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Intelligent Machine Learning Paradigm and Automation
Submission Deadline: Oct. 30, 2019

This special issue currently is open for paper submission and guest editor application.

Join as Guest Editor Submit to Special Issue
Lead Guest Editor
Department of Computer Science, School of Engineering, Stanford University, California, USA
Guest Editors
  • Yang Yung
    Biomedical Engineering Research Centre, Nanyang Technological University, Singapore
  • Xianpei Li
    Institute for Computational and Mathematical Engineering, Stanford University, California, USA
  • William Harry
    Center for Biomedical Imaging, Stanford University, California, USA
  • Mingmin Pan
    Biomedical Engineering Research Centre, Nanyang Technological University, Singapore
  • Wenli Hu
    Biomedical Engineering Research Centre, Nanyang Technological University, Singapore
  • Yanmin Yuan
    Department of Bioengineering, Harvard University, Cambridge, Massachusetts, USA
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=339). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

Special Issue Flyer (PDF)

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Special Issue

Introduction
Automated machine learning is a powerful set of techniques for quicker information investigation just as improving model precision through model tuning and better diagnostics. There is a developing network around making devices that computerize many artificial intelligence (AI) undertakings, just as different errands that are a piece of the AI work process. The worldview that epitomizes this thought is the focal point of this special issue “Intelligent Machine Learning Paradigm and Automation”. As man-made reasoning and different methods get progressively sent as key segments of current programming frameworks, the hybridization of machine learning and AI and the resultant programming is inescapable. We are living in a time of quick change, where machine learning will change each part of our lives and the texture of our general public. It will influence most human exercises from supply chains to social insurance to instruction, assembling, simulation and space investigation. These advances can improve human abilities, including natural language frameworks, and the horde of uses that have assumed control over our gadgets and are showing signs of improvement consistently as information turns out to be increasingly inexhaustible and effectively open for early-stage companies and innovators. We are looked with an extraordinary chance to make the future, similar to no other age before ever could, to characterize the new job of the state and of organizations, to examine social effect in each zone of action, and to use common assets to guarantee future ages will really have something to acquire.

Aims and Scope:

  1. Biomedical Robots
  2. Image Analysis
  3. Speech Processing
  4. Mathematics and Machine Learning
  5. Medical Engineering
  6. Artificial Intelligence
  7. Cognitive Computing
  8. Virtual Reality
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