Table of Content

Edge Computing and Machine Learning for Improving Healthcare Services

Submission Deadline: 30 September 2021 (closed)

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.

Summary

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


Keywords

Machine Learning
Edge Computing
Health Care

Published Papers


  • Open Access

    ARTICLE

    Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

    Rohit Raja, Chetan Swarup, Abhishek Kumar, Kamred Udham Singh, Teekam Singh, Dinesh Gupta, Neeraj Varshney, Swati Jain
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2015-2031, 2022, DOI:10.32604/cmc.2022.022904
    (This article belongs to this Special Issue: Edge Computing and Machine Learning for Improving Healthcare Services)
    Abstract As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while… More >

  • Open Access

    ARTICLE

    Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

    Punit Gupta, Sanjit Bhagat, Dinesh Kumar Saini, Ashish Kumar, Mohammad Alahmadi, Prakash Chandra Sharma
    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5659-5676, 2022, DOI:10.32604/cmc.2022.023056
    (This article belongs to this Special Issue: Edge Computing and Machine Learning for Improving Healthcare Services)
    Abstract In the next generation of computing environment e-health care services depend on cloud services. The Cloud computing environment provides a real-time computing environment for e-health care applications. But these services generate a huge number of computational tasks, real-time computing and comes with a deadline, so conventional cloud optimization models cannot fulfil the task in the least time and within the deadline. To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time. In order… More >

  • Open Access

    ARTICLE

    Robust Watermarking Scheme for NIfTI Medical Images

    Abhishek Kumar, Kamred Udham Singh, Visvam Devadoss Ambeth Kumar, Tapan Kant, Abdul Khader Jilani Saudagar, Abdullah Al Tameem, Mohammed Al Khathami, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Khalid Mahmood Malik
    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3107-3125, 2022, DOI:10.32604/cmc.2022.022817
    (This article belongs to this Special Issue: Edge Computing and Machine Learning for Improving Healthcare Services)
    Abstract Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) technologies are widely used in medical field. Within the last few months, due to the increased use of CT scans, millions of patients have had their CT scans done. So, as a result, images showing the Corona Virus for diagnostic purposes were digitally transmitted over the internet. The major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the internet. As a result, it is important to apply a robust and secure watermarking technique to… More >

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