Reminder: paper submission deadline: IJCNN 2015

A reminder that the paper submission deadline for the International Joint Conference on Neural Networks (IJCNN) 2015 is January 15, 2015. This conference will be held in Killarney, Ireland, July 12-17, 2015.

IEEE Computational Intelligence Magazine Special Issue on "Computational Intelligence for Brain Computer Interfaces"

Aims and Scope

Brain Computer Interfaces (BCI) aims at establishing a one or two-way communication protocol between the human brain and an electronic device. The research umbrella of BCI has different names and overlaps with different research areas that evolved under the wider objective of connecting human data to an electronic device of some sort. Some of these areas include: adaptive automation, augmented cognition, brain-machine interface, human-machine symbiosis, and human-computer symbiosis.

The last decade has witnessed a rise in the number of researchers working on BCI. With the advances of sensor technologies, efficient signal processing algorithms, and parallel computing, it was possible to finally realize the dream of many researchers who talked about the concept in one form or another in the sixties and seventies including J.C.R. Licklider, R.B. Rouse, and others. Different sensor and measurement technologies are evolving rapidly from the classical functional magnetic resonance imaging (fMRI), functional near infrared (fNIR), Electroencephalography (EEG), to complex integrated psycho-physiological sensor arrays.

Researchers in Computational Intelligence have been better situated than ever to extract knowledge from these signals, transform it to actionable decisions, and designing the intelligent machine that has long been promised and is now overdue. Success has been seen in many medical applications including assisting people on wheelchairs, stroke rehabilitation, and epileptic seizures. In the non-medical domain, BCI has been used for computer games, authentication in cyber security, and air traffic control.

This special issue aims at showcasing the most exciting and recent advances in BCI and related topics. The guest editors invite submissions of previously unpublished, recent and exciting research on BCI. The special issue welcomes survey, position, and research papers

Topics of Interest include:

• Adaptive control schemes for BCI
• Applications
• Augmented cognition and adaptive aiding using BCI
• Big data for brain mining
• Collaborative multi-humans BCI environments
• Computational intelligence applications for BCI
• Data and signal processing techniques for BCI applications
• Evolutionary algorithms for BCI
• Fusion of heterogeneous psycho-physiological sensors
• Fuzzy logic for BCI
• Neuroplasticity induced by brain-computer interactions
• Neural networks for BCI
• Novel sensor technologies for BCI
• Related computational intelligence methods for BCI
• Situation awareness systems for BCI applications
• Swarm techniques for BCI
• Other closely related topics on computational intelligence for BCI

Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify on the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via https://www.easychair.org/conferences/?conf=ieeecimbci2016

Important Dates (for February 2016 Issue)

15th May, 2015: Submission of Manuscripts
15th July, 2015: Notification of Review Results
15th August, 2015: Submission of Revised Manuscripts
15th September, 2015: Submission of Final Manuscripts
February 2016: Special Issue Publication

Guest Editors

Hussein A. Abbass, The University of New South Wales, School of Engineering and Information Technology, Canberra, ACT 2600, Australia.
Cuntai Guan, Institute for Infocomm Research (I2R), 1 Fusionopolis Way, Fusionopolis, 138632, Singapore.
Kay Chen Tan, National University of Singapore, Department of Electrical and Computer Engineering, 4 Engineering Drive, 117583, Singapore.

IEEE TNNLS Special Issue on "Learning in Neuromorphic Systems and Cyborg Intelligence"

Emulating brain-like learning performance has been a key challenge for research in neural networks and learning systems, including recognition, memory and perception. In the last few decades, a variety of approaches for brain-like learning and information processing have been proposed, including approaches based on sparse representations or  hierarchical/deep architectures. While capable of achieving impressive performance, these methods still perform poorly compared to biological systems under a wide variety of conditions. With the availability of neuromorphic hardware providing a fundamentally different technique for data representation, neuromorphic systems, using neural spikes to represent the outputs of sensors and for communication between computing blocks, and using spike timing based learning algorithms, have shown appealing computing characteristics.  However, current neuromorphic learning systems cannot yet achieve the performance figures comparable to what machine learning approaches can offer. Neuromorphic systems are also compatible with another framework called cyborg intelligence. Cyborg intelligence aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via brain-machine interfaces, enhancing strengths and compensating for weaknesses by combining the biological cognition capability with the machine computational capability. In cyborg intelligence, the real-time interaction and exchange of information between biological and artificial neural systems is still an important open challenge, and existing learning approaches would not be able to meet such a challenge. The goal of the special issue is to consolidate the efforts for developing a suitable learning framework for neuromorphic systems and cyborg intelligence and promote research activities in this area.

Scope of the Special Issue

We invite original contributions related to learning in neuromorphic systems and cyborg intelligence, from theories, algorithms, modelling and experiment studies to applications. Topics include but are not limited to: 

• Cognitive computing and cyborg intelligence
• Neuromorphic information/signal processing
• Brain-inspired data representation models
• Neuromorphic learning and cognitive systems
• Co-learning in bio-machine systems
• Spike-based sensing and learning
• Neuromorphic sensors and hardware systems
• Intelligence for embedded systems
• Cognition mechanisms for big data
• Embodied cognition and neuro-robotics.

