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    INNS 2016 - INNS Conference on Big Data - The second INNS Conference on Big Data 2016

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    Website https://conferences.cwa.gr/inns-big-data2016/ | Want to Edit it Edit Freely

    Category Big data;Analytics;Neural Network;Learning Technol

    Deadline: April 30, 2016 | Date: October 23, 2016-October 25, 2016

    Venue/Country: Grand Hotel Palace, ThessalonikI, Greece

    Updated: 2016-04-26 20:16:41 (GMT+9)

    Call For Papers - CFP

    CALL FOR PAPERS

    Big data is not just about storage of and access to data. Analytics play a big role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms. Most machine learning algorithms can’t easily scale up to big data. Plus there are challenges of high-dimensionality, velocity and variety.

    The neural network field has historically focused on algorithms that learn in an online, incremental mode without requiring in-memory access to huge amounts of data. This type of learning is not only ideal for streaming data (as in the Industrial Internet or the Internet of Things), but could also be used on stored big data. Neural network technologies thus can become significant components of big data analytics platforms and this inaugural INNS Conference on Big Data will begin that collaborative adventure with big data and other learning technologies.

    Thus the aim of this conference is to promote new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of Big Data Analytics to solve real-world problems (e.g. weather prediction, transportation, energy management).

    Topics

    Topics and Areas include, but not limited to:

    Autonomous, online, incremental learning & theory, algorithms and applications in big data

    High dimensional data, feature selection, feature transformation – theory, algorithms and applications

    Scalable algorithms for big data

    Learning algorithms for high-velocity streaming data

    Big data streams analytics

    Deep neural network learning

    Machine vision and big data

    Brain-machine interfaces and big data

    Cognitive modeling and big data

    Embodied robotics and big data

    Fuzzy systems and big data

    Evolutionary systems and big data

    Evolving systems for big data analytics

    Neuromorphic hardware for scalable machine learning

    Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.)

    Big data and collective intelligence/collaborative learning

    Big data and hybrid systems

    Big data and self-aware systems

    Big Data and infrastructure

    Big data analytics and healthcare/medical applications

    Big data analytics and energy systems/smart grids

    Big data analytics and transportation systems

    Big data analytics in large sensor networks

    Big data and machine learning in computational biology, bioinformatics

    Recommendation systems/collaborative filtering for big data

    Big data visualization

    Online multimedia/ stream/ text analytics

    Link and graph mining

    Big data and cloud computing, large scale stream processing on the cloud

    Short Instructions for Authors

    Accepted papers will be published by Springer in the ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING Series (AISC). Authors should submit original papers limited to a maximum of 10 formatted by using the Springer style file. Works be peer-reviewed by at least three PC members on the basis of technical quality, relevance, originality, significance and clarity. At least one author of an accepted submission of the conference must register with a regular fee and present their work at the conference.

    Authors can choose to work with word or latex; however, they are advised to use latex. Here are the style files for word or latex. Further instruction can be found at http://www.springer.com/gp/authors-editors/book-authors-editors/manuscript-preparation/5636

    Awards

    Best papers will be selected and awarded as follows:

    Best regular paper

    Best student paper

    This will be based on a combination of reviewers’ comments, presentations and importance and quality judged by a panel.

    Best paper awards (500 Euros) are donated by the sponsor Springer Verlag, Germany and will be commemorated by a certificate.

    CALL FOR TUTORIALS

    We solicit proposals for tutorials for presentation at the INNS BigData Conference. Tutorial proposals must provide an in-depth survey of the area with the option of describing some particular pieces in more detail. A meaningful summary of open issues in the area is a definite plus. While depth in research is important, the topic should be sufficiently broad to attract a significant portion of the audience. Tutorial proposals must be at most 5 pages, in the LNCS style file and must include enough details to provide a sense of both the scope of material to be covered and the depth to which it will be covered. Proposals should also indicate the tutorial length, typically, 1.5 or 3 hours.

    Proposals should also identify any other venues in which all or part of the tutorial has been or will be presented, and explain how the current proposal differs from those other editions of the tutorial. Tutorial proposals must clearly identify the intended audience and any prerequisite knowledge for attendees. Also, proposals should include a short biography of the presenter(s).

    Important Dates

    Submission of proposals for tutorials: April 15th, 2016

    Notification of acceptance: April 30, 2016

    Tutorial Co-Chairs: Apostolos N. Papadopoulos, Bernardete Ribeiro

    Please submit your proposals (in PDF) via e-mail to papadopoatcsd.auth.gr


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
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