No abstract

Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The data obtained should provide meaningful, correct, and accurate information. Therefore, all data must be separated into clusters correctly, and the right information from these clusters must be obtained. Having the correct clusters depends on the clustering algorithm that is used. There are many clustering algorithms. The density-based methods are very important among the groups of clustering methods, as they can find arbitrary shapes. An advanced model of the density-based spatial clustering of applications with noise (DBSCAN) algorithm, called fuzzy neighborhood DBSCAN Gaussian means (FN-DBSCAN-GM), is offered in this study. The main contribution of FN-DBSCAN-GM is to find the parameters automatically and to divide the data into clusters robustly. The effectiveness of FN-DBSCAN-GM has been demonstrated on overlapping datasets (six artificial and two real-life datasets). The performances of these datasets are compared with the percentage of correct classification and validity index. Our experiments showed that this new algorithm was a preferable and robust algorithm.

No abstract

Image processing has been employed in a variety of fields since the advent of image processing techniques. One of these fields is textiles. The existence of any defect in a fabric is one of the most important factors affecting the quality of the fabric. There are many types of fabric defects that can occur for various reasons. It is critical to figure out what caused the defect and fix it so that it does not occur again. Automation of fabric defect detection has recently attracted a great deal of interest in view of the development in artificial intelligence technology to be able to discover defects with a high degree of success and to limit the harm to the manufacturer. This study focuses on analyzing different feature extraction methods and different classifiers and discussing the advantages/disadvantages of the combinations and, unlike other studies, using feature fusion for feature extraction. Different cases have been created that handle fabric datasets from different angles and apply different methods of feature extraction (convolution neural network, minimum relevance and maximum redundancy) and classification (ensemble learning (EL), k-nearest neighbor, support vector machine (SVM)) for separating defected and un-defected patterned and un-patterned fabrics. ResNet18 is the convolution neural network-based model with the highest performance in feature extraction, while EL and the SVM allow us to achieve close and highly successful results in classification. When feature fusion is used, ResNet18 & GoogLeNet & SVM is the most successful combination compared to the others (94.66%).