Application Of Multilayer Feed Forward Artificial Neural Network Perception In Prediction Of Court Cases Time Span The Case Of Federal Supreme Courts

This research examines and analyzes the use and application of neural networks as a predictiverntool. The research was undergone with the assumption to give the Federal Supreme courts inrnadvance estimation of the court case's time span. The significance of the research could possiblyrnbenefit a plaintiff and defendants to know their case time length in prior as well the federal courtsrnto perform court room monitoring, ensuring transparency and work efficiency. A model to address these needs was constructed using a feed forward multiplayer neural networkrnperception having 9 input neurons to the network and one hidden layer with 20 neurons andrnfinally having a single output neuron, which is the predicted time of the cases in months usingrnMA TLAB 7.0 neural network tool box. A selected model was trained with training and validationrndatasets[67% of the whole datasets], finally tests with the test set reserved for thesernpurpose[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 MeanrnSquare error [MSE] which is the difference between the target output and the network output wasrnminimized to fit to the range offering a value of 0.0033 with 94.44% of the error rate wasrnbetween ±O.2 normalized months. This is the good indi cation that the developed model could berna reliable predictive model for court cases time span especially for criminal, civil and labor courtrncases with the assumption that the external factor that affect the court case time span predictionrnare constant and stable. Finally when the network is trained with same court case types, the network has show highrnpredictive capability for criminal cases with 95.65% of the data sets residual error minimizedrnbetween ±...O.005, 89.54% for civil cases and 91.55% for labor cases. This is the good indicationrnthat the developed predictive model can satisfactorily be an alternate choice for predicting courtrncase time span especially court cases related to criminal cases.

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