Showing posts with label IJCNN. Show all posts
Showing posts with label IJCNN. Show all posts

Wednesday, 23 November 2016

IJCNN 2017 Deadline Extended

IJCNN 2017 - International Joint Conference on Neural Networks
May 14-19, 2017, Anchorage, Alaska
http://www.ijcnn.org/
You can also follow IJCNN2017 on Facebook and Twitter.

Important Announcement

The paper submission deadline has been extended by two weeks, to 01Dec2016!

Important Dates


  • Paper Submission                         December 01, 2016
  • Paper Decision Notification         January 20, 2017
  • Camera-Ready Submission          February 20, 2017

CALL FOR PAPERS       http://www.ijcnn.org/call-for-papers

The 2017 International Joint Conference on Neural Networks (IJCNN 2017) will be held at the William A. Egan Civic and Convention Center in Anchorage, Alaska, USA, May 14–19, 2017. The conference is organized jointly by the International Neural Network Society and the IEEE Computational Intelligence Society, and is the premiere international meeting for researchers and other professionals in neural networks and related areas.  It will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence.  In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest

For the latest updates, follow us on Facebook (https://fb.me/ijcnn2017/) and Twitter (@ijcnn2017).

Paper Submission is Open

http://www.ijcnn.org/call-for-papers

  • Regular paper can have up to 8 pages in double-column IEEE Conference format
  • All papers are to be prepared using IEEE-compliant Latex or Word templates on paper of U.S. letter size.
  • All submitted papers will be checked for plagiarism through the IEEE CrossCheck system. 
  • Papers with significant overlap with the authors own papers or other papers will be rejected without review.

Topics and Areas of Interest

The range of topics covered include, but is not limited to, the following.

(See http://ijcnn.org for a more detailed list of topics).

  • Deep learning
  • Neural network theory & models 
  • Computational neuroscience 
  • Cognitive models 
  • Brain-machine interfaces 
  • Embodied robotics 
  • Evolutionary neural systems 
  • Neurodynamics 
  • Neuroinformatics 
  • Neuroengineering 
  • Hardware, memristors 
  • Neural network applications 
  • Machine perception (vision, speech, ...) 
  • Social media 
  • Big data
  • Pattern recognition
  • Machine learning
  • Collective intelligence
  • Hybrid systems
  • Self-aware systems
  • Data mining
  • Sensor networks
  • Agent-based systems
  • Computational biology
  • Bioinformatics
  • Artificial life
  • Connectomics
  • Philosophical issues

Organizing Committee

The full organizing committee can be found at: http://www.ijcnn.org/organizing-committee

General Chair
Yoonsuck Choe, Texas A and M University, USA

Program Chair
Christina Jayne, Robert Gordon University, UK

Technical Co-Chairs
Irwin King, The Chinese University of Hong Kong, China
Barbara Hammer, University of Bielefeld, Germany

Sponsoring Organizations

  • INNS - International Neural Network Society
  • IEEE - Computational Intelligence Society
  • BSCS - Budapest Semester in Cognitive Science

Sunday, 28 August 2016

Call for papers: IJCNN 2017

30th International Joint Conference on Neural Networks (IJCNN 2017)
May 14-19, 2017,  Anchorage, Alaska, USA
http://www.ijcnn.org/

IJCNN is the premier international conference in the area of neural network theory, analysis, and applications. Co-sponsored by the International Neural Network Society (INNS) and the IEEE Computational Intelligence Society (IEEE-CIS), over the last three decades this conference and its predecessors has hosted [past, present, and future] leaders of neural network research. IJCNN 2017 will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence. In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest.

The 2017 International Joint Conference on Neural Networks (IJCNN 2017) will be held at the William A. Egan Civic and Convention Center in Anchorage, Alaska, USA, May 14-19, 2017. "... Only in Anchorage can you meet a moose, walk on a glacier and explore a vast, natural park all in a single day. Between mountains and an inlet, surrounded by national parks and filled with Alaska wildlife, Anchorage combines the best of Alaska in a city that has the comforts of home and the hospitality of the Last Frontier. ..."

For the latest updates, follow us on Facebook (https://fb.me/ijcnn2017/) and Twitter (@ijcnn2017).

Important Dates

  • Special Session, Panel Sesion & Competition Proposals September 15, 2016
  • Tutorial and Workshop Proposals October 15, 2016
  • Paper Submission November 15, 2016
  • Paper Decision Notification January 20, 2017
  • Camera-Ready Submission February 20, 2017

Paper Submission is now Open 
http://www.ijcnn.org/call-for-papers

  • Regular paper can have up to 8 pages in double-column IEEE Conference format
  • All papers are to be prepared using IEEE-compliant Latex or Word templates on paper of U.S. letter size.
  • All submitted papers will be checked for plagiarism through the IEEE CrossCheck system. 
  • Papers with significant overlap with the authors own papers or other papers will be rejected without review.

Call for Special Sessions

http://www.ijcnn.org/call-for-special-sessions

The IJCNN 2017 Program Committee solicits proposals for special sessions within the technical scopes of the conference. Special sessions, to be organized by international recognized experts, aim to bring together researchers focused in special, novel, and challenging topics. Fast-developing themes such as Deep Learning, Big Data, or applications to fields like chemistry, biology, computer games, robotics, etc. are examples.

Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the regular sessions. Researchers interested in organizing special sessions are invited to submit a formal proposal using the on-line form of the Special Sessions webpage. Due to large expected number of submissions, please do not directly email the information to the special session co-chairs Derong Liu, University of Chicago & Chinese Academy of Sciences. Beijing, and Tatiana Tambouratzis, University of Piraeus, Greece.

For further details, please refer to http://www.ijcnn.org/call-for-special-sessions

Any questions regarding this proposal can be asked to the Special Session Chairs:
Derong Liu, University of Chicago, USA. E-mail: derong@uic.edu
Tatiana Tambouriatzis, University of Piraeus, Greece. E-mail: tatianatambouratzis@gmail.com

Call for Competitions

http://www.ijcnn.org/call-for-competition

Competitions have long been a valuable component of IJCNN, and have been extremely important over time in identifying breakthroughs (as has been the case with Deep Learning since 2008).

Detailed information can be found on the IJCNN 2017 website at http://www.ijcnn.org/call-for-competition

Contact Chair Juyang (John) Weng, Michigan State University, USA to set up a competition for IJCNN 2017.


Call for Panel Session

http://www.ijcnn.org/call-for-panels

Panel sessions provide forums for lively, interactive discussions among world-leading experts in specific areas. Panel Session proposals are solicited in a broad range of areas related to NNs, including but not limited to the following: hot topics and challenges in NNS, such as Deep learning, Big Data, Brain-Computer Interfaces, new generation of AI; history of NNs, with special emphasis on the upcoming 30th birthday of INNS; government funding opportunities; outreach to industry and to sister societies, and many more.

Detailed information can be found on the IJCNN 2017 website at http://www.ijcnn.org/call-for-panels

Contact Chair Robert Kozma, Director of CLION, University of Memphis, Tennessee, USA to set up a panel for IJCNN 2017.


Topics and Areas of Interest

The range of topics covered include, but is not limited to, the following.
(See http://ijcnn.org for a more detailed list of topics).
  • Deep learning
  • Neural network theory & models 
  • Computational neuroscience 
  • Cognitive models 
  • Brain-machine interfaces 
  • Embodied robotics 
  • Evolutionary neural systems 
  • Neurodynamics 
  • Neuroinformatics 
  • Neuroengineering 
  • Hardware, memristors 
  • Neural network applications 
  • Machine perception (vision, speech, ...) 
  • Social media 
  • Big data
  • Pattern recognition
  • Machine learning
  • Collective intelligence
  • Hybrid systems
  • Self-aware systems
  • Data mining
  • Sensor networks
  • Agent-based systems
  • Computational biology
  • Bioinformatics
  • Artificial life
  • Connectomics
  • Philosophical issues

Sponsoring Organizations

  • INNS - International Neural Network Society
  • IEEE - Computational Intelligence Society

Organizing Committee

The full organizing committee can be found at: http://www.ijcnn.org/organizing-committee

General Chair
  • Yoonsuck Choe, Texas A and M University, USA

Program Chair
  • Christina Jayne, Robert Gordon University, UK

Technical Co-Chairs
  • Irwin King, The Chinese University of Hong Kong, China
  • Barbara Hammer, University of Bielefeld, Germany


Tuesday, 23 June 2015

Call for Participation in IJCNN 2015, Killarney, Ireland on July 12 - 17, 2015

We are pleased to announce that the 2015 International Joint Conference on Neural Networks (IJCNN 2015) will be held in Killarney, Ireland on July 12 - 17, 2015. This is the premiere international conference in the area of neural networks, which will be organized by the International Neural Network Society (INNS), and sponsored jointly by INNS and the IEEE Computational Intelligence Society - the two leading professional organizations for researchers working in neural networks.