Important Dates

15 Nov 2014 – Deadline for manuscript submission
15 Feb 2015 – Notification of authors
15 Apr 2015–  Deadline for submission of revised manuscripts
15 May 2015 – Final decision

Guest Editors

Zhaohui Wu, Zhejiang University, China (wzh@zju.edu.cn)
Ryad Benosman, University of Pierre and Marie Curie, France (ryad.benosman@upmc.fr)
Huajin Tang, Institute for Infocomm Research, Singapore and Sichuan University  (huajin.tang@ieee.org)
Shih-Chii Liu, Institute of Neuroinformatics, University of Zurich and ETH Zurich (shih@ini.phys.ethz.ch)

Submission Instructions

1. Read the information for Authors at http://cis.ieee.org/tnnls
2. Submit the manuscript by 15th Nov 2014 at the TNNLS webpage (http://mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript has been submitted to the special issue on Learning in Neuromorphic Systems and Cyborg Intelligence. Send also an email to the guest editors with subject “TNNLS special issue submission” to notify about your submission.

CFP: Special Issue IEEE Computational Intelligence Magazine on "Computational Intelligence for Changing Environments"

Aims and Scope

Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling.

To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non-stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments.

It is possible to use human-level processing power to tackle concept drift in changing enviroments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena.

However, a main outstanding issue in using computational intelligence for changing enviroments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing enviroments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments.

Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing enviroments algorithms and architectures, theoretical foundations, and representations, and their application to real-world problems. We also welcome timely surveys and review papers.

Topics of Interest include (but are not limited to):

• Computational intelligence methodologies and implementation for changing environments
• Transfer learning
• Multitask learning
• Domain adaption
• Incremental Learning architectures
• Incremental Unsupervised and semi-supervised learning architectures
• Incremental Incremental Representation learning and disentangling
• Incremental Knowledge augmentation
• Incremental Adaptive Neuro-fuzzy systems
• Incremental and single-pass data mining
• Incremental Neural Clustering
• Incremental Neural regression
• Incremental Adaptive decision systems
• Incremental Feature selection and reduction
• Incremental Constructive Learning
• Novelty detection in Incremental learning

 Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via

https://www.easychair.org/conferences/?conf=ieeecimcdbil2015.

Important Dates (for August 2015 Issue)

15th November, 2014: Submission of Manuscripts
15th January, 2015: Notification of Review Results
15th Faburary, 2015: Submission of Revised Manuscripts
15th March, 2015: Submission of Final Manuscripts
August 2015: Publication

Guest Editors

Professor Amir Hussain,
University of Stirling,
Stirling FK9 4LA SCOTLAND, UK
Email: ahu@cs.stir.ac.uk

Professor Dacheng Tao,
University of Technology, Sydney
235 Jones Street, Ultimo, NSW 2007, Australia
Email: dacheng.tao@uts.edu.au

Professor Jonathan Wu
University of Windsor
401 Sunset Avenue, Windsor, ON, Canada
Email: jwu@uwindsor.ca

Professor Dongbin Zhao
Institute of Automation, Chinese Academy of Sciences,
No. 95, Zhongguancun East Road, Beijing 100190, China
E-mail: dongbin.zhao@gmail.com

Call for special sessions for CEC 2015

The deadline for special session proposals for the Congress on Evolutionary Computation (CEC) 2015 is October 31, 2014.

Conference paper deadline: FUZZ-IEEE 2015

The deadline for submitting papers to the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2015 is February 8, 2015. This conference will be held in Istanbul, Turkey, August 2-5, 2015.

IEEE Transactions on Evolutionary Computation, Volume 18, Number 5, October 2014

SPECIAL ISSUE ON THEORETICAL FOUNDATIONS OF EVOLUTIONARY COMPUTATION

GUEST EDITORIAL

1. Editorial for the Special Issue on Theoretical Foundations of Evolutionary Computation
Author(s): F. Neumann, B. Doerr, P. K. Lehre, and P. C. Haddow
Pages: 625-627

SPECIAL ISSUE PAPERS

2. Transforming Evolutionary Search into Higher-Level Evolutionary Search by Capturing Problem Structure
Author(s): R. Mills, T. Jansen, and R. A. Watson
Pages: 628-642

3. Convergence of Hypervolume-Based Archiving Algorithms
Author(s): K. Bringmann and T. Friedrich
Pages: 643-657

4. Asymptotic Properties of a Generalized Cross-Entropy Optimization Algorithm
Author(s): Z. Wu and M. Kolonko
Pages: 658-673

5. Reevaluating Immune-Inspired Hypermutations Using the Fixed Budget Perspective
Author(s): T. Jansen and C. Zarges
Pages: 674-688

REGULAR ISSUE PAPERS

6. Differential Evolution With Dynamic Parameters Selection for Optimization Problems
Author(s): R. A. Sarker, S. M. Elsayed, and T. Ray
Pages: 689-707

