Abbreviation: ICCDA 2025

November 7-9, 2025 - Chengdu, China

More details, please visit: https://www.iccda.org/

 

International Conference on Computing and Data Analysis (ICCDA) is an annual conference held 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.

 

ICCDA 2025 will be located in Chengdu, China during November 7-9, 2025. It is hosted by Sichuan Tourism University, China and Chengdu University of Technology, China, and co-sponsored by Wenzhou University of Technology, China, Wenzhou University, China, Wenzhou Business College, China, and Concordia University, Canada, supported by Wenzhou Computer Federation, Wenzhou Software Industry Association. The previous editions of ICCDA were held in Wenzhou, China; Guizhou, China; Shanghai, China (Virtual); Sanya, China (Virtual); Silicon Valley, USA; University of Hawaii Maui College, Kahului; Northern Illinois University (NIU) DeKalb Florida Polytechnic University, Lakeland.

 

*Conference Awards:

Outstanding Organization Award

Outstanding Contribution Award

Best Paper Award

Best Oral Presentation Award

Best Poster Presentation Award

 

*Proceedings:

Full Paper submitted and accepted after successful registration will be published by ICCDA Conference Proceedings, which will be indexed by Scopus & Ei Compendex.

 

*Previous ICCDA:

Past ICCDA papers were all published in the prestigious ACM proceedings:

ICCDA 2024, ISBN: 979-8-4007-1044-5, index soon

ICCDA 2023, ISBN: 979-8-4007-0057-6 EI, Scopus indexed

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/iccda2025

 

*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. Danica Cong

Tel.: +86 139 8006 2041  

Email: iccda_info@163.com

Wechat: iconf-cs-2