NCAA is an annual international neural computing conference, which showcases state-of-the-art R&D activities in neural computing systems and their industrial and engineering applications. It provides a forum for technical presentations and discussions among neural computing researchers, developers and users from academia, business and industry.
The 2020 NCAA will be held in Shenzhen, China on July 3-5, 2020. China, a country which has a long history of 5000 years, is the perfect venue for addressing contemporary neural computing issues as new AI-related technical trends have been arising out of China’s top-notch IT infrastructure and cultural dynamicity. Shenzhen is a sub-provincial city of Guangdong province in southern China, located at the border with the Hong Kong Special Administrative Region and 160 km south of the provincial capital of Guangzhou. Shenzhen is a centre of foreign investment and since the late 1970s has been one of the fastest growing cities in the world. Shenzhen’s major tourist attractions include the Chinese Folk Culture Villages, the Window of the World, Happy Valley, etc. It is also famous for the great variety of cuisines that its numerous restaurants provide. Shenzhen Bao’an International Airport is connected with over 100 flights per day from China and is also linked with over 50 direct flights per day to international destinations in South Korea, Japan, Taiwan, Thailand, Singapore, etc. Additionally, at less than 30 minutes by road from the airport is the conference venue, Harbin Institute of Technology, Shenzhen.
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
TOPICS of interest are relevant to building practical systems are within its scope, including but not limited to:
- adaptive computing
- applicable neural networks theory
- applied statistics
- artificial intelligence
- 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