7. Automated Map Generation for the Physical Traveling Salesman Problem
Author(s): D. Perez, J. Togelius, S. Samothrakis, P. Rohlfshagen, and S. M. Lucas
Pages: 708-720

8. Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering
Author(s): W. Sheng, S. Chen, M. Fairhurst, G. Xiao, and J. Mao
Pages: 721-741

9. A Knowledge-Based Evolutionary Multiobjective Approach for Stochastic Extended Resource Investment Project Scheduling
Author(s): J. Xiong, J. Liu, Y. Chen, and H. A. Abbass
Pages: 742-765

10. The Dynamics of Self-Adaptive Multirecombinant Evolution Strategies on the General Ellipsoid Model
Author(s): H.-G. Beyer and A. Melkozerov
Pages: 764-778

11. Genetic Algorithms for Evolving Computer Chess Programs
Author(s): O. E. David, H. J. van den Herik, M. Koppel, and N. S. Netanyahu
Pages: 779-789

COMMENTARY

12. A Comment on “Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization-Based Classifiers”
Author(s): B. Minnaert and D. Martens
Pages: 790

IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Number 10, October 2014

REGULAR PAPERS

1. A New Learning Algorithm for a Fully Connected Neuro-Fuzzy Inference System
Author(s): C. L. P. Chen, J. Wang, C.-H. Wang, and L. Chen
Pages: 1741-1757

2. Synchronization of Stochastic Dynamical Networks Under Impulsive Control With Time Delays
Author(s): W. Zhang, Y. Tang, Q. Miao, and J.-A. Fang
Pages: 1758-1768

3. Stochastic Learning via Optimizing the Variational Inequalities
Author(s): Q. Tao, Q.-K. Gao, D.-J. Chu, and G.-W. Wu
Pages: 1769-1778

4. Sparse Alignment for Robust Tensor Learning
Author(s): Z. Lai, W. K. Wong, Y. Xu, C. Zhao, and M. Sun
Pages: 1779-1792

5. An Incremental Design of Radial Basis Function Networks
Author(s): H. Yu, P. D. Reiner, T. Xie, T. Bartczak, and B. M. Wilamowski
Pages: 1793-1803

6. Pinning Distributed Synchronization of Stochastic Dynamical Networks: A Mixed Optimization Approach
Author(s): Y. Tang, H. Gao, J. Lu, and J. Kurths
Pages: 1804-1815

7. Deep Networks are Effective Encoders of Periodicity
Author(s): L. Szymanski and B. McCane
Pages: 1816-1827

8. Parsimonious Extreme Learning Machine Using Recursive Orthogonal Least Squares
Author(s): N. Wang, M. J. Er, and M. Han
Pages: 1828-1841

9. LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification
Author(s): F. Charte, A. J. Rivera, M. J. del Jesus, and F. Herrera
Pages: 1842-1854

10. A Fast Algorithm for Nonnegative Matrix Factorization and Its Convergence
Author(s): L.-X. Li, L. Wu, H.-S. Zhang, and F.-X. Wu
Pages: 1855-1863

11. Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
Author(s): M. Hu, H. Li, Y. Chen, Q. Wu, G. S. Rose, and R. W. Linderman
Pages: 1864-1878

12. Multiobjective Optimization for Model Selection in Kernel Methods in Regression
Author(s): D. You, C. F. Benitez-Quiroz, and A. M. Martinez
Pages: 1879-1893

13. Separation of Synchronous Sources Through Phase Locked Matrix Factorization
Author(s): M. S. B. Almeida, R. Vigário, and J. Bioucas-Dias
Pages: 1894-1908

14. Clipping in Neurocontrol by Adaptive Dynamic Programming
Author(s): M. Fairbank, D. Prokhorov, and E. Alonso
Pages: 1909-1920

BRIEF PAPERS

15. Consensus Acceleration in a Class of Predictive Networks
Author(s): H.-T. Zhang and Z. Chen
Pages: 1921-1927

16. H-infinity Output Tracking Control of Discrete-Time Nonlinear Systems via Standard Neural Network Models
Author(s): M. Liu, S. Zhang, H. Chen, and W. Sheng
Pages: 1928-1935

17. Extended Dissipative Analysis for Neural Networks With Time-Varying Delays
Author(s): T. H. Lee, M.-J. Park, J. H. Park, O.-M. Kwon, and S.-M. Lee
Pages: 1936-1941

18. Multilinear Sparse Principal Component Analysis
Author(s): Z. Lai, Y. Xu, Q. Chen, J. Yang, and D. Zhang
Pages: 1942

IEEE Transactions on Fuzzy Systems: Issue 5, Volume 22, October 2014

REGULAR PAPERS

1. Partial Tracking Error Constrained Fuzzy Dynamic Surface Control for a Strict Feedback Nonlinear Dynamic System
Author(s): S.I. Han and J.M. Lee
Pages: 1049-1061

2. Non-L-R Type Fuzzy Parameters in Mathematical Programming Problems
Author(s): C.-F. Hu, M. Adivar, and S.-C. Fang
Pages: 1062-1073

