Modeling helps to understand and predict the outcome of complex systems. Inductive modeling methodologies are beneficial for modeling the systems where the uncertainties involved in the system do not permit to obtain an accurate physical model. However inductive models, like artificial neural...

Successfully determining competitive optimal schedules for electricity generation intimately hinges on the forecasts of loads. The nonstationarity and high volatility of loads make their accurate prediction somewhat problematic. Presence of uncertainty in data also significantly degrades...

Neural networks (NNs) are one of the most widely used techniques in literature for forecasting electricity prices. However, nonzero forecast errors always occur, no matter what explanatory variables, NN types, or training methods are used in experiments. Persistent forecasting errors warrant the...

Defects are unavoidable in casting production owing to the complexity of the casting process. An automatic and reliable defect detection not just enhances the quality control process but positively improves productivity. However, casting defect detection is a challenging task due to diversity...