Scalable Machine Learning for Health Care: Innovations and Applications
https://scpe.org/index.php/scpe/CFP_SI_SMLHC
Introduction:
Scalable machine learning is a rapidly evolving field with wide-ranging
applications in various domains, including health care. With the increasing
demand for effective and efficient solutions to complex health problems,
machine learning is emerging as a critical technology for driving
innovation in health care. The use of machine learning in health care has
the potential to revolutionize the way medical diagnoses are made,
treatment plans are developed, and patient outcomes are improved.
Objective:
The goal of this special issue is to present recent advances in the field
of scalable machine learning for health care and to highlight the impact of
these technologies on real-world health problems. The special issue aims to
provide a comprehensive overview of the current state of the art in
scalable machine learning for health care, including both theoretical and
practical aspects. The objective is to bring together researchers,
practitioners, and decision makers in the field to share their experiences,
insights, and best practices.
Recommended topics (but not limited to):
The following are the recommended topics for this special issue:
• Overview of recent advances in machine learning algorithms for health
care,
• Data mining in health care,
• Artificial intelligence in health care,
• Deep learning for health care,
• Metaverse and health care,
• Digital twins in health care,
• Transfer learning in health care,
• Explainable AI (XAI) for health care,
• IoT and machine learning in health care,
• Cloud computing and machine learning in health care,
• Design and implementation of scalable machine learning systems for health
care,
• Real-world deployment and evaluation of machine learning systems in
health care,
• Case studies and evaluations of machine learning systems in real-world
health care settings,
• Discussion of future directions and challenges in the field of scalable
machine learning for health care,
• Other relevant topics related to scalable machine learning for health
care.
Important dates:
+ Submission deadline: 31 October, 2023
+ Authors notification: 30 November, 2023
+ Revised version deadline: 31 December, 2023
+ Completion of Special Issue: March, 2024
Submission guidelines:
Original and unpublished works on any of the topics aforementioned or
related are welcome. The SCPE journal has a rigorous peer-reviewing process
and papers will be reviewed by at least two referees. All submitted papers
must be formatted according to the journal's instructions, which can be
found at the journal WWW site (please note the LaTeX requirement!)
During submission please select a Special Issue that you want to submit to
and provide this information in the Comments for the Editor field.
Guest Editors:
++ Lead: Dr. Chiranji Lal Chowdhary, Associate Professor, School of
Information Technology and Engineering, Vellore Institute of Technology
Vellore, India, email: prof.chowdhary@gmail.com
++ Dr Mohammad Zubair Khan, Department of Computer Science and Information,
Taibah University Medina 42353 Saudi Arabia, email: zubair.762001@gmail.com
++ Dr. Yulei Wu, Associate Professor, Department of Computer Science,
Faculty of Environment, Science and Economy, University of Exeter, Exeter,
EX4 4QF, email:
y.l.wu@exeter.ac.uk
++ Dr. Dharm Singh, Namibia University, Namibia, email: dsingh@nust.na
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