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Home / Journals / American Journal of Neural Networks and Applications / Intelligent Machine Learning Paradigm and Automation
Intelligent Machine Learning Paradigm and Automation

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Lead Guest Editor:
P. S. Jagadeesh Kumar
Department of Computer Science, School of Engineering, Stanford University, California, USA
Guest Editors
Yang Yung
Biomedical Engineering Research Centre, Nanyang Technological University
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
Wenli Hu
Biomedical Engineering Research Centre, Nanyang Technological University
Yanmin Yuan
Department of Bioengineering, Harvard University
Cambridge, Massachusetts, USA
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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