Faculty Of Computer Science Research Paper

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Browsing Faculty Of Computer Science Research Paper by Author "6th October City"

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    Fuzzy gaussian classifier for combining multiple learners
    (2010) Ali F.; El Gayar N.; El Ola S.; Faculty of Computer Science; October University for Modern Science and Arts; 6th October City; Egypt; Faculty of Computers and Information; Cairo University; Giza; Egypt; Center for Informatics Science; School of Communication and Information Technology; Nile University; Giza; Egypt
    In the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier (KNN), Fuzzy K-nearest neighbor classifier and Multi-layer Perceptron (MLP) and then compare the results with Fuzzy Integral, Decision Templates, Weighted Majority, Majority Na�ve Bayes, Maximum, Minimum, Average and Product combination methods. Results on two benchmark data sets show that the proposed fusion method outperforms a wide variety of existing classifier combination methods.
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    Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm
    (Springer, 2019) Mohamed A.W.; Hadi A.A.; Mohamed A.K.; Operations Research Department; Faculty of Graduate Studies for Statistical�Research; Cairo University; Giza; 12613; Egypt; Wireless Intelligent Networks Center (WINC); School of Engineering and Applied Sciences; Nile University; Giza; Egypt; College of Computing and Information Technology; King Abdulaziz University; P. O. Box 80200; Jeddah; 21589; Saudi Arabia; Department of Computer Science; Faculty of Computer Science; October University for Modern Sciences and Arts (MSA); 6th October City; Giza; 12451; Egypt
    This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. Besides, the GSK algorithm has been applied to solve the set of real world optimization problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. A comparison with 10 state-of-the-art and recent metaheuristic algorithms are executed. Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions. � 2019, Springer-Verlag GmbH Germany, part of Springer Nature.