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July 21-24, 2014
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ICAI'14 - The 2014 International Conference on Artificial Intelligence

Last modified 2014-02-03 20:55

ICAI'14 is the 16th annual conference




    You are invited to submit a paper for consideration. All accepted papers will be published in printed conference books/proceedings (ISBN) and will also be made available online. The proceedings will be indexed in science citation databases that track citation frequency/data for each paper. In addition, like prior years, extended versions of selected papers (about 35%) will appear in journals and edited research books (publishers include: Springer, Elsevier, BMC, and others). See the following links for a small subset of the publications based on the Congress: (some of these books and special issues have already received the top 25% downloads in their respective fields - we also have a number of Elsevier and Springer books in the pipeline based on the last offering of the congress):

    ICAI'14 is composed of a number of tracks, including: tutorials, sessions, workshops, posters, and panel discussions. The conference will be held July 21-24, 2014, Las Vegas, USA.

    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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