ICCDA 2024 - 8th International Conference on Computing and Data Analysis (ICCDA 2024)

Full Name: 2024 The 8th International Conference on Computing and Data Analysis (ICCDA 2024)
Abbreviation: ICCDA 2024
October 25-27, 2024 - Wenzhou, 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 ICCDA was held in Florida Polytechnic University, Lakeland, USA (2017), Northern Illinois University (NIU) DeKalb, USA (2018), University of Hawaii Maui College, Kahului, USA (2019), Silicon Valley, USA (2020), Sanya, China (Virtual, 2021), Shanghai, China (Virtual, 2022), and Guiyang, China (2023). ICCDA 2024 conference will be located in Wenzhou, China during October 25-27, 2024.
Conference Proceedings
Full Paper submitted and accepted after successful registration will be published by Conference Proceedings.
SCI/EI Journal：
Journal of Information Science and Engineering
Indexed In: indexed within EI Compendex, Scopus, SCIE (Web of Science), etc
Impact Factor: 1.142
Special Issue:"Special Issue on Advanced Networking and Communication Solutions for Wireless Mobile Networks"
*Previous ICCDA
Past ICCDA papers were all published in the prestigious ACM proceedings:
ICCDA 2023, ISBN: 979-8-4007-0057-6, EI, Scopus indexing
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/iccda2024
*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 X. Xu
Tel.: +86 180 8007 5398
E-mail: iccda_info-AT-163.com
WeChat: iconf-cs-1