Machine Learning Problems:
Classification, regression, recognition, and prediction; Problem solving and planning;
Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval;
Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control;
Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.
Machine Learning Methods:
Supervised and unsupervised learning methods (including learning decision and regression trees, rules,
connectionist networks, probabilistic networks and other statistical models, inductive logic programming,
case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods;
Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction;
Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning.
Fuzzy, Stochastic and Probabilistic computing, Multi objective optimization, Data Mining,
Neural computing, Pattern recognition, Expert Systems, Soft Computing Fundamental and Optimization,
Soft Computing for Big Data Era, GPU Computing for Machine Learning, Soft Computing Modeling for
Perception and Spiritual Intelligence, Soft Computing and Agents Technology, Soft Computing in Computer
Graphics, Soft Computing and Pattern Recognition, Soft Computing in Bio-mimetic Pattern Recognition,
Data mining for Social Network Data, Spatial Data Mining & Information Retrieval, Intelligent Software
Agent Systems and Architectures, Advanced Soft Computing and Multi-Objective Evolutionary Computation,
Perception-Based Intelligent Decision Systems, Spiritual-Based Intelligent Systems, Soft Computing in
Industry Applications and other issues related to the Advances of Soft Computing in various applications.