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ACM--ICCDA--Ei, Scopus 2021 : ACM--2021 The 5th International Conference on Compute and Data Analysis (ICCDA 2021)--Ei compendex, scopus ICCDA 2021


When Feb 2, 2021 - Feb 4, 2021
Where Sanya, China
Submission Deadline Dec 20, 2020
Categories    computer science   data mining   software engineering   high performance computing

Call For Papers

Full name: The 5th International Conference on Compute and Data Analysis
Abbreviation: ICCDA 2021
Date: Feb. 2-4, 2021
Location: Sanya, China

The International Conference on Compute and Data Analysis (ICCDA), is an annual conference hold each year. It is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice of compute and data analysis.
The previous editions of ICCDA were held in Florida Polytechnic University, Lakeland, Northern Illinois University (NIU) DeKalb, University of Hawaii Maui College, Kahului, Silicon Valley, USA. ICCDA 2021 conference will be located in Sanya, China during February 2-4, 2021.

Accepted and presented papers will be published into the ACM Proceedings (ISBN: 978-1-4503-8911-2), indexed by Ei compendex, scopus, etc.

Harman Resort Hotel Sanya (pending)
136 Yuya Road, Jiyang District, 572000 Sanya, China

*Keynote Speakers
Lili Qiu, The University of Texas at Austin, USA (ACM Fellow, IEEE Fellow, and ACM Distinguished Scientist)
Hai Jin, Huazhong University of Science and Technology, China (IEEE Fellow, CCF Fellow)
Zhiguo Gong, The University of Macau

*Invited Speakers
Yucong Duan, Hainan University, China
Lei Li, Hefei University of Technology, China

*Previous ICCDA
Past ICCDA papers were all published in the prestigious ACM proceedings:
ICCDA 2020, ISBN: 978-1-4503-7644-0, EI, Scopus indexing
ICCDA 2019, ISBN: 978-1-4503-6634-2, EI, Scopus indexed
ICCDA 2018, ISBN: 978-1-4503-6359-4, EI, Scopus indexed
ICCDA 2017, ISBN: 978-1-4503-5241-3, EI, Scopus indexed

*Submission Link

Mathematical, probabilistic and statistical models and theories
Machine learning theories, models and systems
Knowledge discovery theories, models and systems
Manifold and metric learning
Deep learning
Scalable analysis and learning
Non-iidness learning
Heterogeneous data/information integration
Data pre-processing, sampling and reduction
Dimensionality reduction
Feature selection, transformation and construction
Large scale optimization
High performance computing for data analytics
Architecture, management and process for data science
More topics:

Ms. Maggie Lau
Wechat: iconf-cs

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