初稿截稿日期：2020-03-08

Submission Instructions

Authors should submit papers reporting original work that are currently not under review or published elsewhere. Accepted papers will be published in the conference proceedings.Submissions must not exceed 12 pages(including citations)in CCIS (Communications in Computer and Information Science) format. We encourage authors to cite related work comprehensively, and when citing conference papers please also consider to cite their extended journal versions if applicable.

NCAA 2020 will employ double-blind reviewing process, every research paper submitted to NCAA 2020 will undergo a “double-blind” reviewing process: the PC members and referees who review the paper will not know the identity of the authors. To ensure anonymity of authorship, authors must prepare their manuscript as follows:

Author’s names and affiliations must not appear on the title page or elsewhere in the paper.

Funding sources must not be acknowledged on the title page or elsewhere in the paper.

Research group members, or other colleagues or collaborators, must not be acknowledged anywhere in the paper.

Source file naming must also be done with care, to avoid identifying the author’s names in the paper’s associated metadata. For example, if your name is Jane Smith and you submit a PDF file generated from a .dvi file called Jane-Smith.dvi, your authorship could be inferred by looking into the PDF file.

It is the responsibility of authors to do their very best to preserve anonymity. Papers that do not follow the guidelines here, or otherwise potentially reveal the identity of the authors, are subject to immediate rejection. Because of the double blind review policy, the submission of an extended version of a short paper which has published elsewhere is strongly discouraged in NCAA 2020.

Any submitted paper violating the length, file type, or formatting requirements will be rejected without review. For any problems with the submission system, please contact the PC co-chairs directly

TOPICSof interest are relevant to building practical systems are within its scope, including but not limited to:

adaptive computing

algorithms

applicable neural networks theory

applied statistics

architectures

artificial intelligence

benchmarks

case histories of innovative applications

fuzzy logic

genetic algorithms

hardware implementations

hybrid intelligent systems

intelligent agents

intelligent control systems

intelligent diagnostics

intelligent forecasting

machine learning

neural networks

neuro-fuzzy systems

pattern recognition

performance measures

self-learning systems

software simulations

supervised and unsupervised learning methods

system engineering and integration