Connectionists: [CYBCONF 2017 - SS] Call for Papers to Special Session on Deep Learning for Prediction and Estimation

[Teng Teck Hou]

[Apologies for cross-postings]

 

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CALL FOR PAPERS

 

3rd IEEE International Conference on Cybernetics

(CYBCONF-2017)

 

Special Session on Deep Learning for Prediction and Estimation (DLPE)

 

June 21- June 23, 2017,  Exeter, United Kingdom

 

http://cse.stfx.ca/~CybConf2017/

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CYBCONF-2017 is organized by University of Exeter, sponsored byIEEE Systems,
Man, and Cybernetics Society (SMC), and supported by IEEE SMC Technical
Committees on Cybernetics for Cyber-enabled Worlds; Awareness Computing;
Intelligent Industrial Systems; and Distributed Intelligent Systems.
CYBCONF-2017 will be hosted in Exeter, the capital city of Devon and
provides the county with a central base for education, medicine, religion,
commerce and culture. The city is also home to the magnificent Exeter
Cathedral, which dates back to Norman times. Exeter is also ideally placed
to base a trip to branch out visiting places such as the famous Dartmoor
National Park and the unspoilt beaches of the North and South Devon
coastlines.

 

The area of deep learning has been receiving immense attention from
researchers and practitioners across the globe. Deep learning techniques
have achieved excellent results in pattern recognition with images and
speech data. In fact deep learning is the state-of-the-art in the domain of
computer vision, speech recognition and natural language processing.
However, deep learning techniques are yet to be explored extensively for the
task of prediction and estimation. Due to the advancement in sensor
technology, many sectors such as energy, environment, and more recently IoT
require processing of huge amount of sensor data to develop predictive
models. Deep learning, due to its capability of modeling highly non-linear
functions and use of very efficient learning algorithms in terms of time and
computational unit requirements, seems to be very promising in this field.
One of the most attractive properties of deep learning over other machine
learning methods is its automatic feature extraction ability. This ability
overcomes risk of inefficient and time consuming hand crafted feature
extraction that requires lot of hard work and expert knowledge.

 

Through this special session we would like to invite researchers,
academicians, and students for dissemination of their research work in the
direction of prediction and estimation through deep learning techniques.

 

##############################Important Dates##############################

* Paper Submission
February 23, 2017

* Paper Decision Notification
April 22, 2017

* Camera-Ready Submission
May 15, 2017

* Authors registration
May 15, 2017

* Conference
June 21 - June 23, 2017

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############Paper Submission and Publication############

* Authors are invited to submit original previously unpublished research
papers written in English, of up to 8 pages (or 10 pages with over length
charge) including figures and references using IEEE Computer Society
Proceedings Manuscripts style (two columns, single-spaced, 10 fonts). Please
find the manuscript templates and submission related information at the
CYBCONF 2017 conference webpage. All accepted papers must be presented by
one of the authors who must register for the conference and pay the fee.

 

* Presented papers will appear in the conference proceedings, available on
IEEE Xplore and submitted to be indexed in CPCI (ISI conferences and part of
Web of Science) and Engineering Index (EI). The authors of selected best
papers will be invited post conference to extend their contributions for
special issues of prestigious journals, such as IEEE Transactions on
Cybernetics, IEEE SMC Magazine, and Evolving Systems.

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##################Topics and Areas of Interest##################

Topics of interests include but are not limited to:

 

* Theoretical / experimental results on deep learning models and
architectures

 

* Unsupervised, semi supervised, and supervised deep learning

 

* Software/hardware platforms for deep learning, parallelization issues in
deep learning

 

* Applications of deep learning in IoT, energy, environment, medical or any
other domain

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##########################Organizing Committee##########################

* Plamen Angelov, Chain in Intelligent Systems at Lancaster University,
United Kingdom

                

* Rashmi Dutta Baruah, Assistant Professor at IIT Guwahati, India

                

* Teck-Hou Teng, Research Fellow at SMU, Singapore

                

* Ana Kosarsva, Data Scientist at Technische Universitat Berlin, Germany

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