CFP to Special Issue in TCBB on Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare - Deadline Dec. 30, 2021

[Apologies for multiple postings]

Dear Professor(Dr.),

It is my pleasure and honor to share this CFP in IEEE/ACM Transactions on
Computational Biology and Bioinformatics.

Please consider submitting a paper to a Special Issue on "Deep Learning
Empowered Big Data Analytics in Biomedical Applications and Digital
Healthcare" for TCBB. The deadline is Dec. 30, 2021.

Also, please kindly help distribute the CFP (see following) and encourage
your colleagues, friends, and students to make submissions. Your strong
supports are highly appreciated.

[TCBB Call for Papers]
https://www.computer.org/digital-library/journals/tb/call-for-papers-special-issue-on-deep-learning-empowered-big-data-analytics-in-biomedical-applications-and-digital-healthcare


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                          IEEE/ACM Transactions on Computational Biology
and Bioinformatics

                                             Special Issue on

                   Deep Learning-Empowered Big Data Analytics in Biomedical
Applications and Digital Healthcare

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Aims and Scope

Deep learning and big data analysis are among the most important research
topics in the fields of biomedical applications and digital healthcare.
With the fast development of AI and IoT technologies, deep learning for big
data analytics, including affective learning, reinforcement learning, and
transfer learning, are widely applied to sense, learn, and interact with
human health. Examples of biomedical application include smart
biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and
eye tracking. These biomedical applications collect healthcare data through
remote sensors and transfer the data to a centralized system for analysis.
With an enormous amount of historical data, deep learning and big data
analysis technologies are able to identify potential linkage between
features and possible risks, raise important decision for medical
diagnosis, and provide precious advice for better healthcare treatment and
lifestyle. Although significant progress has been made with AI, deep
learning, and big data analysis technologies for medical and healthcare
research, there remain gaps between the computer-aided treatment design and
real-world healthcare demands. In addition, there are unexplored areas in
the fields of healthcare and biomedical applications with cutting-edge AI
and deep learning technologies. Therefore, exploring the possibility of
deep learning and big data analysis technology in the fields of biomedical
applications and healthcare is in high demand.

Topics of interest include (but are not limited to):

• Deep learning in medicine, human biology, and healthcare
• Deep learning-based clinical decision making
• Deep learning in biomedical applications
• Deep learning in medical and healthcare education
• Deep learning-based computer vision on medical images
• Big data with smart computing in bioinformatics and biomechanics
• Big data analytics for human biology and healthcare services
• Big data with intelligent IoT for smart healthcare
• Big data analytics in biomedical services
• Knowledge-based or agent-based models for biological systems
• Distributed systems in medical and healthcare services
• Intelligent devices and instruments for medical and healthcare services
• Intelligent and process-aware information systems in human biology,
healthcare, and medicine

Submissions

Authors should prepare their manuscript according to the Author Information
of IEEE/ACM Transactions on Computational Biology and Bioinformatics
available from https://www.computer.org/csdl/journal/tb, and submit online
at: https://mc.manuscriptcentral.com/tcbb-cs.
To ensure that the manuscript is correctly identified for inclusion into
the special issue, authors must select "SI - Deep Learning-Empowered Big
Data Analytics in Biomedical Applications and Digital Healthcare" when they
reach the “Article Type” step in the submission process.

Important Dates

Paper Submission Deadline: December 30, 2021

First Round of Reviews Deadline: March 30, 2022

Submission of Revision Deadline: May 30, 2022

Second Round of Reviews Deadline: July 30, 2022

Decision of Acceptance Deadline: August 30, 2022

Guest Editors

• Xiaokang Zhou, Shiga University, Japan
• Carson Leung, University of Manitoba, Canada
• Kevin Wang, The University of Auckland, New Zealand
• Giancarlo Fortino, University of Calabria, Italy

Contact Information

Dr. Zhou (zhou@biwako.shiga-u.ac.jp)



-- 
Xiaokang Zhou (周 暁康), Ph.D.
Associate Professor
Faculty of Data Science,
Shiga University
1-1-1 Banba, Hikone, Shiga 522-8522, Japan
Email: zhou@biwako.shiga-u.ac.jp
Phone: +81-749-27-1290