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Articles by Sherihan Abuelenin
Total Records ( 3 ) for Sherihan Abuelenin
  Raya Ismail , Sherihan Abuelenin and Ahmed Aboelfetouh
  Feature selection methods tend to identify the most relevant features for classification and can be categorized as either subset selection (wrapper) methods or ranking (filter) methods. The main purpose of this stusy is to prove that a feature selection preprocessing step could enhance classifiers performance by eliminating redundant features. The proposed method consists of three stages; the first refines sample space domain by resample filtering, the second minimizes feature space by applying subset evaluation algorithm and the third measures the goodness of the resulting set of features using different classifiers. Tow experiments carried out on the data sets from UCI repository. The proposed method is evaluated by measuring the accuracy, number of selected features, precision, recall, f-measure, ROC area, time to build model, error rate and relative absolute error. Tests are done on two main types of classifiers Na´ve Bayes and its variance NBTREE, NBNET and J48 with other tree classifiers Random Forest, BFTREE.
  Israa Abdulqader , Sherihan Abuelenin and Ahmed Aboelfetouh
  Breast cancer is one of the popular cancers in women and is considered one of the popular causes of death. Earlier detection and diagnosis may save lives and make efficient of life. In this study, a new method for breast cancer diagnosis is proposed. The proposed method consists of three stages: the first divides dataset to two clusters using kernel k-means clustering, the second minimizes features by applying feature selection algorithm on each cluster and the third collects resulting feature from each cluster together and measures the quality using different classifiers. The proposed approach is evaluated using datasets for breast cancer: Breast cancer wisconsin diagnostic dataset "WDBC" get from UCI machine learning repository. The performance of the proposed method is evaluated by measuring accuracy, sensitivity, specificity, mean squared error and time. The experiments are done with three classifiers Naive Bayes "NB", Multilayer Perceptron "MLP" and decision tree J48.
  Nehal Safwat , Sherihan Abuelenin and Ahmed Tolba
  Many application fields require detecting real-time composite events and reacting to them immediately considering their sensitive condition. This raises the need for efficient, situational aware Complex Event Processing (CEP) engines to handle massive amount of streamed data. To satisfy this need an optimization method to enhance the engine's efficiency and reliability is proposed. It adopts a combination of enhanced buffer allocation method with pattern matching process under the supervision of domain expert. An error detection and recovery to handle any failure case is proposed with the system to assure high reliability and accuracy in delivering and detecting the events. The proposed system is evaluated in terms of performance over time, throughput, error rate and pattern detection accuracy. A comparative evaluation with a state-of-art engine is done and the proposed system outperformed it under challenging workloads.
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