Application of Multilayer Feed Forward Artificial Neural Network Perceptron in Prediction of Court Case’s Time Span: The Case of Federal Supreme Courts’

Cherinet, Eskinder Mesfin (2009) Application of Multilayer Feed Forward Artificial Neural Network Perceptron in Prediction of Court Case’s Time Span: The Case of Federal Supreme Courts’. Masters thesis, Addis Ababa University.

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Abstract

This research examines and analyzes the use and application of neural networks as a predictive tool. The research was undergone with the assumption to give the Federal Supreme courts in advance estimation of the court case’s time span. The significance of the research could possibly benefit a plaintiff and defendants to know their case time length in prior as well the federal courts to perform court room monitoring, ensuring transparency and work efficiency. A model to address these needs 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. Finally when the network is trained with same court case types, the network has show high predictive capability for criminal cases with 95.65% of the data sets residual error minimized between + 0.005, 89.54% for civil cases and 91.55% for labor cases. This is the good indication that the developed predictive model can satisfactorily be an alternate choice for predicting court case time span especially court cases related to criminal cases.

Item Type:	Thesis (Masters)
Subjects:	K Law > K Law (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions:	Africana
Depositing User:	Selom Ghislain
Date Deposited:	02 Jul 2018 12:23
Last Modified:	02 Jul 2018 12:23
URI:	http://thesisbank.jhia.ac.ke/id/eprint/6316

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