Yang Bingru

A Novel Word Based Arabic Handwritten Recognition System Using SVM Classifier

Every language script has its structure, characteristic, and feature. Character based word recognition depends on the feature available to be extracted from character. Word based script recognition overcome the problem of character segmenting and can be applied for several languages (Arabic, Urdu, Farsi... est.). In this paper Arabic handwritten is classified as word based system. Firstly, words segmented and normalized in size to fit the DCT input. Then extract feature characteristic by computing the Euclidean distance between pairs of objects in n-by-m data matrix X. Based on the point’s operator of extrema, feature was extracted. Then apply one to one-Class Support Vector Machines (SVMs) as a discriminative framework in order to address feature classification. The approach was tested with several public databases and we get high efficiency rate recognition.

Rough Set Based Classification rules generation for SARS Patients

SARS is an acute infectious disease and can cause a large amount of death. Up until now we have not known it well. With the experimental results of micronutrients of 30 SARS patients and 30 non-SARS patients, using rough set theory we induce some classification rules. Attribute reduction results show that micronutrients Fe, Ca, K and Na are necessary and sufficient for classification, whereas micronutrients Zn, Cu and Mg are not necessary or are redundant. Additionally, we find that micronutrient Ca has a strong correlation to SARS. The classification results of 30 other examples show that the rough set classification method is available