The traceability is of paramount importance and considered as a prerequisite for businesses for long-term functioning in today's global supply chain. The implementation of traceability can create visibility by the systematic recall of information related to all processes and logistics movement. The traceability coding tag consists of unique features for identification, which links the product with traceability information, plays an important part in the traceability system. In this paper, we describe an innovative technique of product component-based traceability which demonstrates that product's inherent features-extracted using deep learning-can be used as a traceability signature. This has been demonstrated on textile fabrics, where Faster region-based convolutional neural network (Faster R-CNN) has been introduced with transfer learning to provide a robust end-to-end solution for coded yarn recognition. The experimental results show that the deep learning-based algorithm is promising in coded yarn recognition, which indicates the feasibility for industrial application.
Fuzzy C-means (FCM) has been frequently used to image segmentation in order to separate objects. The most used segmentation attribute is grey level of pixels. Nevertheless, this method can not identify complex image objects because grey level can not take into account all visual information. This paper describes a modified FCM method for tissue classification using retrospective operation of partition tree with expert knowledge. This method is applied to 26 MRI (Magnetic Resonance Imaging) images of thigh for localizing four main anatomical tissues: muscle, adipose tissue, cortical bone, and spongy bone. A test dataset of 6500 representative points has been created by an expert. Using our method, we obtain a classification rate of 95.73% in the test dataset, which largely improved the classification results obtained from existing methods.
Compared to other retail industries, fashion retail industry faces many challenges to foresee future demand of its products. This is due to ever-changing choices of their consumers, who get influenced by rapidly changing market trends and it leads to the short life cycle of a fashion product. Due to the advent of e-commerce business models, fashion retailers have to put a multitude of virtual product images along with their feature information on their websites in order for their customers to know the fashion products and improve their purchasing experience. It is imperative for fashion retailers to predict future consumer preferences in advance; however, they lack advanced tools to achieve this goal. To overcome this problem, this research work combines the historical information of products with their image features using deep learning and predicts future sales. Apparel images are converted into feature vectors and then are merged with historical sales data. We applied backward propagation neural network model to predict the sales of a new product. It is found that the model performs quite well despite the small size of the dataset. This approach could be promising for forecasting the new arrivals of apparels in the market, and fashion retailers could improve their efficiency and growth.
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