3. A Novel Evolutionary Kernel Intuitionistic Fuzzy C-means Clustering Algorithm
Author(s): K.-P. Lin
Pages: 1074-1087

4. A Dynamic Decoupling Approach to Robust T–S Fuzzy Model-Based Control
Author(s): C.-S. Chiu
1088-1100

5. Relaxed Stability and Stabilization Conditions of Networked Fuzzy Control Systems Subject to Asynchronous Grades of Membership
Author(s): C. Peng, D. Yue, and M.-R. Fei
Pages: 1101-1112

6. Fuzzy n-Ellipsoid Numbers and Representations of Uncertain Multichannel Digital Information
Author(s): G. Wang, P. Shi, B.Wang, and J. Zhang
Pages: 1113-1126

7. Prioritized Measure-Guided Aggregation Operators
Author(s): L. Chen, Z. Xu, and X. Yu
Pages: 1127-1138

8. A Fuzzy Measure Approach to Systems Reliability Modeling
Author(s): R. R. Yager
Pages: 1139-1150

9. Fuzzy Concept Hierarchies and Evidence Resolution
Author(s): F. E. Petry and R. R. Yager
Pages: 1151-1161

10. General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial
Author(s): J. M. Mendel
Pages: 1162-1182

11. Nonfragile Control With Guaranteed Cost of T–S Fuzzy Singular Systems Based on Parallel Distributed Compensation
Author(s): C. Han, L. Wu, H.K. Lam, and Q. Zeng
Pages: 1183-1196

12. On the Monotonicity of Interval Type-2 Fuzzy Logic Systems
Author(s): C. Li, J. Yi, and G. Zhang
Pages: 1197-1212

13. Robust Model Predictive Control for Discrete-Time Takagi–Sugeno Fuzzy Systems With Structured Uncertainties and Persistent Disturbances
Author(s): W. Yang, G. Feng, and T. Zhang
Pages: 1213-1228

14. Accelerating Fuzzy-C Means Using an Estimated Subsample Size
Author(s): J. K. Parker and L. O. Hall
Pages: 1229-1244

15. On Advanced Computing With Words Using the Generalized Extension Principle for Type-1 Fuzzy Sets
Author(s): M.R. Rajati and J.M. Mendel
Pages: 1245-1261

16. Adaptive Sliding-Mode Antisway Control of Uncertain Overhead Cranes With High-Speed Hoisting Motion
Author(s): M.-S. Park, D. Chwa, and M. Eom
Pages: 1262-1271

17. The Neuro-Fuzzy Computing System With the Capacity of Implementation on a Memristor Crossbar and Optimization-Free Hardware Training
Author(s): F. Merrikh-Bayat, F. Merrikh-Bayat, and S. B. Shouraki
Pages: 1272-1287

18. Adaptive Fuzzy Control for MIMO Nonlinear Systems With Nonsymmetric Control Gain Matrix and Unknown Control Direction
Author(s): W. Shi
Pages: 1288-1300

19. Two-mode Indirect Adaptive Control Approach for the Synchronization of Uncertain Chaotic Systems by the Use of a Hierarchical Interval Type-2 Fuzzy Neural Network
Author(s): A. Mohammadzadeh, O. Kaynak, and M. Teshnehlab
Pages: 1301-1312

20. Fuzzy Control Design for Nonlinear ODE-Hyperbolic PDE-Cascaded Systems: A Fuzzy and Entropy-Like Lyapunov Function Approach
Author(s): J.-W. Wang, H.-N. Wu, and H.-X. Li
Pages: 1313-1324

21. Attribute Reduction for Heterogeneous Data Based on the Combination of Classical and Fuzzy Rough Set Models
Author(s): D. Chen and Y. Yang
Pages: 1325-1334

SHORT PAPERS

22. On Computing Normalized Interval Type-2 Fuzzy Sets
Author(s): J. M. Mendel and M. R. Rajati
Pages: 1335-1340

23. Adaptive Fuzzy Output-Feedback Control of Pure-Feedback Uncertain Nonlinear Systems With Unknown Dead Zone
Author(s): Y. Li and S. Tong
Pages: 1341-1346

24. A Heuristic Method to Compute the Approximate Postinverses of a Fuzzy Matrix
Author(s): P. Li
Pages: 1347-1351

25. Removal of High-Density Salt-and-Pepper Noise in Images With an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean
Author(s): F. Ahmed and S. Das
Pages: 1352-1358

26. Adaptive Fuzzy Control for a Class of Nonlinear Discrete-Time Systems With Backlash
Author(s): Y.-J. Liu and S. Tong
Pages: 1359-1364

27. Adaptive Fuzzy Decentralized Output Stabilization for Stochastic Nonlinear Large-Scale Systems With Unknown Control Directions
Author(s): S. Tong, S. Sui, and Y. Li
Pages: 1365

Reminder: conference paper deadline for IEEE CIG 2015

A reminder that the paper submission deadline for the 2015 IEEE Conference on Computational Intelligence in Games (IEEE CIG) is April 2, 2015. This conference will be held in Tainan, Taiwan, 31 August to 2 September, 2015.

