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ICAI'16 - The 18th International Conference on Artificial Intelligence

Featured Keynotes

Opening Remarks

Featured Tutorials

Call for Papers

You are invited to submit a paper for consideration. All accepted papers will be published in printed conference books/proceedings (each with a unique international ISBN number) and will also be made available online. The proceedings will be indexed in science citation databases that track citation frequency/data. In addition, like prior years, extended versions of selected papers (about 40%) will appear in journals and edited research books; publishers include, Springer, Elsevier, BMC, and others). See the web link below for a small subset of such publications: (some of these books and journal special issues have already received the top 25% downloads in their respective fields and/or identified as "Highly Accessed" by publishers and/or science citation index trackers.) Click Here for more details

The conference is composed of a number of tracks, tutorials, sessions, workshops, poster and panel discussions; all will be held simultaneously, same location and dates: July 25-28, 2016. 

Prologue: Artificial Intelligence (AI) is the science and engineering of making intelligent machines and systems. This is an important multi-disciplinary field which is now an essential part of technology industry, providing the heavy lifting for many of the most challenging problems in computer science. Since Machine Learning has strong ties with AI, the conference also covers the field of Machine Learning. The list of topics below is by no means meant to be exhaustive.

  • Artificial Intelligence:

- 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:

- 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
- 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
- Information-theoretical approaches to graphs
- Motif search
- Network inference
- Aspects of knowledge structures
- Computational Intelligence
- Knowledge acquisition and discovery techniques
- Induction of document grammars
- 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
- Biostatistics
- High-throughput data analysis
- Computational Neuroscience
- Computational Statistics



Important Dates
« October 2016 »

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