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040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.08.M.Sc.2021.Am.E
100	0		_aAmr Saleh Fouad Hussein Nassar
245	1	0	_aEnhancing cell-phones{u2019} received signal strength prediction using deep learning / _cAmr Saleh Fouad Hussein Nassar ; Supervised Mohsen Rashwan
246	1	5	_aتحسين دقة التنبؤ بمستوى قوة الإشارة المستقبلية فى الهواتف الخلوية باستخدام التعلم العميق
260			_aCairo : _bAmr Saleh Fouad Hussein Nassar , _c2021
300			_a83 P. : _bcharts , facsimiles ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications
520			_aWhile mobile operators invest in providing the best quality of service (QOS) to its customers, a live visibility on the actual QOS at the customer end is often needed. Mobile operators rely on drive test to measure QOS at user level thus identify the service level. When this visibility is inaccurate or not live, detecting and acting on customer problems can take lengthy timeframes.The thesis proposes machine learning models using huge historical dataset collected from actual filed readings to predict the QOS received at the customer level indifferent locations. Five ML approaches are examined, and the results were compared to identify the ML model that can offer higher prediction accuracy for QOS.Then Clustering ML model was built to divide the coverage area into small areas such that probe devices can be used to collect field readings from specific locations to improve the predictive model
530			_aIssued also as CD
653		4	_aMachine Learning
653		4	_aReference Signal Strength
653		4	_aTelecom Optimization
700	0		_aMohsen Rashwan , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c82708 _d82708