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2 Journal of Artificial Intelligence and Soft Computing Research

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Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees

Tambouratzis T>, Souliou D., Chalikias M., Gregoriades A.

Journal of Artificial Intelligence and Soft Computing Research

2014

Vol. 4, No. 1

31--42

The development of universal methodologies for the accurate, efficient, and timely prediction of traffic accident location and severity constitutes a crucial endeavour. In this piece of research, the best combinations of salient accident-related parameters and accurate accident severity prediction models are determined for the 2005 accident dataset brought together by the Republic of Cyprus Police. The optimal methodology involves: (a) information mining in the form of feature selection of the accident parameters that maximise prediction accuracy (implemented via scatter search), followed by feature extraction (implemented via principal component analysis) and selection of the minimal number of components that contain the salient information of the original parameters, which combined bring about an overall 74.42% reduction in the dataset dimensionality; (b) accident severity prediction via probabilistic neural networks and random forests, both of which independently accomplish over 96% correct prediction and a balanced proportion of under- and over-estimations of accident severity. An explanation of the superiority of the optimal combinations of parameters and models is given, as is a comparison with existing accident classification/prediction approaches.

Pulse shape discrimination of neutrons and gamma rays using kohonen artificial neural networks

Tambouratzis T., Chernikova D., Pzsit I.

Journal of Artificial Intelligence and Soft Computing Research

2013

Vol. 3, No. 2

77--88

The potential of two Kohonen artificial neural networks (ANNs) - linear vector quantisation (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n’s) and gamma rays (’s). The effect that (a) the energy level, and (b) the relative size of the training and test sets, have on identification accuracy is also evaluated on the given PSD dataset. The two Kohonen ANNs demonstrate complementary discrimination ability on the training and test sets: while the LVQ is consistently more accurate on classifying the training set, the SOM exhibits higher n/ identification rates when classifying new patterns regardless of the proportion of training and test set patterns at the different energy levels; the average time for decision making equals ˜100 μs in the case of the LVQ and ˜450 μs in the case of the SOM.