Special Issue "Edge Computing and Machine Learning for Improving Healthcare Services"

Submission Deadline: 30 September 2021 (closed)
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Guest Editors
Dr. Alok Kumar Singh Kushwaha, Guru Ghasidas University, India.
Dr. Ashish Khare, University of Allahabad, India.
Dr. Jeonghwan Gwak, Korea National University of Transportation (KNUT), Korea.
Dr. Nguyen Thanh Binh, Ho Chi Minh City University of Technology, Vietnam.


The use of technology in the healthcare domain has been rapidly growing across the world. Edge computing coupled with machine learning can be used for better diagnosis and treatment of patients. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth. With the advent of new paradigms like the Internet of Medical Things (IoMT), lots of data is generated from sensors that can be further analyzed for various purposes.


Taking technology to low-resource settings is a challenge due to bandwidth and infrastructural issues. In developing countries, particularly in rural areas, it is not possible to transfer data in real time to the server. Edge computing can play a vital role in improving healthcare service delivery by localizing the processing and storage of healthcare data. To address this issue, new types of service delivery and architecture models are required. Further, there are numerous research issues, such as those concerning the technical specifications of healthcare systems, optimized machine learning models, newer application areas, and so on. Data processing and analysis can be done in the cloud, but it will require a lot of bandwidth, a long time to get the results, and privacy concerns that aren't acceptable for these applications. One option is to use edge computing, which keeps the data in place and brings the applications close to the data in order to reduce communication costs.

Electronic health records (HER), telemedicine, remote monitoring tools, wearable sensors etc are some of the components of the digital healthcare ecosystem Edge computing and machine learning can have numerous applications in healthcare. This special issue aims at providing a platform to publish advances in improving healthcare services, especially with the applications of edge computing and machine learning. We invite the submission of original research and review articles discussing the new challenges in the field. Potential topics include but are not limited to the following:

• Analysis and management of health records

• Data storage and processing in an edge environment

• Deep learning approaches for healthcare

• Remote health monitoring

• Mental health and well-being

• Internet of Medical Things (IoMT)

• Real-time healthcare applications

• Identification of communicable and non-communicable diseases using machine learning

• Optimization of deep learning models

• Data compression

• Various privacy and security issues relevant to healthcare data in an edge computing environment

Machine Learning
Edge Computing
Health Care