Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction , and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP is working on a grayscale level so colored iris images should be transformed into a grayscale level. The proposed system gives a high recognition rate 99.87 % on different iris datasets compared with other methods.
Sentiment analysis is an important topic that has tracked attention since 2001. It basically is text classification based on analyzing opinions that expressed by writing (e.g., social media, blogs, discussion groups, etc). The widespread use of social networks has, also, led to a widespread availability of opinionated posts, making research in the area more viable and important. We need to make sentiment analysis to calculate the percentage of user acceptance or rejection according to their comments.Although Arabic is the native language of hundreds of millions of people in twenty countries across the Middle East and North Africa, the research in the area of Arabic sentiment analysis is progressing at a very slow pace compared to that being carried out in English[2].In this paper, we presnet our work in which we start by testing on English texts that wrere collected from Amazon (book, DVD, and electronics).Then, we applied the same process on Arabic dataset that we collect from YouTubeArabic pages. We applied more than one machine learning on algorithms both (Arabic. English) (Decision trees, Navie Bayes, functions, and support vector machines. We also createda Sentiword Lexicon based on the Corpus that we gathered. Then we evaluated each method and compared their accuracies.
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