Artificial Intelligence in Predication of Court Case's time Span

A model to address, was constructed using a feed forward multilayer neural network perceptron having 9 input neurons to the network and one hidden layer with 20 neurons and finally having a single output neuron, which is the predicted time of the cases in months using MATLAB 7.0 neural network tool box. A selected model was trained with training and validation datasets[67% of the whole datasets], finally tested with the test set reserved for these purpose[33% of the datasets] and a total of more than 33,000 record set was used in building the model. Based on the performance function, the selected model shows a good performance range of Mean Square error [MSE] which is the difference between the target output and the network output was minimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate was between +0.2 normalized months. This is the good indication that the developed model could be a reliable predictive model for court cases time span especially for Criminal, civil and labor court cases with the assumption that the external factor that affect the court case time span prediction are constant and stable.