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This paper proposes an automated target recognition algorithm using Support Vector Machine (SVM) to extract landmark points for craniofacial features in cephalometry radiograph. The features are extracted by subjecting the radiograph to the Projected Principle Edge Distribution (PPED) algorithm. Edge flags are accumulated in every four columns and spatial distribution of edge flags are represented by a histogram. The resultants are the front end of support vector machine. Vectors, which possess land marks, are separated from all other vectors. The centroid points, automatically determined from PPED vectors, are the location of landmarks. The landmark points which are serving as a guide for construction and measurement of planes, are used to evaluate the dento-facial relationship, study of growth and development, and also for treatment planning.

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Protein sequence classijkation is modelled as a binaty classijkation problem where an unlabeled protein sequence is checked to see if it belongs to a known set of protein superfamilies or not. In this paper we used multilayer perceptrons with supervised learning algorithm to learn the binary class$cation. The training data consists of two sets -a positive set belonging to an identifed set of protein super,family and a negative set comprising sequences from other superfamilies. When applying neural networks the first problem to be addressed is feature extraction. In this paper we used the new feature extraction techniques proposed by Wang et al. [4]. Simulations reveal that the neural network is able to classifi with good precision for Myosin and Photochrome superfamilies in the data set that we have chosen as positive . Also the results for Globin superfamily are good, thus validating the methodology of feature extraction and the application of neural networks for protein sequence classijkation as suggested by Wang et al. But, for Actin and Ribonuclease superfamilies the network showed poor performance. One possible reason for this may be that the choice of sequences in the negative data set is not optimal. We conclude from this work that the classification performance depends upon a proper selection of sequences for positive and negative data sets.

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