Computer-aided ECG analysis is very important for early diagnosis of heart diseases. Automated ECG analysis integrated with experts' opinions may provide more accurate and reliable results for detection of arrhythmia. In this study, a novel genetic algorithm-neural network (GA-NN) approach is proposed as a classifier, and compared with other classification methods. The GA-NN approach was shown to perform better than alternative approaches (e.g. k-nn, SVM, naive Bayes, Bayesian networks) on the UCI Arrythmia and the novel TEPAS ECG datasets, where the GA resulted in a feature reduction of 95%. Based on the selected features, several rule extraction algorithms are applied to allow the interpretation of the classification results by the experts. In this application, the accuracy and interpretability of results are more important than processing speed. The results show that neural network based approaches benefit greatly from dimensionality reduction, and by employing GA, we can train the NN reliably.
A new observation network has been built to observe the surface manifestations of undermining at Gabriela locality. This locality lies in the Czech part of the Upper Silesian Coal Basin and the history of the hard coal underground exploitation is more than 150 years long here. Recently, the last coal mining panel was started to be exploited here. Its location and mining parameters are very suitable for the analysis of the actual and future surface changes caused by undermining. The fixed points of the observation network are surveyed by geodetic GNSS me thod. This method enables the evaluation of both vertical subsidence and horizontal displacements. Such complex evalua tion of processes on the surface of the undermined territory makes it possible to understand the progress of the subsidence depression and to capture the final phase of the surface undermining changes, i.e. the phase of the subsidence decline., Vlastimil Kajzar, Hana Doležalová, Kamil Souček and Lubomír Staš., and Obsahuje bibliografické odkazy