Comparison of ANN and SVM to Identify Children Handwriting Difficulties

Abstract

This paper compares two classification methods to determine pupils who have difficulties in writing. Classification experiments are made with neural network and support vector machine method separately. The samples are divided into two groups of writers, below average printers (test group) and above average printers (control group) are applied. The aim of this paper is to demonstrate that neural network and support vector machine can be successfully used in classifying pupils with or without handwriting difficulties. Our results showed that support vector machine classifier yield slightly better percentage than neural network classifier and it has a much stable result.

Artificial Intelligence (AI) Based Tools for Predicting the Removal Efficiency of Heavy Metals by Adsorption

“…The use of a structural risk minimization (SRM) principle in SVM provides a good generalization performance. There are many past studies which show the superiority of SVM over ANN [12,13,14,15]. SVR now finds many applications for the prediction of many real world problems like time series prediction, predicting thermal-hydraulic performances in compact heat exchangers, predicting the auto-ignition temperatures of the organic compounds, atmospheric temperature prediction, predicting heat transfer coefficient and circulation rate in a thermosiphon reboiler etc.…”