Topics of Interest

Contributed papers are solicited describing original works in Data Mining, Neural Networks and Deep Learning. Topics and technical areas of interest include but are not limited to the following:

Data Mining:
Data processing
Image data mining
Agent-based data mining
Human, domain, organizational
and social factors in data mining
Scalable data preprocessing
Parallel and distributed data mining algorithms
Methodologies on large-scale data mining
High performance data mining algorithms
Novel models and algorithms
OLAP and data mining
Opinion mining and sentiment analysis
Parallel, distributed,
and cloud-based high performance data mining
Privacy preserving data mining
Statistical methods for data mining
Visual data mining
Feature extraction and selection
Competitive analysis of mining algorithms
Data mining systems in finance and e-commerce
Text data mining
Web mining
Video data mining
Multimedia data mining
	
Neural Networks:
Convolutional neural networks
Hebbian theory
Long short term memory
Residual neural networks
Self-organizing feature maps
Biological neural networks
Cellular neural networks
Feedforward neural networks
Extreme learning machines
Multilayer perceptrons
Graph neural networks
Multi-layer neural network
Neural network hardware
Radial basis function networks
Recurrent neural networks
Hopfield neural networks
	
Deep Learning:
Neuro-Fuzzy Algorithms
Evolutionary Methods
Convolutional Neural Networks (CNN)
Deep Hierarchical Networks (DHN)
Unsupervised Feature Learning
Deep Boltzmann Machines
Generative Adversarial Networks (GAN)
Deep Metric Learning Methods
Deep Reinforcement Learning
Learning Deep Generative Models
Graph Representation Learning
Active learning
Agent-based learning
Manifold learning
Multi-task learning
Statistical models and learning
Computational learning
Evolutionary algorithms and learning
Fuzzy logic-based learning
Parallel and distributed learning
Deep Learning for Computing and Network Platforms