Reminder: paper submission deadline for CEC 2015

A reminder that the deadline for submitting papers to the IEEE Congress on Evolutionary Computation (IEEE CEC) 2015 is December 19, 2014. This conference will be held in Sendai, Japan, 25-28 May, 2015.

Neural Networks Special Issue: Neural Network Learning in Big Data

Big data is much more than storage of and access to data. Analytics plays an important 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 encounter theoretical challenges in scaling up to big data. Plus there are challenges of high dimensionality, velocity and variety for all types of machine learning algorithms. 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. The brain is arguably the best and most elegant big data processor and is the inspiration for neural network learning methods. Neural network 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 for stored big data. For stored big data, neural network algorithms can learn from all of the data instead of from samples of the data. And the same is true for streaming data where not all of the data is actually stored. In general, online, incremental learning algorithms are less vulnerable to size of the data. Neural network algorithms, in particular, can take advantage of massively parallel (brain-like) computations, which use very simple processors, that other machine learning technologies cannot. Specialized neuromorphic hardware, originally meant for large-scale brain simulations, is becoming available to implement these algorithms in a massively parallel fashion. Neural network algorithms, therefore, can deliver very fast and efficient real-time learning through the use of hardware and this could be particularly useful for streaming data in the Industrial Internet. Neural network technologies thus can become significant components of big data analytics platforms and this special issue will begin that journey with big data.

For this special issue of Neural Networks, we invite papers that address many of the challenges of learning from big data. In particular, we are interested in papers on 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 online learning to solve real-world big data problems (e.g. health care, transportation, and electric power and energy management).

RECOMMENDED TOPICS:

Topics of interest include, but are not limited to:

1. Autonomous, online, incremental learning – theory, algorithms and applications in big data
2. High dimensional data, feature selection, feature transformation – theory, algorithms and applications for big data
3. Scalable neural network algorithms for big data
4. Neural network learning algorithms for high-velocity streaming data
5. Deep neural network learning
6. Neuromorphic hardware for scalable neural network learning
7. Big data analytics using neural networks in healthcare/medical applications
8. Big data analytics using neural networks in electric power and energy systems
9. Big data analytics using neural networks in large sensor networks
10. Big data and neural network learning in computational biology and bioinformatics

SUBMISSION PROCEDURE:

Prospective authors should visit http://ees.elsevier.com/neunet/ for information on paper submission. During the submission process, there will be steps to designate the submission to this special issue. However, please indicate on the first page of the manuscript that the manuscript is intended for the Special Issue: Neural Network Learning in Big Data. Manuscripts will be peer reviewed according to Neural Networks guidelines.

Manuscript submission due: December 15, 2014
First review completed: March 1, 2015
Revised manuscript due: April 1, 2015
Second review completed, final decisions to authors: April 15, 2015
Final manuscript due: April 30, 2015

GUEST EDITORS:

• Asim Roy, Arizona State University, USA (asim.roy@asu.edu) (lead guest editor)
• Kumar Venayagamoorthy, Clemson University, USA (gkumar@ieee.org)
• Nikola Kasabov, Auckland University of Technology, New Zealand (nkasabov@aut.ac.nz)
• Irwin King, Chinese University of Hong Kong, China (irwinking@gmail.com)

IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games, Volume 6, Issue 3, September 2014

1. Real-Time Monte Carlo Tree Search in Ms Pac-Man
Author(s): T. Pepels, M. H. M. Winands, and M. Lanctot
Pages: 245-257

2. An Automatically Generated Evaluation Function in General Game Playing
Author(s): K. Walędzik and J. Mańdziuk
Pages: 258-270

3. A Computational Model of Plan-Based Narrative Conflict at the Fabula Level
Author(s): S. G. Ware, R. M. Young, B. Harrison, and D. L. Roberts
Pages: 271-288

4. Good Machine Performance in Turing’s Imitation Game
Author(s): K. Warwick and H. Shah
Pages: 289-300

5. Preference Learning for Move Prediction and Evaluation Function Approximation in Othello
Author(s): T. P. Runarsson and S. M. Lucas
Pages: 300-313

Evolving Systems Volume 5, Issue 3, September 2014

Review

1. Online and interactive self-adaptive learning of user profile using incremental evolutionary algorithms
Author(s): Abdelhamid Bouchachia , Arthur Lena & Charlie Vanaret
Pages: 143-157

Original Papers

2. OWA filters and forecasting models applied to electric power load time series
Author(s): R. Ballini & R. R. Yager
Pages: 159-173

3. Visualization of evolving fuzzy rule-based systems
Author(s): Sascha Henzgen , Marc Strickert & Eyke Hüllermeier
Pages: 175-191

4. Interval type-2 fuzzy logic controller design for TCSC
Author(s): Manoj Kumar Panda , G. N. Pillai & Vijay Kumar
Pages: 193-208

5. Classification of mammography images based on cellular automata and Haralick parameters
Author(s): Sarah Benmazou , Hayet Farida Merouani , Soumia Layachi & Beledjhem Nedjmeddine
Pages: 209-216

