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<br>The inaugural INNS Big Data conference 2015
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<br>August 8-10, 2015, San Francisco, USA
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<br>CALL FOR PAPERS
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<br>Homepage: http://www.innsbigdata.org
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<br>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.
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<br>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 th...
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<br>
<br>The inaugural INNS Big Data conference 2015
<br>
<br>August 8-10, 2015, San Francisco, USA
<br>
<br>CALL FOR PAPERS
<br>
<br>###########################################
<br>
<br>Homepage: http://www.innsbigdata.org
<br>
<br>###########################################
<br>
<br>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.
<br>
<br>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.
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<br>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),
<br>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).
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<br>Important Dates
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<br>Special Session Proposals January 22, 2015
<br>Tutorials and Workshops Proposals January 22, 2015
<br>Paper Submission March 22, 2015
<br>Paper Decision Notification May 22, 2015
<br>Camera Ready Submission of papers June 8, 2015
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<br>Co-Sponsors
<br>* International Neural Network Society (INNS)
<br>* Elsevier
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<br>Advisory Board
<br>* Walter Freeman, University of California, Berkeley, USA
<br>* Ali Minai, University of Cincinnati, USA
<br>* Danil Prokhorov, Toyota Tech Center
<br>* Bernard Widrow, Stanford University, USA
<br>* Bart Kosko, University of Southern California, USA
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<br>General Chairs
<br>* Plamen Angelov, Lancaster University, UK
<br>* Asim Roy, Arizona State University, Tempe, USA
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<br>PC Chairs
<br>* Adel M. Alimi, University of Sfax, Sfax, Tunisia
<br>* Theodore Trafalis, University of Oklahoma, USA
<br>* Kumar Venayagamoorthy, Clemson University, USA
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<br>Plenary Chairs
<br>* Nikola Kasabov, Auckland University of Technology, New Zealand
<br>* Irwin King, Chinese University of Hongkong, China
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<br>Special Sessions Chairs
<br>* Bonny Banerjee, University of Memphis, USA
<br>* Alessandro Ghio, University of Genoa, Italy
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<br>Tutorials/Workshops Chair
<br>* Marley Vellasco, PUC-Rio, Rio de Janeiro, Brazil
<br>* Trevor Martin, Univ. of Bristol, UK.
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<br>Poster Session Chairs
<br>* Yi Lu Murphy, University of Michigan-Dearborn, USA
<br>* Bernardete Ribeiro, University of Coimbra, Portugal
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<br>Sponsors/Exhibit Chairs
<br>* James Dankert, BAE Systems, USA
<br>* Rosemary Paradis, Lockheed Martin, USA
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<br>Publication Chairs
<br>* Danilo Mandic, Imperial College, London, UK
<br>* John Weng, Michigan State University, East Lansing, USA
<br>* Mariette Awad, American University of Beirut, Lebanon
<br>* Amir Hussain, University of Stirling, Scotland, UK
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<br>Paper Submission and Publication
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<br>* Prospective authors should submit a full-length draft manuscript (8 pages), including figures, tables and references using the Elsevier Standard template without page numbers.
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<br>* The full papers should be submitted to the INNS-BigData’2015 EasyChair online submission website: https://easychair.org/conferences/?conf=innsbigdata2015
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<br>* Conference proceedings will be submitted for inclusion into Elsevier as well as other Abstracting and Indexing (A&I) databases.
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<br>Topics and Areas include, but not limited to:
<br>* Autonomous, online, incremental learning – theory, algorithms and applications in big data
<br>* High dimensional data, feature selection, feature transformation – theory, algorithms and applications for big data
<br>* Scalable algorithms for big data
<br>* Learning algorithms for high-velocity streaming data
<br>* Kernel methods and statistical learning theory
<br>* Big data streams analytics
<br>* Deep neural network learning
<br>* Machine vision and big data
<br>* Brain-machine interfaces and big data
<br>* Cognitive modeling and big data
<br>* Embodied robotics and big data
<br>* Fuzzy systems and big data
<br>* Evolutionary systems and big data
<br>* Evolving systems for big data analytics
<br>* Neuromorphic hardware for scalable machine learning
<br>* Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.)
<br>* Big data and collective intelligence/collaborative learning
<br>* Big data and hybrid systems
<br>* Big data and self-aware systems
<br>* Big Data and infrastructure
<br>* Big data analytics and healthcare/medical applications
<br>* Big data analytics and energy systems/smart grids
<br>* Big data analytics and transportation systems
<br>* Big data analytics in large sensor networks
<br>* Big data and machine learning in computational biology, bioinformatics
<br>* Recommendation systems/collaborative filtering for big data
<br>* Big data visualization
<br>* Online multimedia/ stream/ text analytics
<br>* Link and graph mining
<br>* Big data and cloud computing, large scale stream processing on the cloud
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<br>Big Data Analytics Section @ INNS
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<br>Considering the growing interest to process and analyse big data, the International Neural Network Society (INNS) has a new Section on Big Data Analytics (BDA) to help the neural network field position itself as a leading technology contributor to big data analytics. By actively promoting the use of neural networks for big data analytics, the INNS-BDA section is dedicated towards bringing the neural network field to greater heights. Anyone who is interested to know more is encouraged to visit the homepage of the INNS-BDA Section at http://www.inns.org/big-data-section.
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