ICAI 2011 - ICAI'11 - The 2011 International Conference on Artificial Intelligence

Website www.world-academy-of-science.org/ | Edit Freely

Category ICAI 2011

Deadline: March 10, 2011 | Date: July 18, 2011-July 20, 2011

Venue/Country: Nevada, U.S.A

Updated: 2010-12-12 22:54:52 (GMT+9)

Call For Papers - CFP

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

Brain models / 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, ...)

Workshop on 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