IEEE Transactions on Neural Networks and Learning Systems: Volume 25, Number 9, September 2014

SPECIAL ISSUE ON COMPLEX- AND HYPERCOMPLEX-VALUED NEURAL NETWORKS

1. Guest Editorial: Special Issue on Complex- and Hypercomplex-Valued Neural Networks
Author(s): A. Hirose, I. Aizenberg, and D. P. Mandic
Page(s): 1597-1599

SPECIAL ISSUE PAPERS

2. Complex-Valued Recurrent Correlation Neural Networks
Author(s): M. E. Valle
Page(s): 1600-1612

3. The Field of Values of a Matrix and Neural Networks
Author(s): G. M. Georgiou
Page(s): 1613-1620

4. Different Complex ZFs Leading to Different Complex ZNN Models for Time-Varying Complex Generalized Inverse Matrices
Author(s): B. Liao and Y. Zhang
Page(s): 1621-1631

5. MLMVN With Soft Margins Learning
Author(s): I. Aizenberg
Page(s): 1632-1644

6. Modified Multivalued Neuron With Periodic Tolerant Activation Function
Author(s): J.-P. Chen and S.-J. Lee
Page(s): 1645-1658

7. A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System
Author(s): K. Subramanian, R. Savitha, and S. Suresh
Page(s): 1659-1672

8. Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems
Author(s): S. Chen, X. Hong, J. Gao, and C. J. Harris
Page(s): 1673-1685

9. Fading Channel Prediction Based on Combination of Complex-Valued Neural Networks and Chirp Z-Transform
Author(s): T. Ding and A. Hirose
Page(s): 1686-1695

10. On the Correction of Anomalous Phase Oscillation in Entanglement Witnesses Using Quantum Neural Networks
Author(s): E. C. Behrman, R. E. F. Bonde, J. E. Steck, and J. F. Behrman
Page(s): 1696-1703

SPECIAL ISSUE BRIEF PAPERS

11. Global Stability Criterion for Delayed Complex-Valued Recurrent Neural Networks
Author(s): Z. Zhang, C. Lin, and B. Chen
Page(s): 1704-1708

12. Further Investigate the Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
Author(s): T. Fang and J. Sun
Page(s): 1709-1713

13. Threshold Complex-Valued Neural Associative Memory
Author(s): P. Zheng
Page(s): 1714-1718

14. Principal Component Analysis With Complex Kernel: The Widely Linear Model
Author(s): A. Papaioannou and S. Zafeiriou
Page(s): 1719-1726

15. Ultrawideband Direction-of-Arrival Estimation Using Complex-Valued Spatiotemporal Neural Networks
Author(s): K. Terabayashi, R. Natsuaki, and A. Hirose
Page(s): 1727-1732

16. Adaptive Dynamic Programming for a Class of Complex-Valued Nonlinear Systems
Author(s): R. Song, W. Xiao, H. Zhang, and C. Sun
Page(s): 1733

Conference abstract deadline: NCEI 2015

The deadline for submitting abstracts to the 13th International Conference on Neuro-Computing and Evolving Intelligence (NCEI) 2015 is 30 November, 2014. This conference will be held in Auckland, New Zealand, 19-20 February, 2015.

Neural Networks Volume 58, Pages 1-148, October 2014

Special Issue on Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis
Edited by Amir Hussain, Erik Cambria, Björn Schuller and Newton Howard

1. Affective neural networks and cognitive learning systems for big data analysis  
Pages: 1-3
Author(s): Amir Hussain, Erik Cambria, Björn Schuller, Newton Howard
   
2. Discrete particle swarm optimization for identifying community structures in signed social networks
Pages: 4-13
Author(s): Qing Cai, Maoguo Gong, Bo Shen, Lijia Ma, Licheng Jiao
   
3. An incremental community detection method for social tagging systems using locality-sensitive hashing
Pages: 14-28
Author(s): Zhenyu Wu, Ming Zou
   
4. Affective topic model for social emotion detection
Pages: 29-37
Author(s): Yanghui Rao, Qing Li, Liu Wenyin, Qingyuan Wu, Xiaojun Quan
   
5. Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing
Pages: 38-49
Author(s): Alexander Gross, Dhiraj Murthy
   
6. Semi-supervised word polarity identification in resource-lean languages
Pages: 50-59
Author(s): Iman Dehdarbehbahani, Azadeh Shakery, Heshaam Faili
   
7. Incorporating conditional random fields and active learning to improve sentiment identification
Pages: 60-67
Author(s): Kunpeng Zhang, Yusheng Xie, Yi Yang, Aaron Sun, Hengchang Liu, Alok Choudhary
   
8. A classification of user-generated content into consumer decision journey stages
Pages: 68-81
Author(s): Silvia Vázquez, Óscar Muñoz-García, Inés Campanella, Marc Poch, Beatriz Fisas, Nuria Bel, Gloria Andreu
   
9. Sentiments analysis at conceptual level making use of the Narrative Knowledge Representation Language
Pages: 82-97
Author(s): Gian Piero Zarri
   
10. Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy
Pages: 98-110
Author(s): Yi Cai, Qing Li, Haoran Xie, Huaqin Min
   
11. Community-aware user profile enrichment in folksonomy
Pages: 111-121
Author(s): Haoran Xie, Qing Li, Xudong Mao, Xiaodong Li, Yi Cai, Yanghui Rao
   
12. A multi-label, semi-supervised classification approach applied to personality prediction in social media
Pages: 122-130
Author(s): Ana Carolina E.S. Lima, Leandro Nunes de Castro
   
13. Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding
Pages: 131-147
Author(s): Daniel Olsher

IEEE Transactions on Evolutionary Computation, Volume 18, Number 4, August 2014

1. Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems
Author(s): M. Iqbal, W. N. Browne, and M. Zhang
Pages: 465-480

2. Quick Hypervolume
Author(s): L. M. S. Russo and A. P. Francisco
Pages: 481-502

3. Ant Colony Optimization for Mixed-Variable Optimization Problems
Author(s): T. Liao, K. Socha, M. A. Montes de Oca, T. Stutzle, and M. Dorigo
Pages: 503-518

4. Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables
Author(s): H. Karshenas, R. Santana, C. Bielza, and P. Larranaga
Pages: 519-542

5. Evolving an Improved Algorithm for Envelope Reduction Using a Hyper-Heuristic Approach
Author(s): B. Koohestani and R. Poli
Pages: 543-558

6. Evolving Classifiers to Recognize the Movement Characteristics of Parkinson’s Disease Patients
Author(s): M. A. Lones, S. L. Smith, J. E. Alty, S. E. Lacy, K. L. Possin, D. R. S. Jamieson, and A. M. Tyrrell
Pages: 559-576

7. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Author(s): K. Deb and H. Jain
Pages: 577-601

8. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
Author(s): H. Jain and K. Deb
Pages: 602

IEEE Transactions on Fuzzy Systems, Volume 22, Number 4, August 2014

REGULAR PAPERS

1. Design of Fuzzy-Neural-Network-Inherited Backstepping Control for Robot Manipulator Including Actuator Dynamics
Author(s): R.-J.Wai and R.Muthusamy
Pages: 709-722

2. Rule-Based Cooperative Continuous Ant Colony Optimization to Improve the Accuracy of Fuzzy System Design
Author(s): C.-F.Juang, C.-W. Hung, and C.-H. Hsu
Pages: 723-735

3. A Model-Based Fault Detection and Prognostics Scheme for Takagi–Sugeno Fuzzy Systems
Author(s): B.T. Thumati, M. A. Feinstein, and S. Jagannathan
Pages: 736-748

4. Intuitionistic Fuzzy Analytic Hierarchy Process
Author(s):Z. Xu and H. Liao
Pages: 749-761

5. A Semisupervised Multiagent System Model to Support Consensus-Reaching Processes
Author(s): I. Palomares and L. Martinez
Pages: 762-777
 
6. Context-Dependent Fuzzy Systems With Application to Time-Series Prediction
Author(s): D. T. Ho and J. M. Garibaldi
Pages: 778-790
 
7. Intelligent Control Using the Wavelet Fuzzy CMAC Backstepping Control System for Two-Axis Linear Piezoelectric Ceramic Motor Drive Systems
Author(s): C.-M. Lin and H.-Y. Li
Pages: 791-802

8. Moment Adaptive Fuzzy Control and Residue Compensation
Author(s): T. Tao and S.-F. Su
Pages: 803-816
 
9. The Exponential Stability and Asynchronous Stabilization of a Class of Switched Nonlinear System Via the T–S Fuzzy Model
Author(s): Y. Mao, H. Zhang, and S. Xu
Pages: 817-828
 
10. Cooperative Coevolution for Large-Scale Optimization Based on Kernel Fuzzy Clustering and Variable Trust Region Methods
Author(s): J. Fan, J. Wang, and M. Han
Pages: 829-839
 
11. The Reduction of Interval Type-2 LR Fuzzy Sets
Author(s): C.-L. Chen, S.-C. Chen, and Y.-H. Kuo
Pages: 840-858

12. From Fuzzy Cognitive Maps to Granular Cognitive Maps
Author(s): W. Pedrycz and W. Homenda
Pages: 859-869
 
13. Robust H8 Control for Stochastic T–S Fuzzy Systems via Integral Sliding-Mode Approach
Author(s): Q. Gao, G. Feng, L. Liu, J. Qiu, and Y. Wang
870-881
 
14. Multicriteria Decision-Making With Imprecise Importance Weights
Author(s): R. R. Yager and N. Alajlan
Pages: 882-891
 
15. Simulation of Fuzzy Queueing Systems With a Variable Number of Servers, Arrival Rate, and Service Rate
Author(s): E.Munoz and E. H. Ruspini
Pages: 892-903
 
16. Membership Function Design for Multifactorial Multivariate Data Characterizing and Coding in Human Component System Studies
Author(s): P. Loslever
Pages: 904-918
 
