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ICCIDS 2017 : IEEE International Conference on Computational Intelligence in Data Science | |||||||||||||||
Link: http://www.iccids.in | |||||||||||||||
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Call For Papers | |||||||||||||||
We invite high-quality submissions describing original and unpublished work for the conference. Accepted submissions will be included in the IEEE Xplore Digital Library.
All submissions will be subject to plagiarism check. Papers submitted for consideration should not have been published elsewhere and should not be under review or submitted for review elsewhere during the duration of consideration. At least one author of an accepted paper must register for the conference and present the paper in the conference. Only the presented papers will be included in the proceedings. Papers that do not make the grade for publication, yet show promise, may be selected for poster presentation instead. If you are specifically interested in submitting a poster, then please ensure that the paper does not exceed 4 pages (including figures, references and appendices). Topics of Interest The theme of this conference is use of Computational Intelligence in Big Data. The papers need not be based only on this theme. Topics of interest for the conference include, but are not restricted to: Topics Foundations Probabilistic and statistical models and theories Machine Learning algorithms for high-velocity streaming data Scalable analysis and learning Data pre-processing, sampling and reduction High dimensional data, feature selection and feature transformation High performance computing for data analytics Architecture, management and process for data science Data analytics, machine learning and knowledge discovery Knowledge discovery theories, models and systems Learning for streaming data Intent and insight learning Cross-media data analytics Big data visualization, modeling and analytics Multimedia/stream/text/visual analytics Computational Intelligence and Big Data Analytics Computational theories for big data analysis Incremental learning – theory, algorithms and applications in big data Sparse data, feature selection, feature transformation – theory, algorithms and applications for big data Associative memories Probabilistic and information-theoretic methods Supervised, unsupervised and reinforcement learning Support vector machines and kernel methods Time series analysis Algorithms and libraries - Optimization for Big Data Analytics |
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