You are kindly invited to attend this great conference in beautiful Killarney, Ireland on July 12 - 17, 2015.

For more details for this IJCNN 2015, you can visit http://www.ijcnn.org/ or http://www.ic-ic.org/ijcnn2015/

De-Shuang Huang,
dshuang@tongji.edu.cn
Tongji University, China
IJCNN 2015 General Chair

Saturday, 6 December 2014

Call for Papers IJCNN 2015 Special Session "Emerging Methodologies for Big Data Integration"

Over the years, huge quantities of data have been generated by large-scale scientific experiments (biomedical, “omic”, imaging, astronomical, etc.), big industrial companies and on the web. One of the main characteristics of such Big Data is that they are multi-view, i.e. there are multiple sources (in the “omics” sciences, experiments related to mRNA, miRNA etc.), relate the same patterns (in this case patients) or multi-domain (in biomedical applications for examples, “omics, imaging and clinical data).

As a consequence, new methodologies based on neural networks, machine and statistical learning, computation Intelligence and others, have been proposed to integrate these kinds of big data and to elicit relevant information to infer novel models and correlations.

The aim of the special session is to solicit new approaches to real world scientific and industrial big data integration, as well as applications of above mentioned Big Data methodologies.

Topics

Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:
  • Multi-view learning
  • Multi-view clustering
  • data fusion
  • data integration 
  • multi-view data applications 
  • multi domain data applications

THE DEADLINE FOR THE PAPER SUBMISSION TO THE SPECIAL SESSION IS THE SAME OF IJCNN 2015, January 15th 2015.

All the submissions will be peer-reviewed with the same criteria used for other contributed papers.

Perspective authors will submit their papers through the IJCNN2015 conference submission system at http://www.ijcnn.org/

Please make sure to select the Special Session "Emerging Methodologies for Big Data Integration " from the "S. SPECIAL SESSION TOPICS" name in the "Main Research topic" dropdown list;

Templates and instructions for authors will be provided on the IJCNN webpage http://www.ijcnn.org/

All papers submitted to the special sessions will be subject to the same peer-review procedure as regular papers, accepted papers will be published in the conference proceedings.

Further information about IJCNN 2015 can be found at http://www.ijcnn.org/
and about the special session at
http://neuronelab.unisa.it/emerging-methodologies-for-big-data-integration/

Organizers

  • Amir Hussain Professor of Computing Science and founding Director of the Cognitive Signal-Image Processing and Control Systems Research (COSIPRA) Laboratory,  University of Stirling, UK
  • Giovanni Montana Professor and Chair in Biostatistics and Bioinformatics, Biomedical Engineering Department, King’s College, London, UK
  • Francesco Carlo MorabitoProfessor and Chair of the Neurolab, Dipartimento DICEAM, Università Mediterranea di Reggio Calabria, Italy
  • Roberto TAGLIAFERRIProfessor and Chair of the Neuronelab, Dipartimento di Informatica, Università di Salerno, Italy

Technical Program Committee

  • Elia Mario Biganzoli, Università di Milano, Italy
  • Erik Cambria, NTU, Singapore
  • Ciro Donalek, Caltech, CA, USA
  • Anna Esposito, Seconda Università di Napoli, Italy
  • Marcos Faundez-Zanuy, Escola Universitaria Politecnica de Mataro (Tecnocampus), Spain
  • Alexander Gelbukh, National Polytechnic Institute, Mexico
  • Dario Greco, FIOH, Finland
  • Newton Howard, MIT Media Lab, USA
  • Pietro Liò, University of Cambridge, UK
  • Bin Luo, Anhui University, China
  • Mufti Mahmud, Antwerp University, Belgium
  • Riccardo Rizzo, CNR, Italy
  • Domenico Ursino, Università Mediterranea di Reggio Calabria, Italy
  • Alfredo Vellido, Universidad Politécnica de Cataluña, Spain
  • Pierangelo Veltri, Università "Magna Graecia" di Catanzaro, Italy
  • Jonathan Wu, University of Windsor, Canada
  • Yunqing Xia, Tsinghua University, China
  • Erfu Yang, University of Strathclyde, UK

Thursday, 4 December 2014

Call for Papers IJCNN 2015 Special Session "Distributed Learning Algorithms, Techniques, and Implementations for Internet-of-Things"

Updated:  New submission deadline February 5th, 2015.

Aim and Motivation:

Many learning algorithms and techniques were developed with the requirements of single application in mind, such as e-commerce, search, and vision. Internet-of-Things (IoT) presents a great challenge to existing computational intelligence schemes such as machine learning and neural networks due to its data and compute complexity. IoT devices, such as sensors, cameras, and actuators commonly produce distributed, sparse and highly complex data representations that require these intelligent schemes to adapt and adjust, with limited resource availability. The ability to perform learning in distributed manner will enable better resource coordination and utilisation in handling complex computations in such resource-constrained environment.

This special session aims at presenting novel approaches to distributed learning for Internet-of-Things, both from the theoretical and practical perspectives of computational intelligence schemes.

Topics:

This includes but is not restricted to the following topics:
  • Parallel and distributed computational intelligence algorithms and techniques:
  • Deep learning
  • Hierarchical learning
  • Associative memory
  • Sparse distributed memory
  • Machine learning
  • Neural networks
  • Hybrid approaches
  • IoT Analytics
  • Autonomous IoT
  • Resource utilisation and coordination
  • Control and communication systems
  • Intelligent IoT applications and services

Paper Submission:

The format of paper and submission procedure should be followed as specified on the IJCNN 2015 website. Accepted special session papers will be included in the conference proceedings. Submissions to this special session should follow the following instructions:
  1. Go to http://ieee-cis.org/conferences/ijcnn2015/upload.php to submit your paper to IJCNN 2015.
  2. Be sure to set the "Main research topic" to "SS35 - Distributed Learning Algorithms, Techniques, and Implementations for Internet-of-Things".

For more information, please visit IJCNN'2015 CFP website: http://www.ijcnn.org/call-for-papers, or our Special Session CFP: https://sites.google.com/site/ijcnn2015distlearniot/ . All submissions will be peer-reviewed with the same criteria used for other contributed papers.

IMPORTANT DATES

Paper submission UPDATED:  February 5th, 2015
Paper Decision notification: March 15th, 2015 
Camera-ready submission: April 15th, 2015
Conference Dates: July 12 - 17th, 2015

Organizers:

Anang Hudaya Muhamad Amin
Thundercloud Research Lab
Faculty of Information Science and Technology (FIST)
Multimedia University
Melaka, Malaysia
Email: anang.amin@mmu.edu.my

Asad I. Khan
Faculty of Information Technology
Monash University
Clayton, VIC, Australia
Email: Asad.Khan@monash.edu

Benny B. Nasution
Politeknik Negeri Medan
Medan, Indonesia
Email: benny.nasution@polmed.ac.id

Wednesday, 3 December 2014

Call for Papers IJCNN 2015 Special Session "Biologically Inspired Computational Vision"

Introduction:

Constructive understanding of computational principles of visual information processing, perception and cognition is one of the most fundamental challenges of contemporary science. Deeper insight into biological vision helps to advance intelligent systems research to achieve robust performance similar to biological systems. Biological inspiration indicates that sensory processing, perception, and action are intimately linked at various levels in animal vision. Implementing such integrated principles in artificial systems may help us achieve better, faster and more efficient intelligent systems. This session provides an integrated platform to present original ideas, theory, design, and applications of computational vision.