17. OptiFel: A Convergent Heterogeneous Particle Swarm Optimization Algorithm for Takagi–Sugeno Fuzzy Modeling
Author(s): N.J. Cheung, X.-M. Ding, and H.-B. Shen
Pages: 919-933
 
18. The Generalized TP Model Transformation for T–S Fuzzy Model Manipulation and Generalized Stability Verification
Author(s): P. Baranyi
Pages: 934-948
 
19. Hypermatching: Similarity Matching With Extreme Values
Author(s): R. R. Yager and F. E. Petry
Pages: 949-957
 
20. Pythagorean Membership Grades in Multicriteria Decision Making
Author(s): R. R. Yager
Pages: 958-965
 
21. Dual Bipolar Measures of Atanassov’s Intuitionistic Fuzzy Sets
Author(s): L.-H. Chen, and C.-C. Tu
Pages: 966-982
 
22. Generalized Markov Models for Real-Time Modeling of Continuous Systems
Author(s): D. P. Filev and I. Kolmanovsky
Pages: 983-998
 
23. GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier
Author(s): A. Bouchachia and C. Vanaret
Pages: 999-1018

SHORT PAPERS

24. Fuzzy-Model-Based D-Stability and Nonfragile Control for Discrete-Time Descriptor Systems With Multiple Delays
Author(s): F. Li, P. Shi, L. Wu, and X. Zhang
Pages: 1019-1025
 
25. Further Studies on Control Synthesis of Discrete-Time T–S Fuzzy Systems via Useful Matrix Equalities
Author(s): X. Xie, D. Yue, and X. Zhu
Pages: 1026-1030
 
26. Stability Analysis of Positive Interval Type-2 TSK Systems With Application to Energy Markets
Author(s): M.S.  Fadali and S. Jafarzadeh
Pages: 1031-1038
 
27. Orness Measure of OWA Operators: A New Approach
Author(s): A. Kishor, A. K. Singh, and N. R. Pal
Pages: 1039-1044
 
28. A Characterization of the Orthogonal Grid Constructions of Copulas
Author(s): J. F. Sanchez and M. Ubeda-Flores
Pages: 1045

Neural Networks Volume 57, Pages 1-166, September 2014

Neuroscience

1. Bayesian common spatial patterns for multi-subject EEG classification
Author(s): Hyohyeong Kang, Seungjin Choi
Pages: 39-50
  
2. Estimating the correlation between bursty spike trains and local field potentials
Author(s): Zhaohui Li, Gaoxiang Ouyang, Li Yao, Xiaoli Li
Pages: 63-72

3. Effect of hybrid circle reservoir injected with wavelet-neurons on performance of echo state network
Author(s): Hongyan Cui, Chen Feng, Yuan Chai, Ren Ping Liu, Yunjie Liu
Pages: 141-151

Learning Systems

4. Noise model based image -support vector regression with its application to short-term wind speed forecasting
Author(s): Qinghua Hu, Shiguang Zhang, Zongxia Xie, Jusheng Mi, Jie Wan
Pages: 1-11
   
5. Using financial risk measures for analyzing generalization performance of machine learning models
Author(s): Akiko Takeda, Takafumi Kanamori
Pages: 29-38
   
6. Fast Gaussian kernel learning for classification tasks based on specially structured global optimization
Author(s): Shangping Zhong, Tianshun Chen, Fengying He, Yuzhen Niu
Pages: 51-62
   
7. Semi-supervised information-maximization clustering
Author(s): Daniele Calandriello, Gang Niu, Masashi Sugiyama
Pages: 103-111
   
8. Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation
Author(s): Voot Tangkaratt, Syogo Mori, Tingting Zhao, Jun Morimoto, Masashi Sugiyama
Pages: 128-140

Mathematical and Computational Analysis

9. Periodicity and dissipativity for memristor-based mixed time-varying delayed neural networks via differential inclusions
Author(s): Lian Duan, Lihong Huang
Pages: 12-22
   
10. Comparing fixed and variable-width Gaussian networks
Author(s): Věra Kůrková, Paul C. Kainen
Pages: 23-28
   
11. Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach
Author(s): A. Chandrasekar, R. Rakkiyappan, Jinde Cao, S. Lakshmanan
Pages: 79-93
   
12. Sudoku associative memory
Author(s): Jiann-Ming Wu, Pei-Hsun Hsu, Cheng-Yuan Liou
Pages: 112-127

Engineering and Applications

13. Neural network for solving Nash equilibrium problem in application of multiuser power control
Author(s): Xing He, Junzhi Yu, Tingwen Huang, Chuandong Li, Chaojie Li
Pages: 73-78
   
14. A new switching design to finite-time stabilization of nonlinear systems with applications to neural networks
Author(s): Xiaoyang Liu, Daniel W.C. Ho, Wenwu Yu, Jinde Cao
Pages: 94-102
   
15. Image denoising using nonsubsampled shearlet transform and twin support vector machines
Author(s): Hong-Ying Yang, Xiang-Yang Wang, Pan-Pan Niu, Yang-Cheng Liu
Pages: 152-165