Computational and Mathematical Methods in Medicine

Machine Learning for Human-Machine Interaction


Publishing date
01 Jun 2022
Status
Published
Submission deadline
21 Jan 2022

Lead Editor

1University of Adelaide, Adelaide, Australia

2University of Macau, Macau, China

3University 2020 Foundation, Singapore


Machine Learning for Human-Machine Interaction

Description

As society becomes increasingly more informatized, humans require a higher level of computer intelligence. Human-computer interaction (HCI) is not limited to the original hardware-based interaction. Some relatively smarter interaction methods are gradually appearing in people's lives, such as a series of more intelligent methods related to face recognition, gesture recognition, and voice recognition. Intelligent systems can help to establish communication between humans and computers. The emergence of these more convenient interaction methods has become a major development trend in the current field of HCI. The goal of HCI development is naturally to make computers serve and adapt to human needs. People-centred rather than forcing people to adapt to the computer.

Obtaining data from HCI enables us to learn more effectively and build smarter systems. Machine learning is an important branch of artificial intelligence. It has made great progress in many fields and has demonstrated strong research and development (R&D) potential. With the application of machine learning technology in HCI, machines are becoming more intelligent.

The aim of this Special Issue is to bring together original research and review articles discussing the latest developments in the field of human-machine interaction based on machine learning.

Potential topics include but are not limited to the following:

  • Voice interaction based on machine learning
  • Face and expression recognition based on neural networks
  • Deep learning models for medical image reconstruction, restoration, and registration
  • Gesture and motion recognition
  • Artificial intelligence for smart medical analysis
  • Artificial intelligence techniques for computer-aided detection and diagnosis
  • Brain-computer interface
  • Analysis of human factors in the design of machines

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