At present, Van Herick's method is a standard technique used to screen a Narrow Anterior Chamber Angle (NACA) and Angle-Closure Glaucoma (ACG). It can identify a patient who suffers from NACA and ACG by considering the width of peripheral anterior chamber depth (PACD) and corneal thickness. However, the screening result of this method often varies among ophthalmologists. So, an automatic screening of NACA and ACG based on slit-lamp image analysis by using Support Vector Machine (SVM) is proposed. SVM can automatically generate the classification model, which is used to classify the result as an angle-closure likely or an angle-closure unlikely. It shows that it can improve the accuracy of the screening result. To develop the classification model, the width of PACD and corneal thickness from many positions are measured and selected to be features. A statistic analysis is also used in the PACD and corneal thickness estimation in order to reduce the error from reflection on the cornea. In this study, it is found that the generated models are evaluated by using 5-fold cross validation and give a better result than the result classified by Van Herick's method.
A design pattern provides a proven solution to a problem that commonly occurs in software design. It provides a flexible, reusable, and modular software design with objectoriented programming. The selection process of design patterns is however a difficult task, especially for novice designers. In our previous work, we constructed a pattern usage hierarchy for helping a designer to select Gang-of-Four patterns. The pattern usage hierarchy was constructed based on the intentions of a user, which can be divided into five categories. The experimental results motivated an improvement by incorporation of an algorithm for recommending the category to which a given problem belongs. We apply a text classification approach in order to guide the user to select an appropriate category. Famous classification methods, i.e., Naïve Bayes, J48, k-NN, and SVM, are used to classify given textual problems. The framework was evaluated with 26 case studies. The results of the experiment are reported.
This paper introduces a newly developed automatic classification system for wedge tightness inside the generator by applying support vector machine (SVM) classifier. The automatic classifying system for wedge tightness of the generator consists of 4 parts including data collection, preprocessing, feature extraction, and classification. Machine learning algorithm called SVM is used with the linear and radial basis function (RBF) classifier. Each input feature is extracted in different ways to evaluate the performance of classification. The evaluation is completed by using a 10-fold cross validation technique to provide high accuracy and a low number of False Negatives (FN). By applying the proposed system, the number of tightness and looseness inside wedge generator can be classified. Based on the classification results, the signals extracted in the frequency domain gives the best performance among the time domain and the frequency domain. This paper shows that the automatic classifying method has a high potential to identify the wedge tightness inside the generator.
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