![]() |
| |||||||||||
ICCDA 2023 : ACM--2023 The 7th International Conference on Computing and Data Analysis (ICCDA 2023) | |||||||||||
Link: http://iccda.org/ | |||||||||||
| |||||||||||
Call For Papers | |||||||||||
Full name: 2023 The 7th International Conference on Computing and Data Analysis
Abbreviation: ICCDA 2023 September 15-17, 2023 - Guiyang, China More details, please visit: http://iccda.org/ The International Conference on Computing 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 computing 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, Sanya, China (Virtual); Shanghai, China (Virtual). ICCDA 2023 conference will be located in Guiyang, China during September 15-17, 2023. *Proceedings Full Paper submitted and accepted after successful registration will be published by ACM Conference Proceedings (ISBN: 979-8-4007-0057-6) , the content will be submitted to Ei compendex, Scopus, etc. *Previous ICCDA Past ICCDA papers were all published in the prestigious ACM proceedings: ICCDA 2022, ISBN: 978-1-4503-9547-2, EI, Scopus indexed ICCDA 2021, ISBN: 978-1-4503-8911-2, EI, Scopus indexed ICCDA 2020, ISBN: 978-1-4503-7644-0, EI, Scopus indexed 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 https://www.zmeeting.org/submission/iccda2023 *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 *Venue Guizhou University Address: South Jiaxiu Road, Huaxi District, Guiyang, Guizhou, China *Contact Ms. Maggie X. Xu Tel.: +86 180 8007 5398 Tmail: iccda_info@163.com Wechat: iconf-cs-1 |
|