2024 The 8th International Conference on Computing and Data Analysis
2024年第八届计算与数据分析国际会议诚邀您的加入！
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 2024 will be located in Wenzhou, China during November 15-17, 2024. It is hosted by Wenzhou University of Technology, China and Sichuan Tourism University, China, and co-sponsored by 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 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.
We believe the final program will be the result of a highly selective review process designed to include the best work of its kind in every category. You are cordially invited to submit your recent research work to the ICCDA 2024.
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Conference Proceedings
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Full Paper submitted and accepted after successful registration will
be published by ACM Conference Proceedings (ISBN: 979-8-4007-1041-4
), which will be indexed by Scopus & Ei Compendex. |
SUBMISSION INSTRUCTIONS
The manuscript in one column paper format, up to 10 pages. Manuscripts must be written in English and follow the instructions at Word Template.
To ensure the high quality of the accepted papers, all submissions will be peer-reviewed. Please only submit original material where copyright of all parts is owned by the authors declared and which is not currently under review elsewhere.
Online Submission System: https://www.zmeeting.org/submission/iccda2024.
Call for Papers
TOPICS INTERESTED BUT NOT LIMITED
- 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
- 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
- Data warehouses, cloud architectures
- Large-scale databases
- Information and knowledge retrieval, and semantic search
- Web/social/databases query and search
- Personalized search and recommendation
- Human-machine interaction and interfaces
- Crowdsourcing and collective intelligence
- Learning for streaming data
- Learning for structured and relational data
- Latent semantics and insight learning
- Mining multi-source and mixed-source information
- Mixed-type and structure data analytics
- Cross-media data analytics
- Big data visualization, modeling and analytics
- Multimedia/stream/text/visual analytics
- Relation, coupling, link and graph mining
- Personalization analytics and learning
- Web/online/social/network mining and learning
- Structure/group/community/network mining
- Cloud computing and service data analysis
