A Robust Algorithm for Ear Recognition System Based on Self Organization Maps

This paper presents a robust algorithm for ear identification based on geometrical features of the ear and Kohnen Self Organization Maps (SOM). Using ears in identifying people has been interesting at least 100 years. The researches still discuss if the ears are unique or unique enough to be used as biometrics. Ear shape applications are not commonly used, yet, but the area is interesting especially in crime investigation. In this paper we present the basics of using ear as biometric for person identification and authentication. High resolution ear images are taken by high resolution digital camera. Six images have been taken for twenty three persons. Four geometrical distances were calculated for each image. These geometrical distances are used as an input to the unsupervised Kohonen self organization maps. The accuracy of identification were found to be equal to 98%, for the proposed system .We conclude that that the proposed model gives faster and more accurate identification of persons based on the ear biometrics and it works as promising tool for person identification of persons from the  mage of their ear for criminal investigation purposes

ANN-Based Prediction of Kidney Dysfunction Using Clinical Laboratory Data

This paper presents the prediction of Kidney dysfunction using probabilistic neural network (PNN). Six hundred and sixty (660) sets of analytical laboratory test have been collected from one of the private Clinical laboratories in Baghdad. For each subject, Serum urea and Serum creatinin levels have been analyzed and tested by using clinical laboratory measurements. The collected Urea and cretinine levels are then used as inputs to the Artificial Neural network model in which the training process is done by PNN which is a class of radial basis function (RBF) network is used as a classifier to predict whether Kidney is normal or it will have a dysfunction. The accuracy of Prediction, sensitivity and Specificity were found to be equal to 99%, 98% and 99% respectively for this proposed network .We conclude that the proposed model gives faster and more accurate prediction of Kidney dysfunction and it works as promising tool for predicting of routine kidney dysfunction from the clinical laboratory data