000			02727cam a2200313 a 4500
003			EG-GiCUC
008			200308s2020 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.02.M.Sc.2020.Mo.H
100	0		_aMohammed Megahed Hussein Megahed
245	1	0	_aHybrid approach to recognize learners behaviors using facial expressions and machine learning / _cMohammed Megahed Hussein Megahed ; Supervised Ammar Mohammed , Ahmed Hamza
246	1	5	_aأسلوب تكاملى للتعرف على سلوك المتعلم بإستخدام إسلوب تحديد تعبيرات الوجه وتعلم الآلة
260			_aCairo : _bMohammed Megahed Hussein Megahed , _c2020
300			_a115 Leaves : _bcharts , facsimiles ; _c30 Leaves
502			_aThesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science
520			_aComputer based learning environments are technology based instructional tools used for educational purpose. Usually, these environments take into consideration the learner's mental responses that are based on solving tests and exams questions to determine the next level of the learning process and manage the learning flow of the learner.They however suffer from engaging the emotional behaviors and facial expressions of the learner that reflect the learner's emotional states during the learning process.The later engagement could make the learning flow more adaptive as each learner could be redirected to a learning flow that matches his capabilities and academic performance.This thesis proposes a novel approach for modeling an adaptive computer based learning environment by considering the integration between the learner mental responses to exams questions and his emotional states during the exam sessions. In the proposed approach, a convolutional neural network is used to detect and analyze the learners' facial expressions, and outperforms other CNN models on the same training benchmark.The fuzzy system is used to determine the next learning level based on several response and interaction factors of the learner including emotional interaction factors and mental responses factors. The thesis also introduces corpora for evaluating the performance of the proposed approach
530			_aIssued also as CD
653		4	_aArtificial Neural Network (ANN)
653		4	_aDeep Neural Network (DNN)
653		4	_aNeural Network (NN)
700	0		_aAhmed Hamza , _eSupervisor
700	0		_aAmmar Mohammed , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c76883 _d76883