2026 7th International Conference on Computer Information and Big Data Applications (CIBDA 2026)

2026 7th International Conference on Computer Information and Big Data Applications (CIBDA 2026) will be held on March 20-22, 2026 in Wuhan, China. 

 

The topics of interest for submission include, but are not limited to:

· Artificial intelligence 

· Machine Learning 

· Computer network 

· Wireless Sensor Network 

· Automation Software Engineering 

· Bioinformatics and Scientific Computing 

· Computer Aided Design 

· Computer Animation 

· Computer Modeling 

· Computer Security 

· Computer Simulation 

· Mobile Computing

· Information system planning and management 

· System analysis and design method 

· Distributed information system 

· Accounting and financial information system Internet technology 

· Information quality and strategy 

· Decision Support Systems 

· Information Technology Management Knowledge management 

· Enterprise Information System

· Data analysis / Data flow application 

· Data science models and methods 

· Algorithms for big data 

· Big data and deep learning 

· Big data and high-performance computing 

· Big data network infrastructure 

· Multi-core computing and accelerator 

· Big data applications for the Internet of Things 

· Big data mobile applications 

· Big data applications in smart cities 

· Fault tolerance and reliability

· Scalability of big data systems

All papers, both invited and contributed, will be reviewed by two or three experts from the committees. After a careful reviewing process, all accepted papers of CIBDA 2026 will be published in ACM International Conference Proceedings Series (ISBN: 979-8-4007-2257-8),which will be archived in the ACM Digital Library, and indexed by EI Compendex, Scopus. 

 

Full Paper Submission Date: January 15, 2026

Registration Date: March 6, 2026

Final Paper Submission Date: March 13, 2026

Conference Dates: March 20-22, 2026

 

Please send the full paper(word+pdf) to Submission System: 
https://ais.cn/u/UV7JNn