Conference Information

ICCIDS 2019: International Conference on Computational Intelligence in Data Science

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Submission Date:
2018-11-19
Notification Date:
2018-12-24
Conference Date:
2019-02-21
Location:
Chennai, India
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Call For Papers

ICCIDS 2019 (International Conference on Computational Intelligence in Data Science) is an academic conference held in Chennai, India on 2019-02-21. The paper submission deadline is 2018-11-19. Acceptance notifications are sent on 2018-12-24.

About 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 We invite high-quality submissions describing original and unpublished work for the conference. 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 Data Science. The papers need not be based only on this theme. Topics of interest for the conference include, but are not restricted to: Topics Classification and Regression Semi-supervised and Unsupervised Learning Parallel and Distributed Learning Matrix and Tensor Methods Graph Mining Deep Learning Reinforcement Learning Dimensionality Reduction Bayesian Methods Graphical Models Time Series Analysis Optimization Text analytics, NLP Information Retrieval, Social Network Analysis Recommender Systems, Online Advertising Bioinformatics, Computational Neuroscience, Systems Biology Multimedia Processing, Crowdsourcing Education, Technology for Emerging Markets Robotics
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