Tribological Properties of Polymer Composites Using Non Traditional Optimization Technique: a review

Specific wear rate of composite materials plays a significant role in industry. The processes to measure it are both time and cost consuming. It is essential to suggest a modeling method to predict and analyze the effectiveness of parameters of specific wear rate. Nowadays, computational methods such as Grey Relational Analysis (GRA), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as applicable tools from modeling point of view. The objective of using ANN, ANFIS is also to apply this tool for systematic parameter studies in the optimum design of composite materials for specific applications. In the present review, various principles of the neural network approach for predicting certain properties of polymer composite materials are discussed. The aim of this review is to promote more consideration of using GRA, ANN and ANFIS in the field of polymer composite property prediction and design

A Review on Search Based Face Annotation Using Weakly Labeled Facial Images

This paper investigates framework of face annotation by mining weakly labeledfacial images which are freely or easily available on World Wide Web (WWW). The challenging part of search based face annotation task ismanagement of most similar facial images and their weak labels. To tackle this problem, we propose an unsupervised label refinement (ULR) technic for refining the labels of web facial images using machine learning techniques. Auto face annotation can be beneficial to many real world applications like Facebook. The main aim of image annotation process is to automatically assign associate label to images, so image retrieving users are able to query images by labels and automatically detect human faces from a photo image and further name the faces with the corresponding human names