Thursday, 11 August 2011

CFP: 2012 International Joint Conference on Neural Networks

2012 International Joint Conference on Neural Networks

The annual International Joint Conference on Neural Networks (IJCNN) will be held jointly with the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and the IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI), June 10-15, 2012, Brisbane Convention & Exhibition Centre, Brisbane, Australia. Cross-fertilization of the three technical disciplines and newly emerging technologies is strongly encouraged.

Call for Contributed Papers

IJCNN 2012 will feature a world-class conference that aims to bring together researchers and practitioners in the field of neural networks and computational intelligence from all around the globe. Technical exchanges within the research community will encompass keynote lectures, special sessions, tutorials and workshops, panel discussions as well as poster presentations. In addition, participants will be treated to a series of social functions, receptions, and networking to establish new connections and foster everlasting friendship among fellow counterparts. The annual IJCNN covers all topics in neural networks including:

Neural network theory & models
Computational neuroscience
Learning and adaptation
Pattern recognition
Cognitive models
Machine vision and image processing
Brain-machine interfaces
Collective intelligence
Neural control
Hybrid systems
Evolutionary neural systems
Self-aware systems
Neurodynamics and complex systems
Data mining
Neuroinformatics
Sensor networks and intelligent systems
Neural hardware
Applications
Neural network applications
Computational biology
Neuroengineering
Bioinformatics
Emerging areas

Paper Submission and Inquiries
Prospective authors are invited to contribute high-quality papers to IJCNN 2012. All papers are to be submitted electronically through the IEEE WCCI 2012 website http://www.ieee-wcci2012.org/.

For IJCNN inquiries please contact Conference Chair: Cesare Alippi at cesare.alippi@polimi.it

For Program inquiries please contact Program Chair: Kate Smith-Miles at kate.smith-miles@sci.monash.edu.au

General Enquires for IEEE WCCI 2012 should be sent to the General Chair: Hussein Abbass at h.abbass@adfa.edu.au

Call for Special Sessions

The IJCNN 2012 Program Committee solicits proposals for special sessions within the technical scopes of the Congress. Special sessions, to be organized by international recognized experts, aim to bring together researchers in special focused topics. Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the contributed papers. Proposals should include the session title, a brief description of the scope and motivation, biographic and contact information of the organizers. Researchers interested in organizing special sessions are invited to submit formal proposal to the Special Session Chair: Brijesh Verma at b.verma@cqu.edu.au

Call for Tutorials and Workshops

IJCNN 2012 will also feature pre-Congress tutorials and workshops, covering fundamental and advanced neural network topics. A tutorial or workshop proposal should include title, outline, expected enrollment, and presenter/organizer biography. We invite you to submit proposals to the Tutorial and Workshop Chair: Toshio Fukuda at fukuda@mein.nagoya-u.ac.jp.

Call for Competitions

IJCNN 2012 will host competitions to stimulate research in neural networks, promote fair evaluations, and attract students. The proposals for new competitions should include descriptions of the problems addressed, motivations, expected impact on neural networks and machine learning, and established baselines, schedules, anticipated number of participants, and a biography of the main team members. We invite you to submit proposals to the Competitions Chair: Sung-Bae Cho at sbcho@yonsei.ac.kr

Important Dates

Competition proposals submission deadline: October 17, 2011
Special sessions proposal submission deadline: November 21, 2011
Special session decision notification: November 28, 2011
Paper submission deadline: December 19, 2011
Tutorial and Workshop proposal submission deadline: January 16, 2012
Tutorial and Workshop decision notification: January 23, 2012
Paper acceptance notification date: February 20, 2012
Final paper submission deadline: April 2, 2012
Early registration: April 2, 2012
Conference dates: June 10-15, 2012

Wednesday, 10 August 2011

CFP: 2012 IEEE International Conference on Fuzzy Systems

2012 IEEE International Conference on Fuzzy Systems

The annual IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) will be held jointly with the International Joint Conference on Neural Networks (IJCNN) and the IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI), June 10-15, 2012, Brisbane Convention and Exhibition Centre, Brisbane, Australia. Cross-fertilization of the three technical disciplines and newly emerging technologies is strongly encouraged.

