[INNS-BigData 2016] Call for Papers

[INNS-BigData 2016 - Greece]

[Apologies for cross-postings]

###########################################

The INNS Big Data conference 2015

October 23-25, 2016, Thessaloniki, Greece

CALL FOR PAPERS

###########################################

Homepage: http://www.innsbigdata.org

###########################################

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).

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.

###########################################################

Important Dates

###########################################################

Special Session Proposals February 15, 2016

Tutorials and Workshops Proposals February 15, 2016

Paper Submission March 21, 2016

Paper Decision Notification May 16, 2016

Camera Ready Submission of papers June 13, 2016

###########################################################

Co-Sponsors

* International Neural Network Society (INNS)

* Springer

Plenary Speakers

* Francesco Bonchi, Yahoo Research Labs and ISI, Italy

* Piotr Mirowski, Google DeepMind, London, UK

* Hany Choueri, The Chief Data Scientist, Bank of England, UK (tbc)

Advisory Board

* Walter Freeman, University of California, Berkeley, USA

* Ali Minai, University of Cincinnati, USA

* Danil Prokhorov, Toyota Tech Center 

* Theodore Trafalis, University of Oklahoma, USA

* Kumar Venayagamoorthy, Clemson University, USA

* Bernard Widrow, Stanford University, USA

General Chairs

* Plamen Angelov, Lancaster University, UK

* Yannis Manolopoulos, Aristotle University, Greece

PC Chairs

* Lazaros Iliadias, Democritus University, Greece

* Asim Roy, Arizona State University, Tempe, USA

* Marley Vellasco, PUC-Rio, Rio de Janeiro, Brazil

Special Sessions Chairs

* Alessandro Ghio, University of Genoa, Italy

* Irwin King, Chinese University of Hong Kong, China

Tutorials/Workshops Chair

* Nikola Kasabov, Auckland Universitty of Technology, New Zealand

* Bernardete Ribeiro, University of Coimbra, Portugal

Poster Session Chairs

* Yi Lu Murphy, University of Michigan-Dearborn, USA

* Liang Zhao, University of Sao Paulo, Brazil

Awards Chair

* Araceli Sanchis de Miguel, Carlos III University, Spain

Competitions Chair

Adel Alimi, University of Sfax, Tunisia

Panel Chair

* Leonid Perlovsky, Harvard University, Boston, USA

Sponsors/Exhibit Chairs

* James Dankert, BAE Systems, USA

* Rosemary Paradis, Lockheed Martin, USA

Publication Chairs

* Danilo Mandic, Imperial College, London, UK

* Mariette Awad, American University of Beirut, Lebanon

International Liaison 

* De-Shuang Huang, Tongji University, Shanghai, China

* Petia Georgieva, University of Aveiro, Portugal

Publicity Chairs,

* Teng Teck Hou, Singapore Management University, Singapore

* Simone Scardapane, The Sapienza University of Rome, Italy

* Jose Antonio Iglesias Martinez, Carlos III University, Spain

Paper Submission and Publication

###########################################################

* Original works submitted as a regular paper limited to a maximum of 10 pages in Springer format will be published in the proceedings to be available electronically as a Springer book to download for delegates. 

* It will 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 to the conference should register with a regular fee to present their work at the conference.

###########################################################

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

for big data

* 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