  • Topics of interest include, but are not limited to the following:Theoretical approaches and modeling in computational vision
  • Neuronal mechanisms of visual processing
  • Low level vision and its relationship to biological machinery
  • Artificial learning systems for image and information processing and evidential reasoning for recognition
  • Intelligent search in communications networks
  • Modeling issues in ATM networks, agent-oriented computing architectures
  • Perception of shape, shadows, poses, color and illumination in object recognition
  • Tracking for inferring shapes and 3D motions
  • Active visual perception, attention and robot vision
  • Functional Magnetic Resonance Imaging (fMRI) studies of visual segmentation and perception
  • Application of computational vision in areas of
  • Automated target identification and acquisition systems in defense and industry
  • Biomedical imaging
  • 3D photography
  • Face recognition
  • Learning to segment camouflaged objects
  • Motor actions and robotics
  • Image databases and indexing
  • Hardware implementation of computational vision
  • Any other topics related to biological approaches in computer vision

Organizer:

Khan M. Iftekharuddin, Old Dominion University, USA
Professor and Chair, Department of Electrical and Computer Engineering
http://www.eng.odu.edu/visionlab/

Program Committee:

  • Vijayan Asari, University of Dayton, USA
  • Azzedine Beghdadi, University Paris, 13, France
  • Salim Bouzerdoum, University of Wollongong, Australia
  • Ke Chen, University of Manchester, UK
  • Yoonsuck Choe, Texas A&M University, USA
  • Robert Kozma, University of Memphis, USA
  • Minho Lee, Kyungpook National University, Korea
  • Zicheng Liu, Microsoft Research, USA
  • Bertram Shi, Hong Kong University of Science and Technology, Hong Kong
  • Rufin VanRullen, CNRS, Toulouse, France

Submission instructions:

  1. Go to http://ieee-cis.org/conferences/ijcnn2015/upload.php to submit a paper to IJCNN 2015.
  2. Be sure to set the "Main research topic" to this special session. (The special sessions are found at the bottom of the list.)

Monday, 24 November 2014

Call for Papers IJCNN 2015 Special Session "Models of Cognitive-Emotional Interactions"

Special Session for IEEE IJCNN 2015. Updated submission deadline 5th February 2015

Website: http://ieee-cis.blogspot.com/2014/11/call-for-papers-ijcnn-2015-special_64.html

Scope:

Recent brain imaging studies have highlighted the interrelationship between cognitive and emotional processes.  Emotion helps the cognitive system decide allocations of attention among a large, heterogeneous, and confusing set of stimuli in the environment.  Conversely, satisfaction or dissatisfaction of the drive to understand the environment generates positive or negative emotions.  Hence, it has become increasingly difficult to categorize brain regions as primarily “cognitive” or “emotional.”  Rather, a complex network of interconnected regions including amygdala, hypothalamus, ventral and dorsal striatum, anterior cingulate cortex, insula, and several regions of prefrontal cortex is involved in the interplay of cognition and emotions in human and animal decision making.

Furthermore, a number of researchers have found it advantageous to add emotion-like capabilities to robots and other artificial neural systems.  As the work of Antonio Damasio and many others points out, emotions can be of aid in classifying objects within a robot’s environment.  In particular, emotions like joy and interest can generate approach to specific objects, whereas emotions like fear and disgust can generate avoidance of specific objects.

Topics:

We particularly encourage submissions related to the following non-exhaustive list of topics:
  • Emotional influences on decision making
  • Normal and abnormal affect
  • Cognitive and emotional effects of the psychotherapeutic process
  • Computational psychiatry
  • Differences between primary biological emotions and aesthetic emotions 
  • Emotional robots
  • Emotional representations in artificial neural systems.

Dates and submissions:

The deadlines for submissions, author feedback, etc. are bound to the normal IJCNN 2015 deadlines (and, thus, are also subject to the same changes and extensions).

The current schedule is:
  • Paper submission due UPDATED: February 5, 2015
  • Paper review feedback: March 15, 2015
  • Final papers due: April 15, 2015

For details on the submission process, formats, etc., please refer to the IJCNN 2015 Call for Papers ( http://www.ijcnn.org/call-for-papers ) and the IJCNN 2015 submission guidelines ( http://www.ijcnn.org/paper-submission ).
When submitting to the special session, please make sure to select the corresponding session topic during the submission process.

Session Co-Chairs:

Daniel S. Levine, University of Texas at Arlington (Levine@uta.edu)
Leonid Perlovsky, Northeastern University (lperl@rcn.com)
Abbas Edalat, Imperial College London (a.edalat@imperial.ac.uk)

Call for Papers IJCNN 2015 Special Session "Ensemble Systems and Machine Learning"

Special Session for IEEE IJCNN 2015

Scope

Ensembles are essential tools for classification and prediction tasks in many real-world applications.  The past decade has witnessed a vast growth of the amount of machine learning methods using ensemble, becoming a popular approach due to their capabilities in handling many real world complex problems and to the good results and analysis presented in many recently published papers. Results show that ensembles of classifiers and forecasting models can achieve better accuracy than single ones. However, ensembles face many challenges in obtaining diverse and optimized base models, as well as obtaining good fusion algorithms.

This special session of IJCNN 2015 will cover all aspects of the latest achievements in ensemble systems particularly machine learning based ensembles and their applications.

Topics for submission include, but are not limited to:
  • Ensemble techniques and algorithms
  • Multiple experts
  • Committee of experts
  • Fusion of classifiers
  • Fusion of forecasting models
  • Optimization techniques for multiple classifiers
  • Applications of ensemble and fusion techniques
etc.

Biographic Information:

Marley Vellasco received the BSc and MSc degrees in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil, in 1984 and 1987, respectively, and the PhD degree in Computer Science from the University College London (UCL) in 1992. Dr. Vellasco is currently Head of the Electrical Engineering Department of PUC-Rio and of the Computational Intelligence and Robotics Laboratory (LIRA) of PUC-Rio. She is the author of four books and more than 45 papers in professional journals, 300 papers in conference proceedings and 15 book chapters in the area of soft computing and machine learning. Her research interests include Neural Networks, Fuzzy Logic, Neuro-Fuzzy Systems, Neuro-Evolutionary models, Robotics and Intelligent Agents, applied to decision support systems, pattern classification, time-series forecasting, control, optimization and Data Mining.

Teresa B. Ludermir received the Ph.D. degree in Artificial Neural Networks in 1990 from Imperial College, University of London, UK. She is a Full Professor at the Center of Informatics, Universidade Federal de Pernambuco, Brazil. She has published over a 200 articles in scientific journals and conferences, three books in Neural Networks and organized two of the Brazilian Symposium on Neural Networks. She is one of the editors-in-Chief of the International Journal of Computation Intelligence and Applications. Her main interests are Machine Learning, Meta-Learning, Hybrid Intelligent Systems, and Time Series Forecasting.

Saturday, 22 November 2014

Call for Papers IJCNN 2015 Special Session "Modeling and Forecasting Financial and Commodity Markets by Neural Networks"

Special Session for IEEE IJCNN 2015.Updated submission deadline: 5th February, 2015.

Aim and scope

Behaviors of stock price changes in financial and commodity markets have long been a focus of economic research for a more clear understanding of mechanism and characteristics of markets. In the empirical research, some statistical properties for the market fluctuations are uncovered by the high frequency financial time series, such as fat tails distribution of price changes, power-law of logarithmic returns and volumes, volatility clustering, multifractality of volatility, etc. The applications of neural networks in time series forecasting for financial applications have gained enormous popularity in the recent years. In fact, the analysis of financial time series is of primary importance in the economic world; by using a data driven empirical analysis, the goal is to obtain insights into the dynamics of series and out-of-sample forecasting. If one were able to forecast tomorrow’s returns on an asset with some degree of precision, one could use this information in an investment today.

The aim of this special session, which stems by the excellent success obtained during the 2014 IEEE WCCI Conference held in Beijing, is to promote research and reflect the most recent advances of neural networks, including their hybridization with evolutionary computation, fuzzy systems, metaheuristic techniques and other intelligent methods, in a series of practical problems relevant to the interactions between machine learning and financial modeling and forecasting, the main interest being finalized for searching optimal relationships in the area of financial engineering, energy commodity trading, risk management, portfolio optimization, industrial organization, auctions, searching equilibriums, financial forecasting, market simulation, agent-based computational economics, and many other areas.

Topics

The topics of interest to be covered by this Special Session include, but are not limited to:

  • Financial data mining
  • Time series analysis and forecasting
  • Soft computing applications
  • Dynamics of commodity markets
  • Decision support systems
  • Risk analysis and credit scoring
  • Portfolio management
  • Automated trading systems
  • Agent-based computational economics
  • Economic modeling and finance
  • Stock volatility prediction
  • Investment strategy
  • Artificial economics
  • Simulation of social processes

Important Dates

Paper submission UPDATED: February 5, 2015
Paper decision notification: March 15, 2015
Camera-ready submission: April 15, 2015
Conference days: July 12-17, 2015

Submission

Manuscripts submitted to special sessions should be done through the paper submission website of IJCNN 2015. All papers submitted to special sessions will be subject to the same peer-review procedure as the regular papers. If a sufficient number of papers are accepted to fill a special session, then it will be included in the final program. If not enough papers are accepted for this special session, then the accepted papers will be automatically moved to regular sessions.

The authors intended to contribute to this special session are kindly recommended to follow the manuscript style information and templates of regular IJCNN 2015 papers, as described here.