Call for Contributed Papers

FUZZ-IEEE 2012 will feature a world-class conference that aims to bring together researchers and practitioners in the field of fuzzy systems and computational intelligence from all around the globe. Technical exchanges within the research community will encompass keynote lectures, special sessions, tutorials and workshops, panel discussions as well as poster presentations. In addition, participants will be treated to a series of social functions, receptions, and networking to establish new connections and foster everlasting friendship among fellow counterparts. The annual FUZZ-IEEE covers all topics in fuzzy systems including:

Fuzzy logic and fuzzy set theory
Fuzzy document retrieval systems, text mining
Lattice theory and multi-valued logics
Fuzzy systems for natural language processing
Approximate reasoning
Fuzzy information processing
Type-2 fuzzy logic
Fuzzy and rough data analysis, fuzzy statistics
Rough sets and random sets
Fuzzy data mining and forecasting
Fuzzy mathematics
Fuzzy systems modeling and identification
Possibility theory and imprecise probability
Fuzzy control and intelligent systems
Fuzzy decision making and decision support systems
Fuzzy systems for robotics
Fuzzy optimization and design
Fuzzy pattern recognition
Fuzzy man-machine interfaces, emotional computing
Fuzzy clustering
Fuzzy computing with words, granular computing
Fuzzy systems architectures and hardware
Fuzzy systems for agent technology
Fuzzy web intelligence
Hybrid fuzzy systems (fuzzy-neuro-evolutionary)
Fuzzy sets in bioinformatics
Fuzzy image and multimedia processing
Fuzzy databases, fuzzy data summarization
Medical, financial, industrial applications
Emerging areas

Paper Submission and Inquiries

Prospective authors are invited to contribute high-quality papers to FUZZ-IEEE 2010. All papers are to be submitted electronically through the IEEE WCCI 2012 website http://www.ieee-wcci2012.org/.

For FUZZ-IEEE inquiries, please contact Conference Chair Bernadette Bouchon-Meunier: Bernadette.Bouchon-Meunier@lip6.fr.

For Program inquiries please contact Program Chair James M. Keller: KellerJ@missouri.edu.

General Enquires for IEEE WCCI 2012 should be sent to the General Chair: Hussein Abbass at h.abbass@adfa.edu.au

Call for Special Sessions

The FUZZ-IEEE 2012 Program Committee solicits proposals for special sessions within the technical scopes of the Congress. Special sessions, to be organized by international recognized experts, aim to bring together researchers in special focused topics. Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the contributed papers. Proposals should include the session title, a brief description of the scope and motivation, biographic and contact information of the organizers. Researchers interested in organizing special sessions are invited to submit formal proposal to the Special Session Chair: Laszlo T. Koczy at koczy@tmit.bme.hu.

Call for Tutorials and Workshops

FUZZ-IEEE 2012 will also feature pre-Congress tutorials and workshops, covering fundamental and advanced evolutionary computation topics. A tutorial or workshop proposal should include title, outline, expected enrollment, and presenter/organizer biography. We invite you to submit proposals to the Tutorial and Workshop Chair: Scott Dick at dick@ee.ualberta.ca.

