International Conference on Artificial Neural Networks and Machine Learning ICANNML on October 24-25, 2024 in Houston, United States

Neural network theory & models
Computational neuroscience
Neuronal automata
Connectionist cognitive science
Pattern recognition
Neuroeconomics
Neurofinance Graphical network models
Brain machine interfaces
Evolutionary neural networks
Neurodynamics
Complex systems
Neuroinformatics
Neuroengineering
Hybrid systems
Computational biology
Neural hardware
Bioinspired embedded systems
Collective intelligence
Self-orgnization
Embodied robotics
Learning algorithms
Neural data analysis
Cognitive models
Cellular Neural Networks

statistical learning
neural network learning
learning through fuzzy logic
learning through evolution (evolutionary algorithms)
reinforcement learning
multistrategy learning
cooperative learning
planning and learning
multi-agent learning
online and incremental learning
scalability of learning algorithms
inductive learning
inductive logic programming
Bayesian networks
support vector machines
case-based reasoning
evolutionary computation
machine learning and natural language processing
multi-lingual knowledge acquisition and representation
grammatical inference
knowledge acquisition and learning
knowledge discovery in databases
knowledge intensive learning
knowledge representation and reasoning
machine learning and information retrieval
machine learning for bioinformatics and computational biology
machine learning for web navigation and mining
learning through mobile data mining
text and multimedia mining through machine learning
distributed and parallel learning algorithms and applications
feature extraction and classification
theories and models for plausible reasoning
computational learning theory
cognitive modeling
hybrid learning algorithms
machine learning in:
game playing and problem solving
intelligent virtual environments
industrial and engineering applications
homeland security applications
medicine, bioinformatics and systems biology
economics, business and forecasting applications