energy efficient data analytics scheme for high dimensional data
• Cloud computing and infrastructure for eHealth
• Machine learning frameworks designed on top of big data technologies
• Predictive models gaining valuable knowledge from big data
• Information diffusion models and methods
• Large stream data mining techniques on users’ historical data
• Optimize resource usage and energy consumption when executing the analytics application
• Typical mobile database management tools
• Novel applications and case studies for healthcare based on smart and mobile devices
• Big data privacy preservation in cloud environment
• Health management models for big data

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Yuan Zhang, Professor and IEEE SM, University of Jinan, China

Guest Editors:
1. Lin Zhang, Professor and Dean, Beijing University of Posts and Telecommunications, China
2. Eiji Oki, Professor and IEEE Fellow, The University of Electro-Communications, Japan
3. Nitesh V Chawla, Professor of Computer Science and Engineering, University of Notre Dame, USA
4. Anton Kos, Assistant Professor, University of Ljubljana, Slovenia

IEEE Access Editor in Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to: http://mc.manuscriptcentral.com/ieee-access

For information regarding IEEE Access including its publication policy and fees, please visit the website http://www.ieee.org/ieee-access For inquiries regarding this Special Section, please contact: Bora M. Onat, Managing Editor, IEEE Access (Phone: (732) 562-6036, ieeeaccess@ieee.org)