Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

This paper describes a new neural architecture for unsupervised learning of a classi cation of mixed transient signals. This method is based on neural techniques for blind separation of sources and subspace methods. The feed-forward neural network dynamically builds and refreshes an acoustic events classication by detecting novelties, creating and deleting classes. A self-organization process a...

Text categorization is the classication of documents with respect to a set of predened categories. In this paper, we propose a new probabilistic model for text categorization, that is based on a Single random Variable with Multiple Values (SVMV). Compared to previous probabilistic models, our model has the following advantages; 1) it considers within-document term frequencies, 2) considers term...

Medical images play an important role in medical diagnosis. However, acquiring a large number of datasets with annotations is still difficult task the field. For this reason, research field image-to-image translation combined computer-aided diagnosis, and data augmentation methods based on generative adversarial networks are applied to images. In paper, we try perform unimodal data. The designe...