id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_y6i7l3737zhnxb6nkrgyvnvbou	Mona Alshahrani	DANNP: an efficient artificial neural network pruning tool	2017	22	.pdf	application/pdf	9178	993	61	enhance the running time of the ANN pruning algorithms we implemented. set of features that remained in the pruned ANN with those obtained by different stateof-the-art feature selection (FS) methods. Keywords Artificial neural networks, Pruning, Parallelization, Feature selection, Classification Gan, Chen & Huang, 2016; Gardnera & Dorlinga, 1998; Hatzigeorgiou, 2002; HernándezSerna & Jiménez-Segura, 2014; Jayne, Iliadis & Mladenov, 2016; Kalkatawi et al., 2013; tool, which implements several parallelized variants of ANN pruning algorithms. We measured the performance of the implemented ANN pruning algorithms, on 15 OBS variants, the accuracy resulting from ANN pruning and the effects on input features effects of the different ANN pruning algorithms on the network, we evaluated the training Figure 3 Effects of different pruning algorithms on ANN performance on the training data. and pruned ANNs by the different algorithms on our datasets. Table 4 Selection of the input features through the ANN pruning and the effect on performance.	./cache/work_y6i7l3737zhnxb6nkrgyvnvbou.pdf	./txt/work_y6i7l3737zhnxb6nkrgyvnvbou.txt