Call for Competitions

FUZZ-IEEE 2012 will host competitions to stimulate research in fuzzy systems, promote fair evaluations, and attract students. The proposals for new competitions should include descriptions of the problems addressed, motivations and expected impact on fuzzy systems, data description, evaluation procedures and established baselines, schedules, anticipated number of participants, and a biography of the main team members. We invite you to submit proposals to the Competitions Chair: Simon Lucas at sml@essex.ac.uk

Important Dates

Competition proposals submission deadline: October 17, 2011
Special sessions proposal submission deadline: November 21, 2011
Special session decision notification: November 28, 2011
Paper submission deadline: December 19, 2011
Tutorial and Workshop proposal submission deadline: January 16, 2012
Tutorial and Workshop decision notification: January 23, 2012
Paper acceptance notification date: February 20, 2012
Final paper submission deadline: April 2, 2012
Early registration: April 2, 2012
Conference dates: June 10-15, 2012

Monday, 4 July 2011

Call for Papers: IEEE Transactions on Smart Grid

CALL FOR PAPERS: IEEE TRANSACTIONS ON SMART GRID
Special Issue on Computational Intelligence Applications in Smart Grids

Computational Intelligence (CI) evolves computational models and tools of intelligence capable of handling large raw numerical sensory data directly, processing them by exploiting the representational parallelism and pipelining the problem, generating reliable and just-in-time responses, with high fault tolerance. Smart grid is basically the embedding of intelligence to enable bi-directional power flows between sources of electric power generation (traditional and renewable sources), and smart devices (traditional loads, energy storage, etc), within some specified constraints and performance requirements. CI deployment is essential for smart grids to be viable. The objective of this special issue is to address and disseminate state-of-the-art research and development in the applications of computational intelligence in smart grids. Authors are invited to submit their original and unpublished contributions to this special issue with emphasis on CI paradigms and their applicat! ! ions in smart grids, including, but not limited to:

- Adaptive dynamic programming
- Artificial immune systems
- Evolutionary computation
- Fuzzy Systems
- Neural Networks
- Reinforcement Learning
- Swarm Intelligence
- CI Algorithms for modeling, control and optimization
- Communication and control
- Cyber security
- Demand response and Demand side management
- Distributed energy resources
- Emission reductions
- Forecasting (loads and sources)
- Markets and economics
- Methods and algorithms for real-time analysis
- Optimization, Placements and Scheduling
- Optimal Power Flow
- Planning, operation and control
- Plug-in vehicles (G2V and V2G)
- Renewable energy (wind and solar)
- Smart micro-grids
- Smart sensing, sense-making and Situational Awareness
- Synchrophasors and state estimation
- Wide area monitoring, control and protection
- Visualizations for control centers

Submission Guidelines
Two page extended abstracts are solicited for the first round of reviews. Authors of selected abstracts will be invited to submit the full papers in the second round. Authors must refer to the IEEE Transactions on Smart Grid author guidelines at http://www.ieee-pes.org/publications/information-for-authors for information on content and formatting of submissions. The direct link to the Manuscript Central for the submission of papers is http://mc.manuscriptcentral.com/tsg-pes. In the Manuscript type drop-down menu box, the author must choose Special Issue on CIASG. For information purposes, please submit a PDF version of the abstracts including a cover letter with authors and contact information via e-mail to gkumar@ieee.org with the subject line "Special Issue on CIASG" by the submission date.

Important Dates
August 31, 2011: Deadline for the submission of extended abstract
Oct. 15, 2011: Completion for first-round of reviews
Dec. 15, 2011: Deadline for full paper submission
June 15, 2012: Final notification of authors

Guest Editorial Board
Guest Editor-in-Chief:
Ganesh Kumar Venayagamoorthy, Missouri University of Science and Technology, USA

Editors:
Jung-Wook Park, Yonsei University, Korea
Haibo He, University of Rhode Island, USA
Komla Folly, University of Cape Town, South Africa

Editor-in-Chief of IEEE Transactions on Smart Grid
Mohammad Shahidehpour, Illinois Institute of Technology, USA

IEEE Transactions on Neural Networks; Volume 22, Issue 6, June 2011

The following articles appear in the latest issue of IEEE Transactions on
Neural Networks; Volume 22, Issue 6, June 2011.

The articles can be retrieved on IEEE Xplore:
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5779942
or directly by clicking the individual paper URL below.

