Classification of emergency phone conversations with artificial neural network

This paper presents a series of experiments on the classification of emergency phone conversation records using artificial neural networks (ANNs). Input data which were processed by ANNs were the features of callers and events taken from emergency phone calls. The authors analyzed four variants of classification: the groups of callers which have specified features, the groups of events which have specified features, selected callers, and selected events. Then the efficiency of classification by the ANN (artificial neural network) with various sets of features was compared. Results show that ANNs can properly classify precisely defined callers or events, from e.g. so called `black-list' of callers.