ICAI 2015 - International Conference on Artificial Intelligence

The conference is concerned with topics in relation to Artificial Intelligence, Fuzzy logic and soft computing, Software tools for AI, Decision support systems, Brain models / cognitive science, Neural networks and applications, AI and evolutionary algorithms, Machine Learning Techniques, Computational Intelligence, Computational Neuroscience.
EDAS submission link : https://edas.info/newPaper.php?c=18223&track=61035 Conference Topics of interest include, but are not limited to:
AI and evolutionary algorithms
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, …)
Automated problem solving
Brain models / cognitive science
Constraint-based reasoning and constraint programming
Decision support systems
Distributed AI algorithms and techniques
Distributed AI systems and architectures
Emerging technologies
Evaluation of AI tools
Expert systems
Fuzzy logic and soft computing
Heuristic searching methods
Integration of AI with other technologies
Intelligent agents
Intelligent data mining and farming
Intelligent databases
Intelligent information fusion
Intelligent information systems
Intelligent networks
Intelligent tutoring systems
Intelligent user interface
Intelligent web-based business
Knowledge acquisition
Knowledge discovery
Knowledge networks and management
Knowledge representation
Knowledge-intensive problem solving techniques
Languages and programming techniques for AI
Learning and adaptive sensor fusion
Multisensor data fusion using neural and fuzzy techniques
Natural language processing
Neural networks and applications
Reasoning strategies
Search and meta-heuristics
Social impact of AI
Social intelligence (markets and computational societies)
Software tools for AI
Swarm Optimization
Topics on satisfiability
Machine Learning
General Machine Learning Theory
Statistical learning theory
Unsupervised and Supervised Learning
Hierarchical learning models
Relational learning models
Stochastic optimization
Simulated annealing
Heuristic optimization techniques
Neural networks
Reinforcement learning
Multi-criteria reinforcement learning
Multiple hypothesis testing
Decision making
Markov chain Monte Carlo (MCMC) methods
Graphical models
Gaussian graphical models
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-based semi-supervised learning
Graph clustering
Graph learning based on graph transformations and grammars
Graph learning based on graph matching
Information-theoretical approaches to graphs
Motif search
Network inference
General issues in graph and tree mining
Computational Finance
Computational Intelligence
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
Intelligent linguistic
Aspects of text technology
Computational vision
Bioinformatics and computational biology
Biostatistics
Biological network analysis
protein-protein networks, signaling networks, metabolic networks, transcriptional regulatory networks. systems biology
Graph-based models in biostatistics
Computational Neuroscience
Computational Chemistry
Computational Statistics
Algebraic Biology