Introduction

The conference provides an opportunity for the researchers, engineers, developers and practitioners from academia and industry to discuss and address the solicit experimental, theoretical work and methods in solving problems and to share their experience, exchange and cross-fertilize their ideas in the fields of Data Science and Computational Intelligence. This conference will be useful for professionals working in data science, big data, machine learning, artificial intelligence and predictive modeling.

We invite high-quality submissions describing original and unpublished work for the conference.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).

Call for paper

Important date

2017-02-01
Abstract submission deadline
2017-04-10
Draft paper submission deadline
2017-05-11
Draft paper acceptance notification
2017-05-20
Final paper submission deadline

Submission 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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Important Date

  • Conference Date

    Jun 02

    2017

    to

    Jun 03

    2017

  • Feb 01 2017

    Abstract Submission Deadline

  • Apr 10 2017

    Draft paper submission deadline

  • May 11 2017

    Draft Paper Acceptance Notification

  • May 20 2017

    Final Paper Deadline

  • Jun 03 2017

    Registration deadline

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