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040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aM.Sc
099 _aCai01.20.01.M.Sc.2014.Al.P
100 0 _aAlzahraa Sabry Ahmed Mohammed
245 1 0 _aPattern recognition based methods for modeling and analysis of software rejuvenation /
_cAlzahraa Sabry Ahmed Mohammed ; Supervised Reda A. Elkhoribi , Eid Emary
246 1 5 _aطرق قائمة على ادراك الانماط لنمذجة وتحليل تجديد البرمجيات
260 _aCairo :
_bAlzahraa Sabry Ahmed Mohammed ,
_c2014
300 _a75 Leaves :
_bcharts , facsimiles ;
_c30cm
502 _aThesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Technology
520 _aThis thesis handles the software aging problem, which may be prevented using some rejuvenation techniques for the system before the crash occurs. Several machine learning based techniques are used to detect when to rejuvenate the system, in order to ensure its reliability and availability. For this purpose several machine learning based techniques are tried and tested on a set of collected bank data. Feature selection and reduction is first applied to the collected data in order to avoid the curse of dimensionality problem. A set of experiments with different classifiers are run on the processed data. These include back-propagation neural networks, K-nearest neighbors, Naive Bayes classifier, support vector machine, quadratic discriminant analysis and linear discriminant analysis. The highest obtained accuracy with respect to some online banking data is 87% using the linear discriminant analysis method. These experimental results are very promising, proving that machine learning techniques are reliable in the field of software rejuvenation
530 _aIssued also as CD
653 4 _aMethods-sof
653 4 _aPattern recognition
653 4 _aWare rejuvenation
700 0 _aEid Emary ,
_eSupervisor
700 0 _aReda A. Elkhoribi ,
_eSupervisor
856 _uhttp://172.23.153.220/th.pdf
905 _aAml
_eCataloger
905 _aNazla
_eRevisor
942 _2ddc
_cTH
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_d48003