Volume 22, Issue 6, June 2011

1. Title: Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods
Authors: Sanqing Hu; Guojun Dai; Gregory A. Worrell; Qionghai Dai; Hualou Liang
Page(s): 829-844
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5751700

2. Title: Discriminant Independent Component Analysis
Authors: Chandra Shekhar Dhir; Soo-Young Lee
Page(s): 845-857
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5756242

3. Title: Implementation Study of an Analog Spiking Neural Network for Assisting Cardiac Delay Prediction in a Cardiac Resynchronization Therapy Device
Authors: Qing Sun; Francois Schwartz; Jacques Michel; Yannick Herve; Renzo Dalmolin
Page(s): 858-869
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5756693

4. Title: Kernel Map Compression for Speeding the Execution of Kernel-Based Methods
Authors: Omar Arif; Patricio A. Vela
Page(s): 870-879
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762616

5. Title: A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation
Authors: Yuli Chen; Sung-Kee Park; Yide Ma; Rajeshkanna Ala
Page(s): 880-892
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762617

6. Title: Adaptive Learning Control for Finite Interval Tracking Based on Constructive Function Approximation and Wavelet
Authors: Jian-Xin Xu; Rui Yan
Page(s): 893-905
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5764839

7. Title: Transformation Invariant On-Line Target Recognition
Authors: Khan M. Iftekharuddin
Page(s): 906-918
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5766756

8. Title: Analyzing the Scaling of Connectivity in Neuromorphic Hardware and in Models of Neural Networks
Authors: Johannes Partzsch; Rene Schuffny
Page(s): 919-935
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5765695

9. Title: Practical Training Framework for Fitting a Function and Its Derivatives
Authors: Arjpolson Pukrittayakamee; Martin Hagan; Lionel Raff; Satish T. S. Bukkapatnam; Ranga Komanduri
Page(s): 936-947
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5768082

10. Title: Observability of Boolean Control Networks With State Time Delays
Authors: Fangfei Li; Jitao Sun; Qi-Di Wu
Page(s): 948-954
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5754601

11. Title: Feature Selection Using Probabilistic Prediction of Support Vector Regression
Authors: Jian-Bo Yang; Chong-Jin Ong
Page(s): 954-962
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762619

12. Title: Improvements on Twin Support Vector Machines
Authors: Yuan-Hai Shao; Chun-Hua Zhang; Xiao-Bo Wang; Nai-Yang Deng
Page(s): 962-968
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762620

13. Title: Hyperellipsoidal Statistical Classifications in a Reproducing Kernel Hilbert Space
Authors: Xun Liang; Zhihao Ni
Page(s): 968-975
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5762618

14. Title: Stability and Dissipativity Analysis of Distributed Delay Cellular Neural Networks
Authors: Zhiguang Feng; James Lam
Page(s): 976-981
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5764837

15. Title: Efficient Algorithm for Training Interpolation RBF Networks With Equally Spaced Nodes
Authors: Hoang Xuan Huan; Dang Thi Thu Hien; Huynh Huu Tue
Page(s): 982-988
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5764838

16. Title: Embedded Feature Ranking for Ensemble MLP Classifiers
Authors: Terry Windeatt; Rakkrit Duangsoithong; Raymond Smith
Page(s): 988-994
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771118

Call for Participations: Tutorial on Conformal Predictions

Call for Participation: A Tutorial on "Conformal Predictions for Reliable Machine Learning: Theory and Applications"

Sunday, Jul 31, 2011 (IJCNN 2011)
San Jose, CA, 1:30 - 3:30 pm

Monday, Aug 8, 2011 (AAAI 2011)
San Francisco, CA, 2:00 - 6:00 pm

http://www.public.asu.edu/~vnallure/conformalpredictions/index.html

The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This theory is based on the relationship derived between transductive inference and the randomness deficiency of an i.i.d. (identically independently distributed) sequence of data instances. One of the desirable features of this framework is the calibration of the obtained confidence values in an online setting. While probability/confidence values generated by existing approaches can often be unreliable and difficult to interpret, the theory behind the CP framework guarantees that the confidence values obtained using this transductive inference framework manifest as the actual error frequencies in the online setting i.e. they are well-calibrated. Further, this framework can be applied across all existing classification and regression methods (such as neural networks, Support Vector Machines, k-Nearest Neig! hbors, ridge regression, etc), thus making it a very generalizable approach.