When submitting their manuscripts, authors are recommended to follow these steps:
  1. select the Special Session ID and Name in the “Main research topic” dropdown list, that is SS29 - Modeling and Forecasting Financial and Commodity Markets by Neural Networks 
  2. fill out the input fields, upload the PDF file and finalize the submission by January 15, 2015.

Special Session Organizer

MASSIMO PANELLA, Ph.D.
Dept. of Information Engineering, Electronics and Telecommunications
University of Rome “La Sapienza”
Via Eudossiana 18, 00184 Rome, Italy
Tel.: +39-0644585496; Skype: m.panella
E-mail: massimo.panella@uniroma1.it
Web: http://massimopanella.site.uniroma1.it
LinkedIn: http://it.linkedin.com/in/massimopanella

Wednesday, 19 November 2014

Call for Papers IJCNN 2015 Special Session "Unsupervised Neural Network Clustering"

Special Session for IEEE IJCNN 2015.

IMPORTANT DATES

Paper submission UPDATED:  February 5, 2015
Paper Decision notification: March 15, 2015
Camera-ready submission: April 15, 2015
Conference: July 12-17, 2015

SCOPE AND MOTIVATION

Generally, unsupervised learning or self-organized learning finds regularities in the data represented by the examples. Clustering methods such as model-based, density based and user guided methods are often applied for data reduction such as summarization like preprocessing of classification; compression like vector quantization; and finding the nearest neighbors. Specifically, a feed-forward neural network is a software version of the human brain and have their roots in Hebbian and competitive learning such as Kohonen’s self-organizing map and growing neural gas. In this network, data processing has only one forward direction from the input layer to the output layer without any cycle or backward movement; and generally exhibits several advantages such as an inherent distributed parallel processing architectures, as well as capabilities to adjust the interconnection weights to learn and describe suitable clusters, process vector quantization prototypes and distribute similar data without class labels to describe the clusters, control noisy data, cluster unknown data, and learn the types of input values on the basis of their weights and properties. The current online dynamic unsupervised feed-forward neural network clustering methods such as evolving self-organizing map and dynamic self-organizing map inherit some of the advantages and disadvantages of static unsupervised feed-forward neural network clustering methods; which are suitable to be applied in different research areas such as email logs, networks, credit card transactions, astronomy and satellite communications. Generally, the critical issues of clustering are data losing, definition of clustering principles, number and Unsupervised clustering is a valuable subject to research, however, their critical issues are data losing, definition of clustering principles, number and densities of clusters. Specially, the main problems in dynamic feed-forward neural network clustering are low speed, high memory usage and memory complexity through using random weights and parameters, and relearning. The goal of this research is an investigation of current unsupervised clustering and identify their limitations and problems through a literature review and experience.

TOPICS

The topics of the special session include, but are not limited to:
  • Learning and Neural Network
  • Unsupervised Feed Forward Neural Network clustering
  • Static Unsupervised Neural Network clustering
  • Dynamic Unsupervised Neural Network clustering
  • Semi-supervised Neural Network clustering

ORGANIZERS

Roya Asadi, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Selangor, 50603, Malaysia, (royaasadi@siswa.um.edu.my).

Sameem Abdul Kareem, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Selangor, 50603, Malaysia. (sameem@um.edu.my).

Shokoofeh Asadi, Department of Agreecultural Management Engineering, Faculty of Ebne-Sina, University of Sciece and Research Branch, Tehran, 930277941, Iran, (shokoofeh.ame@gmail.com).

SUBMISSION

All papers are to be submitted through the conference website: http://www.ijcnn.org/

Call for Papers IJCNN 2015 Special Session "Mind, Brain, and Cognitive Algorithms"

Organizers: Leonid Perlovsky (Harvard University and Northeastern University, MA; lperl@rcn.com), José F. Fontanari, Asim Roy, Angelo Cangelosi, Daniel Levine.

Recent progress opens new directions for modeling the mind and brain and developing cognitive algorithms for engineering applications. Cognitive algorithms solve traditional engineering problems much better than before, and new areas of engineering are opened modeling human abilities in cognition, emotion, language, art, music, cultures. Cognitive dissonances and behavioral economics is another new active area of research. A wealth of data are available about the ways humans perform different cognitive tasks (e.g., scene and object recognition, language acquisition, interaction of cognition and language, music cognition, cognitive dissonance) as well as about the biases involved in human judgment and decision making (e.g., the prospect theory and the fuzzy-trace theory). A wealth of data on the web can be exploited for extracting cognitive data. Explaining these laws and biases using realistic neural networks architectures, including neural modeling fields, as well as more traditional learning algorithms requires a multidisciplinary effort.

The aim of this special session is to provide a forum for the presentation of the latest data, results, and future research directions on the mathematical modeling of higher cognitive functions using neural networks, neural modeling fields, as well as cognitive algorithms exploiting web data and solving traditional and new emerging engineering problems, including genetic association studies, medical applications, Deep Learning, and Big Data.

The special session invites submissions in any of the following areas:
  • Neural network models of higher cognitive function
  • Neural mechanisms of emotions, cognition
  • Embodied cognition modeling
  • Neural modeling fields (NMF) 
  • Perceptual processing
  • Language learning 
  • Cognitive and emotional processing
  • Cognitive models of decision-making
  • Models of emotional mechanisms
  • Models of cognitive dissonances
  • Cognitive, language, and emotional models of cultures
  • Cognitive functions of art, music, and spiritual emotions.
  • Emotions in cognition (affective cognition)
  • Cognitive dissonance, neural models
  • Cognition and cultures
  • Medical applications 
  • Genome association studies
  • Big Data

Keywords:

Cognition, Emotions, Decision-Making, Language Acquisition, Language Emotionality, Cognitive Dissonance, Music Cognition, Models of Cultures, Neural Modeling Fields, ART Neural Network, Fuzzy-Trace Theory, Prospect Theory, Deep Learning, Genome Associations, Big Data   

Program Committee:

M. Cabanac (Canada)
A. Cangelosi (UK)
J. F. Fontanari (Brazil)
R. Illin (USA)
B. Kovalerchuk  (USA)
R. Kozma (USA)
D. Levine (USA)
D. Marocco  (UK)
A. Minai
L. I. Perlovsky (USA)
S. Petrov (USA)
A. Roy
J. Weng

Tuesday, 18 November 2014

Call for Papers IJCNN 2015 Special Session "Big Data in Smart Industry"

Submissions are invited for IJCNN 2015 Special Session on “Big Data in Smart Industry”

Updated submission deadline 5th February, 2015.

Webpage: http://www.cpdee.ufmg.br/~apbraga/Paginas2014/SSIJCNN2015.html

ABSTRACT

Industry is experiencing worldwide the beginning of a new era of innovation and change. Sensors, actuators, supervision and control elements are increasingly endowed with autonomy, flexibility, communication capability and interoperability. The new generation of devices, which is capable of data collection and processing, has been gradually incorporated into several levels of the industrial production chain. The synergy of these physical and computational elements forms the base for a profound transformation of the global industry with a perspective of a dramatic increase of productivity and reliability, and significant benefits for the society. Within this new scenario, the availability of a large amount of high dimensional data reveals itself as a dilemma for the induction of data models. On the one hand, there is an expectation that a greater ability of sampling might improve performance and reliability. Nevertheless, the reality is that most current methods and models are not able to deal with problems of such high dimension and volume. Many current problems involve terabytes of data with hundreds of variables and dimensions that tend to rise continuously if the expected industrial growth rate in the sector is maintained. Prognostics are for an exponential growth of the data storage capacity in the worldwide network of devices during the next years. In addition to the increased internal connectivity of the industry, an improved integration and enhanced synergy with consumer markets and inputs through networking appears to be an inevitable path. Current trend suggests worldwide industry will highly demand data models and processing capabilities to handle time-varying, massive and high dimensional data. This is where Big Data in Smart Industry problems become relevant, and it is crucial that academia and industry are prepared from scientific and technological points of view to face the new challenges.

TOPICS:

We would like to encourage the submission of papers within the general scope of the Special Session (Big data in Smart Industry) in the following topics:
  • Modeling of large datasets
  • Dimensionality reduction of very large datasets
  • Learning from large industrial datasets
  • Data analysis and visualization
  • Online modeling, optimization, and autonomous control of industrial processes
  • Embedded intelligence in cyber-physical systems
  • Computational intelligence for smart energy management
  • Data stream processing for water, transportation, agriculture, and sustainability
  • Internet of things and smart resources management
  • Data-driven optimization and control of dynamical systems
  • Cyber-physical system units (CPSU) with embedded autonomy
  • Data acquisition and storage in distributed industrial environments

ORGANIZERS:

Antônio Pádua Braga
Federal University of Minas Gerais, Brazil
apbraga@ufmg.br
http://www.ppgee.ufmg.br/~apbraga

Fernando Gomide
University of Campinas, Brazil
gomide@dca.fee.unicamp.br
http://www.dca.fee.unicamp.br/~gomide

SUBMISSION & IMPORTANT DATES

Special session papers will undergo the same review process as regular papers and follow the same schedule of the conference. Paper submission should be done directly to IJCNN submissions page found at http://ijcnn.org. Be sure to set the "Main research topic" to your special session. The special sessions are found at the bottom of the list.

