Beshah T.,Addis Ababa Institute of Technology |
Ejigu D.,Addis Ababa Institute of Technology |
Abraham A.,VSB - Technical University of Ostrava |
Abraham A.,European Center for Excelence |
And 4 more authors.
Neural Network World | Year: 2012
Descriptive analysis of the magnitude and situation of road safety in general and road accidents in particular is important, but understanding of data quality, factors related with dangerous situations and various interesting patterns in data is of even greater importance. Under the umbrella of information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore data quality issues, analyze trends and predict the role of road users on possible injury risks. The research employed TreeNct, Classification and Adaptive Regression Trees (CART), Random Forest (RF) and hybrid ensemble approach. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is subject to several analyses. Empirical results illustrate that data quality is a major problem that needs architectural guideline and the prototype models could classify accidents with promising accuracy. In addition, an ensemble technique proves to be better in terms of predictive accuracy in the domain under study. © ICS AS CR 2012.
Prokop L.,VSB - Technical University of Ostrava |
Misak S.,VSB - Technical University of Ostrava |
Snasel V.,VSB - Technical University of Ostrava |
Snasel V.,European Center for Excelence |
And 4 more authors.
Neural Network World | Year: 2013
This article presents an application of evolutionary fuzzy rules to the modeling and prediction of power output of a real-world Photovoltaic Power Plant (PVPP). The method is compared to artificial neural networks and support vector regression that were also used to build predictors in order to analyse a time-series like data describing the production of the PVPP. The models of the PVPP are created using different supervised machine learning methods in order to forecast the short-term output of the power plant and compare the accuracy of the prediction. © CTU FTS 2013.