Browsing by Author "Safwat S."

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Comparative study of clustering medical images
(Institute of Electrical and Electronics Engineers Inc., 2016) Fayez M.; Safwat S.; Hassanein E.; Faculty of Computer Sience; October Universityfor Modern Scienc and Arts; Giza; Egypt; Faculty of Computers and Information; Cairo University; Giza; Egypt
Due to the fast growing of the images data repositories, there is a big challenge to organize these repositories and make them easy to search and mine to get knowledge. Image clustering goal is to link each image in an image database with a class label, so similar images are grouped and have the same class label and which are different from other images in a database. Image clustering organize image repositories to groups or clusters and this is a basic step in many applications as content based image retrieval (CBIR), image classification, powerful search engines, browsing images and segmentation of images. In this paper, we proposed two new methods for clustering medical images. The two proposed methods are implemented, tested on medical images data set and the results obtained from the two proposed methods are compared. In the first proposed method, GLCM and Haralick's statistical measures are used for extracting texture features from the images and k-means clustering is applied to cluster the extracted feature vectors. In the second proposed method, 2D wavelet transform is used to extract features from the images, feature selection is applied and finally, K-means clustering algorithm is applied to cluster feature vectors. � 2016 IEEE.
A mapreduce fuzzy techniques of big data classification
(Institute of Electrical and Electronics Engineers Inc., 2016) El Bakry M.; Safwat S.; Hegazy O.; Faculty of Computer Science; October University for Modern Sciences and Arts; Giza; Egypt; Faculty of Computers and Information; Cairo University; Giza; Egypt
Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition a need for a means to meet the computational requirements to process such huge volume of data. The objective of this research is to implement a map reduce paradigm using fuzzy and crisp techniques, and to provide a comparative study between the results of the proposed systems and the methods reviewed in the literature. In this paper four proposed system is implemented using the map reduce paradigm to process on big data. First, in the mapper there are two techniques used; the fuzzy k-nearest neighbor method as a fuzzy technique and the support vector machine as non-fuzzy technique. Second, in the reducer there are three techniques used; the mode, the fuzzy soft labels and Gaussian fuzzy membership function. The first proposed system is using the fuzzy KNN in the mapper and the mode in the reducer, the second proposed system is using the SVM in the mapper and the mode in the reducer, the third proposed system is using the SVM in the mapper and the soft labels in the reducer, and the fourth proposed system is using the SVM in the mapper and fuzzy Gaussian membership function in the reducer. Results on different data sets show that the fuzzy proposed methods outperform a better performance than the crisp proposed method and the method reviewed in the literature. � 2016 IEEE.