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18篇 您的检索式：作者名="Abdulhamit"

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		题名	作者	年代	出处	被引量
	1	Appliction of lifting based wavelet transform to characterize power quality events 显示文摘	Yilmaza Serdar Subasi Abdulhamit Bayrak Mehmet	2007	Energy Conversion and Management2007，，48：	1
	2	Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing显示文摘	Abdulhamit Subasi Ahmet Alkan Etem Koklukaya M. Kemal Kiymik	2005	Neural Networks2005，，7：	1
	3	Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines显示文摘	Abdulhamit S	2012	Computers in Biology and Medicine2012，42，8：	1
	4	Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing neural networks显示文摘	ABDULHAMIT S	2005	IEEE Transaction on Neural Networks2005，18，2：	1
	5	Classifica- tion of EMG signals using wavelet neural network显示文摘	ABDULHAMIT S MUSTAFA Y HASAN R O	2006	Journal of Neuroscience Methods2006，156，12：	1
	6	Robust multi bit and high quality audio watermarking using pseudo-random sequences显示文摘	Abdulhamit Subasi	2005	Computers and Electrical Engineering2005，9，31：	1
	7	Robust multi bit and high quality audio watermarking using pseudo-random sequences显示文摘	Abdulhamit Subasi	2005	Computers and Electrical Engineering2005，9，31：	1
	8	Classification of EEG signals using neural network and logistic regression显示文摘	Abdulhamit S Ergun E	2005	Computer Methods and Pcogranls in Biomedicine2005，78，：	1
	9	Application of lifting based wavelet transforms to characterize power quality events显示文摘	YILMAZ A Serdar SUBASI Abdulhamit BAYRAK Mehmet	2007	Energy Conversion and Management2007，，48：	1
	10	Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing 显示文摘	Abdulhamit Subasi Ahmet Alkan Etem Koklukaya	2005	Neural Network2005，，18：	1
	11	Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing显示文摘	Ahmet Alkan Etem Koklukaya	2005	Neural Network2005，，18：	1
	12	Application of lifting based wavelet transforms to characterize power quality events显示文摘	A Serdar Yilmaz Abdulhamit Subasi Mehmet Bayrak	2007	Energy Conversion and Management2007，48，1：	1
	13	Classification of EMG signals using wavelet neural network显示文摘	ABDULHAMIT S MUSTAFA Y HASAN R O	2006	Journal of Neuroscience Methods2006，156，12：	1
	14	Application of lifting based wavelet transforms to characterize power quality events显示文摘	SERDAR YILMAZ ABDULHAMIT SUBASI MEHMET BAYRAK	2007	Energy Conversion and Management2007，48，3：	1
	15	Robust multi bit and high quality audio watermarking using pseudo-random sequences显示文摘	Ergun Ercelebi Abdulhamit Subasi	2005	Computers & Electrical Engineering2005，31，08：	1
	16	Effects of expanded perlite aggregate and different curing conditions on the physical and mechanical properties of self-compacting concrete显示文摘	IBRAHIM TURKMEN ABDULHAMIT KANTARCL	2007	Building and Environment2007，42，6：	1
	17	Application of Lifting based Wavelet Transforms to eharaeterize Power Quality Events显示文摘	A Serdar Yilmaz Abdulhamit Subasi Mehmet Bayrak	2007	Energy Conversion and Management2007，，48：	1
	18	Predicted Oil Recovery Scaling-Law Using Stochastic Gradient Boosting Regression Model显示文摘In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.	Mohamed F.El-Amin Abdulhamit Subasi Mahmoud M.Selim Awad Mousa	2021	Computers, Materials & Continua2021，，8：	0

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