In the era of big data, we are facing with an immense volume and high velocity of data with
complex structures. Data can be produced by online and offline transactions, social …

A review of the optimization methods used in the design of type-2 fuzzy systems, which are
relatively novel models of imprecision, has been considered in this work. The fundamental …

Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

Type-2 fuzzy sets are used for modeling uncertainty and imprecision in a better way. These
type-2 fuzzy sets were originally presented by Zadeh in 1975 and are essentially" fuzzy …

Incremental training is commonly applied to training recurrent neural networks (RNNs) for
applications involving prognosis. As the number of prognostic time-step increases, the …

Combined cycle power plants, which combine gas and steam turbines, have negative
impacts on surrounding populations and structures. Control of NOx emissions is an …

An accurate exchange rate forecasting and its decision-making to buy or sell are critical
issues in the Forex market. Short-term currency rate forecasting is a challenging task due to …

Traditionally, the autoregressive moving average (ARMA) model has been one of the most
widely used linear models in time series prediction. Recent research activities in forecasting …

This monograph describes new methods for intelligent pattern recognition using soft
computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid …

This study aims to forecast oil prices using evolutionary techniques such as gene expression
programming (GEP) and artificial neural network (NN) models to predict oil prices over the …