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A key issue in many neural network applications is to determine which of the available input features should be used for modeling (Kumar, 2003). Mostly, researchers have used different ways for feature selection based on image processing methods in conjunction with neural network. An image acquisition setup that yields suitable images is crucial for a reliable and accurate judgment. , 1999). Some studied have used near sensor image processing (NSIP) technology as well. Most researchers had converted the original color image to gray level image to improve the computer processing speed and reducing the dimensions of information.

Objective Evaluation of the Trash and Color of Raw Cotton by Image Processing and Neural Network. Textile Research Journal, 2002, 72(9), 776-782. , Wang, X. and Beltran, R. An Artificial Neural Networkbased Hairiness Prediction Model for Worsted Wool Yarns. Textile Research Journal, 2009, 79(8), 714-720. I. S. Using Neural Network Theory to Predict the Properties of Melt Spun Fibers. Textile Research Journal, 2004, 74(9), 840-843. J. Prediction of Yarn Shrinkage using Neural Nets. Textile Research Journal, 2007, 77(5), 336-342.

Findings Limitations 21 The Prediction of Hadizad Textile 2009 79(17), predicting initial / 1599-1609. load-extension Initial Load-extension eh et al. Research Journal behavior (Young’s Behavior of Woven Fabrics Using modulus) in the Artificial Neural warp and weft Network directions of plain weave and plain weave derivative fabrics 22 Application of an Hadizad Textile 2010 80(10), predicting initial / Adaptive Neuro-fuzzy eh et al. Research 981-990. load–extension System for Prediction Journal behavior of plainwoven fabrics of Initial Load-based on an Extension Behavior of Plain-woven Fabrics adaptive neurofuzzy inference system (ANFIS) 23 Fabric Inspection best wavelet / Hu and Textile 2000 70(8), Based on Best Wavelet Tsai Research 662-670.

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