Paper submission deadline : February 5, 2015
Paper decision notification: March 15, 2015
Camera-ready submission: April 15, 2015


Monday, 17 November 2014

Call for Papers IJCNN 2015 Special Session "Autonomous Learning from Big Data"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5 February, 2015.

Organizers: 

P. Angelov (www.lancs.ac.uk/staff/angelov)
A. Roy (ASIM.ROY@asu.edu)

The aim of the special session is to present latest results in this fast expanding area of Autonomous Learning Systems and Big Data Analytics and to give a forum to discuss the challenges for the future.
It is organised by the new Special Interest Group on Autonomous Learning Systems and the Section on Big Data Analytics within INNS and by the Technical Committee on Evolving Intelligent Systems, SMC Society, IEEE and aims to be a focal point of the latest research in this emerging area.

One of the important research challenges today is to cope effectively and efficiently with the ever growing amount of data that is being exponentially produced by sensors, Internet activity, nature and society. To deal with this ocean of zeta-bytes of data, data streams and navigate to the small islands of human-interpretable knowledge and information requires new types of analytics approaches and autonomous learning systems and processes.

Traditionally, for decades or even centuries machine learning, AI, cognitive science were developed with the assumption that the data available to test and validate the hypotheses is a small, finite volume and can be processed iteratively and offline. The realities of dynamically evolving big data streams and big data sets (e.g. pentabytes of data from retail industry, high frequency trading, genomics or other areas) become more prominent only during the last decade or so. This poses new challenges and requires new, revolutionary approaches.

Topics of interest 

(include but not limited to):

Methodology

  • 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
  • Kernel methods and statistical learning theory
  • 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.)
  • New Adaptive and Evolving Learning Methods
  • Autonomous Learning Systems
  • Stability, Robustness, Unlearning Effects
  • Structure Flexibility and Robustness in Evolving Systems
  • Evolving in Dynamic Environments
  • Drift and Shift in Data Streams
  • Self-monitoring Evolving Systems
  • Evolving Decision Systems
  • Evolving Perceptions
  • Self-organising Systems
  • Neural Networks with Evolving Structure
  • Non-stationary Time Series Prediction with Evolving Systems
  • Automatic Novelty Detection in Evolving Systems
  • On-Line Identification of Fuzzy Systems
  • Evolving Neuro-fuzzy Systems
  • Evolving Clustering Methods
  • Evolving Fuzzy Rule-based Classifiers
  • Evolving Regression-based Classifiers
  • Evolving Intelligent Systems for Time Series Prediction
  • Evolving Intelligent System State Monitoring and Prognostics Methods
  • Evolving Intelligent Controllers
  • Evolving Fuzzy Decision Support Systems
  • Evolving Probabilistic Models
  • 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

Real-life applications

  • Robotics
  • Defence
  • Intelligent Transport
  • Bio-Informatics
  • Industrial Applications
  • Data Mining and Knowledge Discovery
  • Control Systems
  • Evolving Consumer Behaviour
  • Evolving Activities Recognition
  • Evolving Self-localisation Systems

Dates:

  • Send Title & Abstract to p.angelov@lancaster.ac.uk or ASIM.ROY@asu.edu as soon as possible
  • Deadline for Paper Submission Updated 5 February, 2015
  • Notification of Acceptance 15 March, 2015
  • Final Paper Submission 15 April, 2015

Selected authors will be invited to submit extended papers for a special issue of the Springer journal Evolving Systems

Call for Papers IJCNN 2015 Special Session "Digital Audio Applications"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5 February, 2015.

THEME AND SCOPE

Neural Networks (NN) based techniques, and Computational Intelligence (CI) ones from a wider perspective, are largely used to face complex modelling, prediction, and recognition tasks in different research fields. One of these is represented by Digital Audio, which finds application in contexts like entertainment, security, and health. Scientists and technicians worldwide actively cooperate to develop new services and products, and they typically employ advanced NN and CI techniques, in combination with suitable Digital Signal Processing algorithms.

In particular, this is typically accomplished with the aim of extracting and manipulating useful information from the audio stream to pilot the execution of automatized services, also in an interactive fashion. Several are the Digital Audio topics touched by such a paradigm, involving different kinds of audible signals. In the “music” case study we have the music information retrieval with many diverse sub-topics therein; for “speech” we can mention speech/speaker recognition, speaker diarization, speaker localization; in the case of “sound”, acoustic monitoring and sound detection and identification have lately registered a big interest among the scientists working in the field. Moreover, also cross-domain approaches to exploit the information contained in diverse signals in the acoustic range have been also recently developed. In many applicative contexts, this happens in conjunction with data coming from other media, like textual and visual, for which specific fusion techniques are required. 

In dealing with the problems correlated to these topics, the adoption of data-driven learning systems is often a ``must'', and the recent success encountered by deep neural architectures comes just in confirmation of that. This is not, however, immune to technological issues, due to the presence of non-stationary operating conditions and hard real-time constraints (made often harder by the big amount of data to process).

It is indeed of great interest for the scientific community to understand how and to what extent novel CI based techniques (with special attention to the NN ones) can be efficiently employed in Digital Audio, in the light of all aforementioned aspects. The aim of the session is therefore to focus on the most recent advancements in the CI field and on their applicability to Digital Audio problems.

TOPICS

Potential topics include, but are not limited to:
  • Computational Audio Analysis
  • Deep Learning algorithms in Digital Audio
  • Neural Architectures for Audio Processing
  • Music Information Retrieval
  • Speech/Speaker Analysis and Classification
  • Sound Detection and Identification
  • Acoustic Source Separation
  • Brain inspired auditory scene analysis
  • Cross-domain Audio Analysis
  • Speech and Audio Forensics
  • Audio-based Security Systems
  • Intelligent Audio Interfaces

IMPORTANT DATES

  • UPDATED February 5, 2015: Paper submission deadline
  • March 15, 2015: Notification of paper acceptance
  • April 15, 2015: Camera-ready deadline
  • July 11-16, 2015: Conference days

ORGANIZERS

Stefano Squartini, Università Politecnica delle Marche, Italy, s.squartini@univpm.it      
Aurelio Uncini, Università La Sapienza, Italy, aurel@ieee.org
Björn Schuller, University of Passau, Germany/Imperial College London, UK, schuller@ieee.org
Francesco Piazza, Università Politecnica delle Marche, Italy, f.piazza@univpm.it

http://a3lab.dii.univpm.it/news/ijcnn2015-special-session

Call for Papers IJCNN 2015 Special Session "Intelligence for cyber-physical, embedded and pervasive systems"

Special Session for IEEE IJCNN 2015.

Overview

The emergence of nontrivial embedded units mounting a rich sensor and actuator platform, sensor networks, the Internet of Things (IoT), pervasive and cyber-physical systems has made possible the design of sophisticated applications where large amounts of real-time data are collected to constitute a “big data” picture as time passes. Intelligence, adaptation through learning, neural and neuromorphic systems, cognitive fault tolerance and healing abilities constitute some key mechanisms needed to boost the future generation of intelligent systems and derived applications.

Topics

The special session aims at gathering scholars and practitioners addressing those research topics and applied problems that the interaction of intelligent systems with the real world is requesting.

Papers must present original work, applications or review the state-of-the-art in the following non-exhaustive list of topics:
  • Intelligent devices and solutions for the Internet of things
  • Cognitive Sensor Networks
  • Neural and neuromorphic Systems
  • Intelligent Measurement Systems
  • Incremental learning in cyber-physical and embedded systems
  • Intelligent diagnosis and healing mechanisms 
  • Low level learning mechanisms for time invariant environments
  • Intelligent sensors and robotics
  • Intelligent systems for embedded applications

Relevance of the topic

Although we experience an increasingly pervasive presence of embedded applications in our everyday life, such that by 2020 it is predicted that each person will possess between 5 and 10 embedded systems, the presence of intelligence in current versions of embedded devices is generally very basic, mostly confined to passive adaptation.  Learning and cognitive mechanisms play a key role here to boost the next generation of embedded systems and applications.

