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NAVIGATION
Call for Papers
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LATE BREAKING PAPERS

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Important Dates
July 21-24, 2014
The WORLDCOMP'14
(including all affiliated conferences)


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Scope

Last modified 2014-02-03 20:57

Topics of interest include, but are not limited to, the following:

    • Brain models / Brain mapping / Cognitive science
    • Natural language processing
    • Fuzzy logic and soft computing
    • Software tools for AI
    • Expert systems
    • Decision support systems
    • Automated problem solving
    • Knowledge discovery
    • Knowledge representation
    • Knowledge acquisition
    • Knowledge-intensive problem solving techniques
    • Knowledge networks and management
    • Intelligent information systems
    • Intelligent data mining and farming
    • Intelligent web-based business
    • Intelligent agents
    • Intelligent networks
    • Intelligent databases
    • Intelligent user interface
    • AI and evolutionary algorithms
    • Intelligent tutoring systems
    • Reasoning strategies
    • Distributed AI algorithms and techniques
    • Distributed AI systems and architectures
    • Neural networks and applications
    • Heuristic searching methods
    • Languages and programming techniques for AI
    • Constraint-based reasoning and constraint programming
    • Intelligent information fusion
    • Learning and adaptive sensor fusion
    • Search and meta-heuristics
    • Multisensor data fusion using neural and fuzzy techniques
    • Integration of AI with other technologies
    • Evaluation of AI tools
    • Social intelligence (markets and computational societies)
    • Social impact of AI
    • Emerging technologies
    • Applications (including: computer vision, signal processing, military, surveillance, robotics, medicine, pattern recognition, face recognition, finger print recognition, finance and marketing, stock market, education, emerging applications, ...)
    • Machine Learning; Models, Technologies and Applications -

      >> General Machine Learning Theory Statistical learning theory:
        - Unsupervised and Supervised Learning
        - Multivariate analysis
        - Hierarchical learning models
        - Relational learning models
        - Bayesian methods
        - Meta learning
        - Stochastic optimization
        - Simulated annealing
        - Heuristic optimization techniques
        - Neural networks
        - Reinforcement learning
        - Multi-criteria reinforcement learning
        - General Learning models
        - Multiple hypothesis testing
        - Decision making
        - Markov chain Monte Carlo (MCMC) methods
        - Non-parametric methods
        - Graphical models
        - Gaussian graphical models
        - Bayesian networks
        - Particle filter
        - Cross-Entropy method
        - Ant colony optimization
        - Time series prediction
        - Fuzzy logic and learning
        - Inductive learning and applications
        - Grammatical inference

      >> General Graph-based Machine Learning Techniques:
        - Graph kernel and graph distance methods
        - Graph-based semi-supervised learning
        - Graph clustering
        - Graph learning based on graph transformations
        - Graph learning based on graph grammars
        - Graph learning based on graph matching
        - General theoretical aspects of graph learning
        - Statistical modeling of graphs
        - Information-theoretical approaches to graphs
        - Motif search
        - Network inference
        - General issues in graph and tree mining

      >> Machine Learning Applications:
        - Aspects of knowledge structures
        - Computational Finance
        - Computational Intelligence
        - Knowledge acquisition and discovery techniques
        - Induction of document grammars
        - Supervised and unsupervised classification of web data
        - General Structure-based approaches in information retrieval, web authoring, information extraction, and web content mining
        - Latent semantic analysis
        - Aspects of natural language processing
        - Intelligent linguistic
        - Aspects of text technology
        - Computational vision
        - Bioinformatics and computational biology
        - Biostatistics
        - High-throughput data analysis
        - Biological network analysis: protein-protein networks, signaling networks, metabolic networks, transcriptional regulatory networks
        - Graph-based models in biostatistics
        - Computational Neuroscience
        - Computational Chemistry
        - Computational Statistics
        - Systems Biology
        - Algebraic Biology
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