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

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