Workshop Organizers


Cesare Alippi (Italy)

CESARE ALIPPI received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Full Professor of information processing systems with the Politecnico di Milano. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), USI (CH). Alippi is an IEEE Fellow, Vice-President education of the IEEE Computational Intelligence Society (CIS), Associate editor (AE) of the IEEE Computational Intelligence Magazine, past AE of the IEEE-Trans Instrumentation and Measurements, IEEE-Trans. Neural Networks.

In 2004 he received the IEEE Instrumentation and Measurement Society Young Engineer Award; in 2011 has been awarded Knight of the Order of Merit of the Italian Republic; in 2013 he received the IBM Faculty Award.Current research activity addresses adaptation and learning in non-stationary environments and Intelligent embedded systems. He holds 5 patents and has published one monograph book published by Springer on "Intelligence for embedded systems", 6 edited books and about 200 papers in international journals and conference proceedings.

Manuel Roveri (Italy)

MANUEL ROVERI received the Dr.Eng. degree in Computer Science Engineering from the Politecnico di Milano (Milano, Italy) in June 2003, the MS in Computer Science from the University of Illinois at Chicago (Chicago, Illinois, U.S.A.) in December 2003 and the Ph.D. degree in Computer Engineering from Politecnico di Milano (Milano, Italy) in May 2007.

Currently, he is an assistant professor at the Departent of Electroncis and Information of the Politecnico di Milano. His research interests include intelligent embedded systems, computational intelligence and adaptive algorithms. Manuel Roveri is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems and served as chair and member in many IEEE subcommittees.

Keywords

  • Embedded systems
  • Cyber-physical systems
  • Intelligent applications
  • Intelligent sensors
  • Intelligence for sensor networks
  • Neuromorphic systems
  • Internet of Things
  • Approximate computing

Further information and Paper submission can be found at the conference homepage:  http://www.ijcnn.org/
Please make sure you select the correct Special Session in the submission process.

Call for Papers IJCNN 2015 Special Session "Clustering and Co-clustering"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5 February, 2015.

Description

Everyday, huge amounts of data are generated by users via the web, social networks, etc. Clustering/Coclustering techniques are a tool of choice to help organize the huge collections of data that increasingly beset us. Clustering/Coclustering is an unsupervised learning approach that allows one to discover global structures in the data (i.e. clusters). Given a dataset, it identifies different data subsets which are hopefully meaningful. The discovered clusters are deemed interesting if they are while instances within each (co)cluster share similar features. This (co)clustering problem has motivated a huge body of work and has resulted in a large number of algorithms. (Co)Clustering has thus been used in numerous real-life application domains such as marketing, city planning, and so forth.

Clustering algorithms are a tool of choice to explore these high-dimensional data sets. However numerous questions remain open as:
  • What are the last advances in
    • supervised clustering that combines the main characteristics of both traditional clustering and supervised classification tasks?
    • quality criteria?
    • clustering for big data?
    • evolving clustering?
    • clustering events or time series?

This special session offers a meeting opportunity for academics and industry researchers belonging to the communities of Computational Intelligence, Machine Learning, Experimental Design, and Data Mining to discuss new areas of (co)clustering, One goal of this special session will be two-fold: On the one hand, to look for new algorithms and techniques proposals based on (co)clustering. On the other hand, to look for new application domains, real problems, where the application of (co)clustering have demonstrated an outstanding performance or interpretation abilities against other traditional approaches.

Publication opportunities: Papers should be submitted to IJCNN. We encourage papers that describe new algorithm and applications of (co)clustering in real-world. In the industrial context, the main difficulties met and the original solutions developed, have to be described.

Paper acceptance and publication will be judged on the basis of their quality and relevance to the special session themes, clarity of presentation, originality and accuracy of results and proposed solutions.

The set of proposed topics includes, but is not limited to::
  • Clustering, Coclustering
  • Supervised Clustering (Coclustering)
  • Semi-Supervised Clustering (Coclustering)
  • Quality Criteria for Clustering (Coclustering)
  • Measure of Variable Importance for a Clustering (Coclustering)
  • Automatic tuning of Cluster Number (Cocluster Number)
  • Clustering for Big Data
  • Method to assess the evolution of a Clustering (Coclustering)
  • Constrainted Clustering (Coclustering)
The list of application domain is includes, but it is not limited to:
  • Evolving textual information analysis
  • Evolving social network analysis
  • Dynamic process control and tracking
  • Intrusion and anomaly detection
  • Genomics and DNA micro-array data analysis
  • Adaptive recommender and filtering systems

A list of Applicative domains could be found in
P. Berkhin « Survey of clustering data mining techniques », Accrue Software, San Jose, CA, 2002.

Organizers:

Vincent Lemaire
ORANGE-LABS, Lannion, France
2 avenue Pierre Marzin
22300 Lannion
vincent.lemaire@orange-ftgroup.com
http://www.vincentlemaire-labs.fr

Pascal Cuxac
INIST-CNRS,
Recherche Développement
2 allée du Parc de Brabois
CS 10310
54519 Vandoeuvre les Nancy Cedex
pascal.cuxac@inist.fr,
https://sites.google.com/site/pascalcuxac/

Jean-Charles Lamirel
TALARIS (ex-TALARIS) -LORIA,
Campus Scientifique
BP 239
54506 Vandoeuvre-lès-Nancy
jean-charles.lamirel@loria.fr

Organizing committee 

(tentative, to be confirmed):
  • Abou-Nasr Mahmoud Ford Motor Company USA
  • Al Shehabi Shadi Allepo University Syria
  • Albatineh Ahmed N. Dept of Biostatistics Florida Int. U. Miami USA
  • Alippi Cesare Politecnico di Milano Italia
  • Arredondo Tomas U.T.F.S.M. Valparaíso Chile
  • Bennani Younes LIPN, Paris France
  • Bifet Albert University of Waikato, Hamilton New Zealand
  • Bondu Alexis EDF R&D France
  • Cabanes Guenael LIPN, Paris France
  • Candillier Laurent (Expertiselcandillier.free.fr)
  • Chawla Nitesh Notre Dame University, Indiana USA
  • Chen Chaomei Drexel University, Philadelphia USA
  • Cleuziou Guillaume (LIFO) France
  • Cornuéjols Antoine (AgroParisTech) France
  • Cuxac Pascal INIST-CNRS, Vandoeuvre-les Nancy France
  • Diallo Abdoulaye B. UQAM Montreal Canada
  • El Haddadi Anass IRIT France
  • Escalante Hugo Jair Mexico
  • García-Rodríguez José University of Alicante Spain
  • Glanzel Wolfgang KU Leuven, Leuven Belgia
  • Guigoures Romain (Zalando - German)
  • Grozavu Nistor LIPN, Paris France
  • Hammer Barbara University of Bielefeld Germany
  • Kumova Bora I. Izmir University Turkey
  • Kuntz-Cosperec Pascale Polytech'Nantes France
  • Labroche Nicolas (Université de Tours) France
  • Lallich Stephane University of Lyon 2 France
  • Lamirel Jean-Charles TALARIS- LORIA, Nancy France
  • Lebbah Mustapha LIPN, Paris France
  • Lemaire Vincent Orange Labs, Lannion France
  • Lenca Philippe Telecom Bretagne France
  • Li Bin UTS, Sydney Australia
  • Nuggent Rebecca Carnegie Mellon University, Pittsburgh USA
  • Popescu Florin Fraunhofer Institute, Berlin Germany
  • Roveri Manuel Politecnicodi Milano Italia
  • Sublemontier Jacques-henri (CEALIST), France
  • Tamir Dan Texas State University, San Marcos USA
  • Torre Fabien University of Lille3 France
  • Urvoy Tanguy Orange Labs, Lannion France
  • Vrain Christel (LIFO) France
  • Zhou Zhi-Hua Nanjing University China
  • Zhu Xingquan Florida Atlantic University USA

Important dates:

  • Paper submission UPDATED: February 5, 2015
  • Paper decision notification: March 15, 2015
  • Camera-ready submission: April 15, 2015

Organizers:

Vincent Lemaire (Orange Labs, France, vincent.lemaire@orange.com) was born in 1968 and he obtained his undergraduate degree from the University of Paris 12 in signal processing and was in the same period an Electronic Teacher. He obtained a PhD in Computer Science from the University of Paris 6 in 1999. He thereafter joined the R&D Division of France Telecom where he became a senior expert in data-mining. His research interests are the application of machine learning in various areas for telecommunication companies with an actual main application in data mining for business intelligence. He developed exploratory data analysis and classification interpretation tools. Active learning and data-space exploration are now his main research interests. He obtained his Research Accreditation (HDR) in Computer Science from the University of Paris-Sud 11 (Orsay) in 2008.

