- Mathematical and theoretical methods:
- Neural dynamics, complex systems, and chaos.
- RBF structures.
- Self-organizing networks.
- Independent Component Analysis (ICA); etc.
- Support vector machines and kernel methods.
- Neurocomputational formulations and models:
- Computational neuroscience.
- Neurons and networks modeling; etc.
- Learning and adaptation:
- Bayesian models and statistical machine learning methods.
- Supervised, unsupervised, and reinforcement algorithms.
- Adaptive systems and reconfigurable systems; etc.
- Connectionist methods in cognitive functions:
- Reasoning, decision making, planning.
- Consciousness and emotions.
- Emergent intelligence.
- Perceptual and motor function (visual, auditory, tactile, virtual reality, etc.).
- Cognitive and neural robotics.
- Hybrid systems:
- Neuro-fuzzy.
- Neuro-evolutionary systems.
- Neuro-swarm.
- Neural dynamic logic and other methods; etc.
- Neuro-technologies and neuro-engineering:
- Microelectronics for neural and hybrid systems.
- Neuromorphic engineering.
- Neural prostheses.
- Nanocognitive systems; etc.
- Applications of Artificial Neural Networks:
- Data analysis and pre-processing.
- Data mining and knowledge discovery.
- Signal processing and speech analysis.
- Image processing and machine vision.
- System identification, process control, and manufacturing.
- Time series analysis and prediction.
- Human-centric computer interfaces and adaptive interfaces (BCI, etc.).
- Ambient Intelligence (AI) and Ambient assisting living (AAL).
- Neuroinformatics and bioinformatics.
- Web applications.
- Robotics.
- Other real world applications of ANN.
- Computational Intelligence on Earth and Environmental Sciences
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