Machine Learning and Block-chain based solution for privacy and access
control in IoT
https://scpe.org/index.php/scpe/CFP_SI_MLB_IOT
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Original and unpublished works on any of the topics aforementioned or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
* SCPE does NOT charge any fees for publishing Open Access papers
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results from June 2023
* SCPE Web of Science Impact factor = 1.1 (2023 for 2022 -- first impact
factor) *
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papers for Special Issues), sign to:
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