Previous workshop or special session organizations:
  • Atelier - Clustering and Co-clustering (CluCo) at EGC 2015
  • Incremental classification, concept drift and novelty detection (IClaNov) - ICDM 2014 
  • Workshop of the Discovery Challenge - ECML 2014 
  • Incremental learning and novelty detection methods and their applications - ESANN 2014 
  • Atelier - Clustering and Co-clustering (CluCo) at EGC 2014 
  • Incremental clustering, concept drift and novelty detection (IClaNov) - ICDM 2013 
  • Active Learning and Experimental Design (ALED) - IJCNN 2013 
  • Incremental classification and novelty detection (CIDN) - EGC 2013 
  • Active Learning in Real-World Applications - (ALRA) - ECML 2012 
  • Active and Incremental Learning (AIL) - ECAI 2012 
  • Incremental classification and novelty detection (CIDN) - EGC 2012 
  • Active, Incremental and Autonomous Learning: Algorithms and Applications - IJCNN 2012 
  • Workshop on Unsupervised and Transfer Learning (UTL) - ICML 2011 
  • Autonomous and Incremental Learning (AIL) - IJCNN 2011 
  • Active and Autonomous Learning (AAL) - IJCNN 2010 
  • Fast scoring on a Large Database - KDD 2009

Jean-Charles Lamirel (LORIA - INRIA, France, lamirel@loria.fr) is Lecturer since 1997. He obtained his PhD in Computer Science in 1995 and his Research Accreditation in the same domain in 2010. He is currently teaching Information Science and Computer Science at the University of Strasbourg and achieving his research at the INRIA laboratory of Nancy. He was a research member of the INRIA-CORTEX project whose scope is Neural Networks and Biological Systems. He has recently integrated the INRIA-TALARIS project whose main concern is automatic language and text processing. Jean-Charles Lamirel main domains of research are textual data mining based on neural networks, multiple viewpoints data analysis paradigms, data mining auto-evaluation methods and evolving data mining. He is board member of the international Webometrics journal: "Collnet Journal of Scientometrics and Information Management" and was taking part in the committee of ICTAI 2011-2012 and WSOM 2012 conference. He is member of the IEE task force on "Evaluation and quality issues in data mining" within the Data Mining Technical Committee and committee member of the corresponding PAKDD-QIMIE 2013 workshop.

Previous workshop or special session organizations:
  • Clustering and co-clustering – CluCo - workshop EGC 2014
  • Incremental clustering, concept drift and novelty detection (IClaNov), Workshop ICDM 2013
  • Incremental classification and novelty detection - CIDN - workshop EGC 2013
  • Intelligent analysis of time varying information and concept drift management – IEA/AIE 2012
  • Incremental classification and novelty detection - CIDN 2012
  • Incremental clustering and novelty detection techniques and their application to intelligent analysis of time varying information - IEA/AIE 2011
  • Incremental clustering and novelty detection - CIDN 2011
  • International Conference on Webometrics, Informetrics and Scientometrics & 7th COLLNET Meeting in conjunction with the Extra Session on Information Visualization for Webometrics, Informetrics and Scientometrics, Nancy, France, 10-12 May, 2006

Pascal Cuxac (INIST - CNRS, France pascal.cuxac@inist.fr) is Research Engineer at the INIST/CNRS (Institute for Scientific & Technical Information / National Center for Scientific Research) in Nancy, France. He obtained his PhD in Geological and Mining Engineering from the Nancy School of Geology in 1991 and he was working on mechanical behavior of anisotropic rock. In 1993, he joined the CNRS as Research Engineer. Currently, in INIST Research & Development Engineering Service, he takes part in a research program on classification methods for bibliographic corpora, in particular in the development of an incremental unsupervised clustering algorithm.

Previous workshop or special session organizations:
  • Clustering and co-clustering – CluCo - workshop EGC 2014
  • Incremental clustering, concept drift and novelty detection (IClaNov), Workshop ICDM 2013
  • Incremental classification and novelty detection - CIDN - workshop EGC 2013
  • Incremental classification and novelty detection - CIDN 2012
  • Incremental clustering and novelty detection techniques and their application to intelligent analysis of time varying information - IEA/AIE 2011
  • Incremental clustering and novelty detection - CIDN 2011

Call for Papers IJCNN 2015 Special Session "Nature Inspired Deep Learning"

Special Session for IEEE IJCNN 2015.

IMPORTANT DATES

Paper submission UPDATED:  February 5, 2015
Paper Decision notification: March 15, 2015
Camera-ready submission: April 15, 2015
Conference: July 12-17, 2015

AIM

Deep learning has recently emerged as a prominent CI discipline. However, applications of the nature-inspired methods such as particle swarm optimisation and evolutionary optimisation to deep learning are still very limited. Training deep neural networks is a challenging task due to the inherent high dimensionality. The aim of this special session is to discuss the existing nature-inspired approaches to deep learning, to identify problems that arise, and to encourage research in this new and exciting field of computational intelligence.

SCOPE

The topics of the special session include, but are not limited to:
  • Applications of evolutionary algorithms to deep learning
  • Applications of swarm-based algorithms to deep learning
  • Hybrid approaches to deep learning
  • Theoretical and empirical analysis of the nature-inspired deep learning algorithms
  • Identifying and understanding the limitations of nature-inspired deep learning
  • Real-world applications of nature-inspired deep learning
  • Training Restricted Boltzmann Machines with nature-inspired algorithms
  • Training Deep Belief Networks with nature-inspired algorithms
  • Training autoencoders and stacked autoencoders with nature-inspired algorithms
  • Training convolutional neural networks with nature-inspired algorithms
  • Weight pretraining with nature-inspired algorithms
  • High-performance implementations of nature-inspired deep learning
  • Analysis of overfitting and generalization of deep networks training using nature-inspired algorithms
  • Training of deep networks in dynamic environments

ORGANIZERS

Prof Andries Engelbrecht, Department of Computer Science, University of Pretoria, Pretoria, South Africa (engel@cs.up.ac.za)
Ms Anna Rakitianskaia, Department of Computer Science, University of Pretoria, Pretoria, South Africa (annar@cs.up.ac.za)

SUBMISSION

All papers are to be submitted through the conference website: http://www.ijcnn.org/

More details on the special session can be found at: https://sites.google.com/site/ijcnn2015deepnature/

Call for Papers IJCNN 2015 Special Session "Autonomous Machine Learning for Cyber-Physical Systems"

Special Session for IEEE IJCNN 2015.

Updated submission deadline 5th February, 2015.

Scope and Motivation

Recent development in ICT and sensor devices brings us a new form of intelligent systems called Cyber-Physical System (CPS). In CPS, physical entities such as humans, robots, cars, factories, houses interact and communicate with other entities in both physical- and cyber-worlds. The information processed in cyber-physical worlds are video images, voice/sounds, texts (e.g. documents, tweets, e-mails), control signals, sensor data, etc., and such data are continuously generated as “big stream data”. In general, such data are composed not only of explicit information on physical entities (e.g. location, translation, acceleration), but also of implicit information such as health conditions, emotion, and behaviors, which should be extracted from original sensor data. To acquire knowledge from the latter type of implicit information, autonomous machine learning and data mining methods that can learn from high-dimensional stream data are solicited for CPS.

The purpose of this special session is to share new ideas to develop autonomous machine learning and data mining methods for big stream data that are generated not only by connecting cyber-physical worlds but also within either of cyber- and physical- worlds.

Topics

A wide range of autonomous machine learning/data mining methods and applications for cyber-physical systems is covered, including but not limited to the followings:
Theoretical approaches to machine learning /data mining methods for cyber-physical systems
  • Supervised/Unsupervised Learning
  • Online/Incremental Learning
  • Online Feature Selection/Extraction
  • Online Clustering
  • Active Learning
  • Stream Data Mining
  • Text Mining
  • Time-Series Analysis

Applications of cyber-physical systems such as
  • Human-Robot Interactions
  • Smart Life Technologies (Smart Grids, Smart City, Smart Home, Smart Car, Smart Agriculture, etc.)
  • Social Network Analysis (e.g. sentimental analysis, user profiling, etc.)
  • Cybersecurity
  • Opinion Mining
  • Emotion/Behavior Mining
  • Person Attitude Mining
  • Realty Mining

Important Dates

  • February 5, 2015: Paper submission deadline
  • March 15, 2015: Notification of paper acceptance
  • April 15, 2015: Camera-ready deadline
  • July 12-17, 2015: Conference days

Submission

  1. Manuscripts submitted to special sessions should be done through the paper submission website of IJCNN 2015 as regular submissions. Please follow the instructions below:Go to http://ieee-cis.org/conferences/ijcnn2015/upload.php to submit a paper to IJCNN 2015.
  2. Be sure to set the "Main research topic" to “SS32: Autonomous Machine Learning for Cyber-Physical Systems”. (The special sessions are found at the bottom of the list.)
All papers submitted to special sessions will be subject to the same peer-review review procedure as the regular papers. Accepted papers will be part of the regular conference proceedings.

