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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 | |||||||||||
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Full name: The 5th International Conference on Compute and Data Analysis
Abbreviation: ICCDA 2021 Website: http://iccda.org/ 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. *Proceedings Accepted and presented papers will be published into the ACM Proceedings (ISBN: 978-1-4503-8911-2), indexed by Ei compendex, scopus, etc. *Venue 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 http://www.easychair.org/conferences/?conf=iccda2021 *Topics 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: http://iccda.org/cfp.html *Contact Ms. Maggie Lau iccda_info@163.com Wechat: iconf-cs |
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