Expert Systems with Applications: An International Journal
Neural Networks to Predict Schooling Failure/Success
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Effects of feature selection on the identification of students with learning disabilities using ANN
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
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This paper presents a unique application of artificial neural networks used to predict the successful or unsuccessful completion of special education programming for students diagnosed with serious emotional disturbance (SED). In this study, as is common in medical applications, there is an insufficient amount of input data for training and testing the neural network. Bootstrapping and noisy replication of the input data are two techniques used to attempt to compensate for this small amount of available data. While the results would have benefited if more data were available, the results show some promise in being able to correctly predict the successful or unsuccessful completion of SED programming with artificial neural networks, particularly as a diagnostic test.