000			02013cam a2200349 a 4500
003			EG-GiCUC
005			20250223032203.0
008			190210s2018 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.20.03.M.Sc.2018.As.A
100	0		_aAsmaa Hamad Elsaied Mohamed
245	1	0	_aApplication of swarm intelligence optimization for enhancing detection of epileptic seizures in EEG signals / _cAsmaa Hamad Elsaied Mohamed ; Supervised Aly Aly Fahmy , Aboulella Hassanien , Essam Halim Houssein
246	1	5	_aتطبيق امثلية الذكاء السربي لتحسين نوبات اكتشاف الصرع في إشارات رسم المخ
260			_aCairo : _bAsmaa Hamad Elsaied Mohamed , _c2018
300			_a89 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science
520			_aThe thesis introduces a hybrid classification model using swarm optimization algorithms and support vector machines (SVMs) for automatic seizure detection in EEG. This proposed classification model consists of four main phases; namely,1) EEG pre-processing used to remove the noises from the EEG signals and decompose EEG signal into various sub-bands,2) feature extraction used to extract the EEG signal features from decomposed signal,3) Feature selection and classifier Parameters Optimization based swarm algorithms and 4) classification phase that is mainly used to analyze and classify the EEG signal into normal or abnormal
530			_aIssued also as CD
653		4	_aEEG
653		4	_aMachine Learning
653		4	_aSwarm Intelligence
700	0		_aAboulella Hassanien , _eSupervisor
700	0		_aAly Aly Fahmy , _eSupervisor
700	0		_aEssam Halim Houssein , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c70062 _d70062