IEEE Symposium on Computational Intelligence in Big Data (IEEE CIBD)
Building on the success of last year's meeting, the IEEE Symposium Series on Computational Intelligence (SSCI) 2019 will host the Computational Intelligence in Big Data (CIBD) 2019. The event will bring together international experts to discuss theories and applications of big data in computer science. Sponsored by the IEEE Computational Intelligence Society, the symposium will host academics, researchers, professionals, industrial representatives, students and practitioners. Registration to SSCI 2019 will allow participants to attend the CIBD meeting, other sessions, and coffee breaks, lunches and conference banquet.
The manuscripts should be submitted in PDF format. Click Here to know further guidelines for submission.
The IEEE CIBD 2019 will bring together international scientists, researchers and professionals to present and discuss the current challenges and opportunities in big data related to computational intelligence (CI). The organisers welcome presentation of recent results relating to CI algorithms, software, systems and architecture, data analytics, current challenges, and new and emerging applications. Presentations relating to industry, novel applications and emerging CI areas in BG are strongly encouraged.
Specific topics include, but are not limited to:
- Novel CI methods of big data acquisition
- CI in distributed computing of big data
- Memory efficient CI algorithms relating to reading, processing or analysing big data
- Data mining in big data
- Deep learning in big data
- Integration of big data, such as multi-modal, multi-fidelity, structured and unstructured data
- Big data in industry
- Big data in healthcare
- Big data and the internet of things
- Big data in the future of media and social media
- Big data in finances and economy
- Big data in public services
- Big data in intelligent robotics
- Big data driven business or industry
- Extracting understanding from distributed, diverse and large-scale data resources
- Real time analysis of large data streams
- Predictive analysis and in-memory analytics
- Dimensionality reduction and analysis of large and complex data
- New information infrastructures
- Visualisation of big data and visual data analytics
- Semantics technologies for big data
- Scalable learning in big data
- Optimisation of big data in complex systems
- Data governance and management
- CI in curation of big data
- Human-computer interaction and collaboration in big data
- Big data and cloud computing
- Applications of big data, such as industrial process, business intelligence, healthcare, bioinformatics and security.