H. Erdinc Kocer

GENETIC ALGORITHM BASED FEATURE SELECTION LEVEL FUSION USING FINGERPRINT AND IRIS BIOMETRICS

An accuracy level of unimodal biometric recognition system is not very high because of noisy data, limited degrees of freedom, spoof attacks etc. problems. A multimodal biometric system which uses two or more biometric traits of an individual can overcome such problems. We propose a multimodal biometric recognition system that fuses the fingerprint and iris features at the feature extraction level. A feed-forward artificial neural networks (ANNs) model is used for recognition of a person. There is a need to make the training time shorter, so the feature selection level should be performed. A genetic algorithms (GAs) approach is used for feature selection of a combined data. As an experiment, the database of 60 users, 10 fingerprint images and 10 iris images taken from each person, is used. The test results are presented in the last stage of this research.

Diagnosis of pathology on the vertebral column with backpropagation and Naive Bayes classifier

Minimizing human errors in diagnosis is one of the most important aims of Computer-Aided Diagnosis (CAD) systems. CAD software is the software helping radiologists , specialist doctor for detect abnormality on medical views by using advanced pattern diagnosis and view processing methods. In this study, a CAD system that can help detection and diagnosis of Pathology on the Vertebral Column is introduced. The vertebral database used in the application is obtained from UCI (Machine Learning Repository). Various artificial intelligence methods were applied for the classification of this database obtained. The results obtained are comparatively explained in detail in the related part.