Special Issue "Machine Learning and Computational Methods for Disease Detection and Prediction"

Submission Deadline: 15 September 2020 (closed)
Guest Editors
Dr. Ashutosh Kumar Dubey, Chitkara University Institute of Engineering and Technology, India
Dr. Sreenatha Anavatti, University of New South Wales, Australia
Dr. Ahmed M. Elmisery, University of South Wales, UK
Dr. Abhishek Kumar, Chitkara University, India

Summary

The advancement of machine learning and computational methods has become a driving force for global healthcare development and transformation. Machine learning allows a system to learn from the environment, through re-iterative processes. Along with the computational methods, it also improves itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The goal of this thematic issue is to explore how machine learning and computational methods in disease and healthcare applications can help human beings to lead healthy lives. Specifically, innovative contributions that either solve or advance the understanding of issues related to new technologies and applications in the real world in the direction of disease detection and prediction are very welcome.

 

It also seeks to not only bring solutions that combine state-of-the-art prediction methods for exploiting the huge health and biodata resources available, but also emerging methods that more generally describe the successful application of artificial intelligence and big data analytic methodologies to issues such as disease prediction, machine learning, deep learning, knowledge discovery, big data, and feature selection in the medical domain as well as healthcare, biology, and wellbeing domains. The main idea is to cover health data analytics issues addressing all facets of the solutions from the disease prediction and detection in healthcare technology perspective.

 

The general idea behind this is to disseminate disease prediction and healthcare solution contributions from various engineering, scientific, and social settings that exploit data analytics, machine learning, and data mining techniques.


Keywords
Potential topics include, but are not limited to the following:
• Computational methods for disease prediction and detection
• Data mining and knowledge discovery in healthcare
• Machine and deep learning approaches for disease and health data
• Decision support systems for healthcare and wellbeing
• Optimization for healthcare problems
• Medical expert systems
• Biomedical applications
• Applications of artificial intelligence techniques in healthcare systems
• Intelligent computing and platforms in medicine and healthcare
• Biomedical text mining
• Big data frameworks and architectures for applied medical and health data
• Visualization and interactive interfaces related to healthcare systems

Published Papers
  • Detection of COVID-19 Enhanced by a Deep Extreme Learning Machine
  • Abstract The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are… More
  •   Views:757       Downloads:522        Download PDF

  • Design and Development of Collaborative AR System for Anatomy Training
  • Abstract Background: Augmented Reality (AR) incorporates both real and virtual objects in real-time environments and allows single and multi-users to interact with 3D models. It is often tricky to adopt multi-users in the same environment because of the devices’ latency and model position accuracy in displaying the models simultaneously. Method: To address this concern, we present a multi-user sharing technique in the AR of the human anatomy that increases learning with high quality, high stability, and low latency in multiple devices. Besides, the multi-user interactive display (HoloLens) merges with the human body anatomy application (AnatomyNow) to teach and train students, academic… More
  •   Views:585       Downloads:363        Download PDF