A Special Issue on: Scalable Machine Learning for Health Care: Innovations and Applications
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Original and unpublished works on any of the topics aforementioned or related are welcome.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 here.
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