Over the last few years, there has been a growing interest in applying this framework to real-world problems such as clinical decision support, medical diagnosis, sea surveillance, network traffic classification, and face recognition. The promising results have generated in further extensions of the framework to problem settings beyond just classification or regression. The framework has now been extended towards newer settings such as active learning, model selection, feature selection, change detection, outlier detection, and anomaly detection.

The key objectives of this tutorial are:
- to expose the audience to the basic theory of the Conformal Predictions framework
- to demonstrate examples of how the framework can be applied in real-world problems (including code simulations), and
- to provide sample adaptations of the framework to related machine learning problems such as active learning, transfer learning, anomaly detection and model selection, illustrating the potential of the framework in machine learning applications.

Presenters:
Vineeth N Balasubramanian, Arizona State University
Shen-Shyang Ho, University of Maryland

Co-organizers:
Sethuraman Panchanathan, Arizona State University
Vladimir Vovk, Royal Holloway University of London

Please see the website (http://www.public.asu.edu/~vnallure/conformalpredictions/index.html) for more details. We look forward to your participation in the tutorial at IJCNN or AAAI and sincerely hope that by the end of the tutorial you'll be able to utilize the framework in your future research.

Call for Participations: Tutorial on Conformal Predictions

Call for Participation: A Tutorial on "Conformal Predictions for Reliable Machine Learning: Theory and Applications"

Sunday, Jul 31, 2011 (IJCNN 2011)
San Jose, CA, 1:30 - 3:30 pm

Monday, Aug 8, 2011 (AAAI 2011)
San Francisco, CA, 2:00 - 6:00 pm

http://www.public.asu.edu/~vnallure/conformalpredictions/index.html

The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This theory is based on the relationship derived between transductive inference and the randomness deficiency of an i.i.d. (identically independently distributed) sequence of data instances. One of the desirable features of this framework is the calibration of the obtained confidence values in an online setting. While probability/confidence values generated by existing approaches can often be unreliable and difficult to interpret, the theory behind the CP framework guarantees that the confidence values obtained using this transductive inference framework manifest as the actual error frequencies in the online setting i.e. they are well-calibrated. Further, this framework can be applied across all existing classification and regression methods (such as neural networks, Support Vector Machines, k-Nearest Neig! hbors, ridge regression, etc), thus making it a very generalizable approach.

Over the last few years, there has been a growing interest in applying this framework to real-world problems such as clinical decision support, medical diagnosis, sea surveillance, network traffic classification, and face recognition. The promising results have generated in further extensions of the framework to problem settings beyond just classification or regression. The framework has now been extended towards newer settings such as active learning, model selection, feature selection, change detection, outlier detection, and anomaly detection.

The key objectives of this tutorial are:
- to expose the audience to the basic theory of the Conformal Predictions framework
- to demonstrate examples of how the framework can be applied in real-world problems (including code simulations), and
- to provide sample adaptations of the framework to related machine learning problems such as active learning, transfer learning, anomaly detection and model selection, illustrating the potential of the framework in machine learning applications.

Presenters:
Vineeth N Balasubramanian, Arizona State University
Shen-Shyang Ho, University of Maryland

Co-organizers:
Sethuraman Panchanathan, Arizona State University
Vladimir Vovk, Royal Holloway University of London

Please see the website (http://www.public.asu.edu/~vnallure/conformalpredictions/index.html) for more details. We look forward to your participation in the tutorial at IJCNN or AAAI and sincerely hope that by the end of the tutorial you'll be able to utilize the framework in your future research.