Applied Computational Intelligence and Soft Computing

Machine Learning and Visual Computing


1Temple University, Philadelphia, USA

2Medical Sieve Radiology Grand Challenge IBM Research, San Jose, USA

3Shandong University, Weihai, China

4Harbin University of Science and Technology, Harbin, China

Machine Learning and Visual Computing


With the fast development of information science, information contained in big data has raised the interest of researchers from many different disciplines. Extracting and exploring the information from big data sets are essential to applying computational intelligence and Soft Computing to natural and social sciences. Recent advances of machine learning (especially, deep learning) make it possible to gain insight into big data and extract meaningful information, which has accelerated the progression of computational intelligence. Computer vision techniques contribute to understanding image and high-dimensional data from the real world in order to produce numerical or symbolic information. Visualization methods provide various ways to demonstrate information from complex data sets. Both computer vision techniques and visualization methods can be utilized to visually demonstrate the extracted information from data sets.

To promote communication between researchers from both industry and academy, we invite investigators to contribute original research articles as well as review articles related to machine learning and visual computing techniques that will stimulate the continuing efforts to Applied Computational Intelligence and Soft Computing.

Potential topics include but are not limited to the following:

  • Advanced research on image processing and computer vision
  • Visualization methods and scientific visualization techniques
  • Recent development of machine learning and its applications
Applied Computational Intelligence and Soft Computing
 Journal metrics
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Acceptance rate8%
Submission to final decision125 days
Acceptance to publication17 days
Journal Citation Indicator0.460
Impact Factor2.9
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