For more information, please contact the Special Session organizers:

Call for Papers IJCNN 2015 Special Session "CONCEPT DRIFT, DOMAIN ADAPTATION & LEARNING IN DYNAMIC ENVIRONMENTS"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5th February, 2015.

One of the fundamental goals in computational intelligence is to achieve brain-like intelligence, a remarkable property of which is the ability to incrementally learn from noisy and incomplete data, and ability to adapt to changing environments. The special session aims at presenting novel approaches to incremental learning and adaptation to dynamic environments both from the more traditional and theoretical perspective of computational intelligence and from the more practical and application-oriented one.

This Special Session aspires at building a bridge between academic and industrial research, providing a forum for researchers in this area to exchange new ideas with each other, as well as with the rest of the neural network & computational intelligence community.

The Special Session will be held within the IEEE International Joint Conference on Neural Networks (IJCNN 2015), in Killarney, Ireland, on July 12-17, 2015 http://ijcnn.org/

Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:
  • Architectures, techniques and algorithms for learning in non-stationary/dynamic environments
  • Domain adaptation, dataset shift, covariance shift
  • Incremental learning, lifelong learning, cumulative learning
  • Change-detection tests and anomaly-detection algorithms
  • Mining from streams of data
  • Applications that call for incremental learning or learning in non-stationary/dynamic environments, such as:
    • Adaptive classifiers for concept drift and recurring concepts
    • Intelligent systems operating in non-stationary/dynamic environments
    • Intelligent embedded and cyber-physical systems
  • Applications that call for change and anomaly detection, such as:
    • fault detection
    • fraud detection
    • network intrusion and security
    • intelligent sensor networks
  • Cognitive-inspired approaches to adaptation and learning
  • Development of test-sets benchmarks for evaluating algorithms learning in non-stationary/dynamic environments
  • Issues relevant to above mentioned or related fields

Keywords: 

Concept drift, nonstationary environment, change/anomaly detection, domain adaptation, incremental learning, data streams. 

Important dates

Paper Submission: February 5th, 2015
Notifications to Authors: March 15th, 2015
Final Submission: April 15th, 2015
Conference Dates: July 12 - 17th, 2015

Paper Submission

Submissions should contain original, high quality, not submitted or published elsewhere work. Papers will be included in the IEEE IJCNN 2015 proceedings.

Information concerning paper submission and manuscript preparation can be found at the IEEE IJCNN 2015 webpage http://ijcnn.org/ for paper submission .

Please make sure you select the the Special Session 12 "Concept Drift, Domain Adaptation & Learning in Dynamic Environments" from the "S. SPECIAL SESSION TOPICS" name in the "Main Research topic" dropdown list during the submission process.

Organizers

Giacomo Boracchi, Politecnico di Milano, Italy

Giacomo Boracchi received the M.S. degree in Mathematics from the Universit`a Statale degli Studi di Milano, Italy, and the Ph.D. degree in Information Technology at Politecnico di Milano, Italy, in 2004 and 2008, respectively. He was researcher at Tampere International Center for Signal Processing, Finland, during 2004-2005.

Currently, he is an assistant professor at the Dipartimento di Elettronica, Informazione e Bioingegneria of the Politecnico di Milano. His main research interests are design for embedded systems for nonstationary environments as well as mathematical and statistical methods for signal and image processing. He was a co-chair of 2014 IEEE Symposium on Intelligent Embedded Systems (IES 2014), part of the IEEE Symposium Series on Computational Intelligence.

Robi Polikar, Rowan University, NJ (USA).

is a Professor of Electrical and Computer Engineering at Rowan University, in Glassboro, NJ. He has received his B.Sc. degree in electronics and communications engineering from Istanbul Technical University, Istanbul, Turkey in 1993, and his M.Sc and Ph.D. degrees, both co-majors in electrical engineering and biomedical engineering, from Iowa State University, Ames, IA in 1995 and 2000, respectively.

His current research interests within computational intelligence include ensemble systems, incremental and nonstationary learning, and various applications of pattern recognition in bioinformatics and biomedical engineering. He is a member of IEEE, ASEE, Tau Beta Pi and Eta Kappa Nu. His current and prior works are funded primarily through NSF's CAREER and Energy, Power and Adaptive Systems (EPAS) programs. He has been heavily involved with IJCNN for over a decade with many special sessions, as well as serving as part of organizing committee. He is also an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems.

Manuel Roveri, Politecnico di Milano, Italy

received the Dr.Eng. degree in Computer Science Engineering from the Politecnico di Milano (Milano, Italy) in June 2003, the MS in Computer Science from the University of Illinois at Chicago (Chicago, Illinois, U.S.A.) in December 2003 and the Ph.D. degree in Computer Engineering from Politecnico di Milano (Milano, Italy) in May 2007.

Currently, he is an assistant professor at the Departent of Electroncis and Information of the Politecnico di Milano. His research interests include intelligent embedded systems, computational intelligence and adaptive algorithms. Manuel Roveri is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems and served as chair and member in many IEEE subcommittees.

Technical Program Committee

  • Cesare Alippi, Politecnico Milano, Italy
  • Alfred Bifet, University of Waikato, New Zealand
  • Gianluca Bontempi, Université Libre de Bruxelles, Belgium
  • Gregory Ditzler, Drexel University, PA, USA
  • Yaochu Jin, University of Surrey, England, UK
  • Georg Krempl, University Magdeburg, Germany
  • Ludmilla Kuncheva, University of Bangor, Wales, UK
  • Leandro L. Minku, University of Birmingham, England, UK
  • Harris Papadopoulos, Frederick University, Cyprus
  • Leszek Rutkowski, Czestochowa University of Technology, Poland
  • Marley Vellasco, Pontifícia Universidade Católica do Rio de Janeiro, Brasil
  • Shengxiang Yang, Brunel University, England, UK

Call for Papers IJCNN 2015 Special Session "Scalable Machine Learning for Data Stream (SML-DS)"

Special Session for IEEE IJCNN 2015.

Updated submission deadline: 5th February, 2015.

Objectives

In many fields, such as multimedia, insurance information systems, bio-bioinformatics, and with advances in data collection and storage technologies have allowed companies to accumulate and to acquire vast amounts of data (Terabyte, Petabyte, and sometimes Zettabyte). In many cases the data may arrive very rapidly in streaming. A data stream is often presented as an ordered sequence of data that in many applications can be read only once or a small number of times using limited computing and storage capabilities. Recent trends in hardware have brought new challenges to the programming and machine learning community and multi-core systems. Data explosion involves that machine learning algorithms are adapted using the new parallelism paradigm as “MapReduce”. Somme researches have proposed incremental, collaborative and online learning methods making it possible to deal with massive data (big data). This requires a process capable of dealing data continuously with restrictions of memory and time.

This special session offers a meeting opportunity for academic and industry researchers in the fields of machine learning, neural network, data visualization, and Big Data to discuss new areas of learning methods and experimental design. We encourage researchers and practitioners to submit papers describing original research addressing data stream and scalable machine learning challenges.

Topics

This includes but is not restricted to the following topics:
  • Clustering, classification from data streams
  • Neural networks approaches
  • Online learning
  • Method of detecting changes in evolving data
  • Applications of detecting changes of evolving data
  • Clustering and classification of data of changing distributions.
  • Visualization of data streams and stream mining results.
  • Theoretical frameworks for stream mining.
  • Scalability of data stream mining systems
  • Interactive stream mining techniques
  • Distributed ensemble classifier
  • Distributed neural networks
  • Parallel and distributed computational intelligence
  • Future research challenges of data stream mining

Important Dates

  • Paper submission UPDATED: February 5, 2015
  • Paper decision notification: March 15, 2015
  • Camera-ready submission: April 15, 2015

Guidelines for authors

Please use IEEE template http://www.ieee.org/conferences_events/conferences/publishing/templates.html for the paper. For more information about submission procedure, please visit: http://www.ijcnn.org/

Session chairs

Nhat-Quang Doan (University of Science and Technology of Hanoi, Vietnam) - doan-nhat.quang@usth.edu.vn
Hanane Azzag (University of Paris-Nord, France) - hanene.azzag@lipn.univ-paris13.fr
Mustapha Lebbah (University of Paris-Nord, France) - mustapha.lebbah@lipn.univ-paris13.fr