For many uses, biometric systems have gained considerable attention. Iris identification was One of the most powerful sophisticated biometrical techniques for effective and confident authentication. The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with fixed- distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without collaboration among the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion, and reflection. This work aims to use the Gray-Level Co-occurrence Matrix (GLCM) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. GLCM is second-order Statistical-Based Methods for Texture Analysis. The GLCM- based extraction technology was applied after the preprocessing method to extract the pure iris region's characteristics. The Energy, Entropy, Correlation, Homogeneity, and Contrast collection of second-order statistical features are determined from the generated co-occurrence matrix, Stored as a vector for numerical features. This approach is used and evaluated on the CASIA v1and ITTD v1 databases as NIR iris image and UBIRIS v1 as a color image. The results showed a high accuracy rate (99.2 %) on CASIA v1, (99.4) on ITTD v1, and (87%) on UBIRIS v1 evaluated by comparing to the other methods.
The brain tumors are among the common deadly illness that requires early, reliable detection techniques, current identification, and imaging methods that depend on the decisions of neuro-specialists and radiologists who can make possible human error. This takes time to manually identify a brain tumor. This work aims to design an intelligent model capable of diagnosing and predicting the severity of magnetic resonance imaging (MRI) brain tumors to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, two artificial neural networks (ANNs) methods namely, Feed Forward Back Propagation neural network (FFBPNN) and support vector machine (SVM), used to expectations the level of brain tumors. The results show that the prediction result by the (FFBPN) network will be better than the other method in time record to reach an automatic classification with classification accuracy was 97% for 3-class which is considered excellent accuracy. The software simulation and results of this work have been implemented via MATLAB (R2012b).